10cct01_v2rbf_50m.tif: 50-Meter Resolution Grid of Swath Bathymetry Data Collected Offshore of Cat Island, Mississippi in March 2010
In March of 2010, the U.S. Geological Survey (USGS) conducted geophysical surveys east of Cat Island, Mississippi. The efforts were part of the USGS Gulf of Mexico Science Coordination partnership with the U. S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project by mapping the shallow geological stratigraphic framework of the Mississippi Barrier Island Complex. The data ... |
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1869 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
1869 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1869. In 2002, NOAA published digitized shorelines for T-sheet (T-1097), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted. |
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1922 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
1922 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1922. In 2002, NOAA published digitized shorelines for T-sheet (T-3920), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted. |
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1950 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
1950 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1950. In 2002, NOAA published digitized shorelines for T-sheet (T-9393), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted. |
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1970s Shorelines for the Main Island of Puerto Rico
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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1970s Shorelines for Vieques and Culebra, Puerto Rico
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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1983 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the National High Altitude Photography (NHAP) program. The NHAP was coordinated by the U.S. Geological Survey as an interagency project to acquire cloud-free aerial photographs at a specific altitude above mean terrain elevation. Two different camera systems were used to obtain simultaneous coverage of black-and-white (BW) and color infrared (CIR) aerial photographs over the conterminous United States. Black-and-white aerial photographs were obtained on 9-inch film from an ... |
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1998 Atlantic coast NASA/NOAA/USGS Spring ATM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Atlantic Coast ... |
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1998 Digitized Shoreline for Breton Island, Louisiana(Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center's Digital Orthophoto Quarter Quads (DOQQ) images collected on January 24, 1998. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) Version 4.0. |
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1998 East Coast NASA/NOAA/USGS Winter ATM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Atlantic Coast ... |
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1998 Fall Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Fall Gulf Coast ... |
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1998 MA, NY, MD, and VA USGS/NASA ATM2 Lidar-derived dune crest, toe and shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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1998 Southeast ATM Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Southeast USGS/NASA ... |
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1999 Atlantic Coast NASA/NOAA/USGS ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Floyd
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1999 Atlantic Coast ... |
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1999 Fall Texas USGS/NASA/NOAA ATM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1999 Fall Gulf Coast ... |
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2000 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2000 U.S. Army Corps of ... |
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2001 Gulf Coast USGS/NASA ATM Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2001 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 U.S. Army Corps of ... |
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2001 Vectorized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
A first-surface elevation map was produced cooperatively from remotely sensed, geographically referenced elevation measurements collected by the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) on September 07-09, 2001. Elevation measurements were collected over the area using the NASA Airborne Topographic Mapper (ATM), a scanning lidar system that measures high-resolution topography of the land surface. The ATM system is deployed on a Twin Otter or P-3 Orion aircraft ... |
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2002 NOAA/NASA/USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 Post-Hurricane Lili ... |
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2002 Post-Tropical Storm Fay University of Texas Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 University of Texas ... |
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2002 USGS Virgina and Maryland Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ... |
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2003 NOAA Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2003 NOAA Oahu lidar ... |
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2003 Pre- and Post-Hurricane Isabel USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2003 Pre- and Post ... |
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2004 Digitized Shoreline for Breton Island, Louisiana(Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center’s Digital Orthophoto Quarter Quads (DOQQ) images collected on January 20, 2004. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2004 Maine NOAA Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 NOAA Maine lidar ... |
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2004 Post-Hurricane Charley West Florida EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ... |
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2004 Post-Hurricane Frances USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ... |
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2004 Post-Hurricane Ivan Northern Gulf of Mexico EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 USGS Post-Ivan ... |
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2004 Post-Hurricane Jeanne USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ... |
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2004 Pre-Hurricane Ivan Eastern Gulf Coast United States Army Corps of Engineers (USACE) Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Pre-Ivan Eastern ... |
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2004 USACE Post-Ivan Florida Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 U.S. Army Corps of ... |
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2005-2006 Atlantic Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005-2006 Atlantic Coast ... |
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2005 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center’s Digital Orthophoto Quadrangle (DOQ) images collected on November 17, 2005. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2005 EAARL Fire Island Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Fire Island USGS ... |
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2005 East Coast (DE, MD, NJ, NY, NC, and VA) USACE NCMP Topobathy Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2005 Padre Island USGS EAARL Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Experimental ... |
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2005 Post-Hurricane Dennis Florida U.S. Army Corps of Engineers Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 USACE Post-Dennis ... |
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2005 Post-Hurricane Katrina EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Post-Hurricane ... |
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2005 USGS Post-Hurricane Rita Texas and Louisiana Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 USGS Post-Hurricane ... |
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2006 FEMA Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Federal Emergency ... |
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2007 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the National Agriculture Imagery Program (NAIP) digital ortho imagery collected on October 11, 2007. This dataset contains digitized shorelines created from the NAIP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) Version 4.0. |
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2007 Northeast Barrier Islands USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Northeast Barrier ... |
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2007 South Florida FDEM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Florida Division of ... |
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2007 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 U.S. Army Corps of ... |
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2008 Assateague Island USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Assateague Island ... |
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2008 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center high-resolution orthorectified images collected on October 01, 2008. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) Version 4.0. |
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2008 North Carolina and Virginia NOAA/NGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Atlantic Coast ... |
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2008 Post-Hurricane Gustav Northern Gulf of Mexico USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Post-Hurricane ... |
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2008 South Louisiana USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 South Louisiana ... |
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2008 USGS Post-Hurricane Ike Texas Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 USGS Post-Hurricane ... |
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2009 Cape Canaveral USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Cape Canaveral ... |
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2009 Florida USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Florida U.S. Army ... |
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2009 North Carolina USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 U.S. Army Corps of ... |
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2009 Post-NorIda USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Post-NorIda USGS ... |
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2009 Western Gulf of Mexico USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Western Gulf of ... |
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2010-2022 New Jersey and New York Beach Shoreline Change
This dataset defines shoreline change rates for each 10-meter (m)-wide profile calculated via endpoint rate and linear regression from Himmelstoss and others (2018). Shoreline change rates were calculated for two time periods: pre-Sandy (2010-2012) and post-Sandy (2012-2022). The profiles were derived from light detection and ranging (lidar) digital elevation models (DEMs). Refer to Doran and others (2017) for more information about the source lidar data. These data support the National Fish and Wildlife ... |
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2010-2022 New Jersey and New York Beach Volumes
This dataset defines the volume of sand along a 10-meter (m) wide profile between the seaward-most dune toe and the mean high water shoreline derived from light detection and ranging (lidar) digital elevation models (DEMs). Refer to Doran and others (2017) for more information about the source lidar data. These data support the National Fish and Wildlife Foundation (NFWF)-funded project entitled “Monitoring Hurricane Sandy Beach and Marsh Resilience in New York and New Jersey” (NFWF project ID 2300.16 ... |
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2010 Alabama and Florida USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Alabama and Florida ... |
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2010 Assateague Island National Seashore USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Assateague Island ... |
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2010 Delaware USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Delaware U.S. Army ... |
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2010 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the National Agriculture Imagery Program (NAIP) digital ortho imagery collected on May 10, 2010. This dataset contains digitized shorelines created from the NAIP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2010 Florida West Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Florida West Coast ... |
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2010 lidar-derived mean high water shoreline for the coast of South Carolina
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2010 Louisiana and Mississippi USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Louisiana and ... |
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2010 Maryland USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Maryland U.S. Army ... |
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2010 New Jersey USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 New Jersey U.S. ... |
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2010 New York USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 New York U.S. Army ... |
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2010 Northeast Atlantic USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Northeast Atlantic ... |
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2010 profile-derived mean high water shorelines of the North Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2010 profile-derived mean high water shorelines of the South Coast of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2010 Shorelines for Vieques, Culebra, and the Main Island of Puerto Rico
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2010 Southeast Atlantic USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Southeast Atlantic ... |
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2010 Virginia USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Virginia U.S. Army ... |
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2011 East Coast New York/New Jersey NOAA/NGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 East Coast New York ... |
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2011 Northern Gulf Coast USACE Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 Northern Gulf Coast ... |
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2011 profile-derived mean high water shorelines of the Outer Cape of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2011 profile-derived mean high water shorelines of the South Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2011 USGS New York Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 Atlantic Coast ... |
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2012-2014 contour-derived mean high water shorelines of the Massachusetts coast used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2012 Digitized Shoreline for Breton Island, Louisiana(Geographic, NAD83)
Shorelines were derived from a U.S. Geological Survey Earth Resources Observations and Science Center (EROS) high-resolution orthorectified image that was collected on October 20, 2012 over Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2012 Post-Hurricane Isaac USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2012 Post-Hurricane Sandy Fire Island, New York Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2012 Post-Hurricane Sandy Long Island, New York USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2012 Post-Hurricane Sandy New Jersey USGS EAARL-B Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2012 Post-Hurricane Sandy Northeast Atlantic Coast USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post ... |
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2012 Post-Sandy New York and New Jersey USACE NCMP Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Sandy New York ... |
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2012 Pre-Hurricane Sandy Fire Island National Seashore, USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2012 Pre-Sandy New York and New Jersey USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Pre Hurricane Sandy ... |
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2012 profile-derived mean high water shorelines of Martha's Vineyard, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2012 profile-derived mean high water shorelines of Nantucket, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013-14 Massachusetts Lidar-Derived Dune Crest Point Data
This data release of dune metrics for the Massachusetts coast is part of a 2018 update to the Massachusetts Shoreline Change Project. Because of continued coastal population growth and the increased threat of coastal erosion, the Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. Maps of historic shoreline locations from the mid-1800s to 1978 were produced from multiple data sources, and in 2001, a 1994 shoreline ... |
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2013-14 Massachusetts Lidar-Derived Dune Toe Point Data
This data release of dune metrics for the Massachusetts coast is part of a 2018 update to the Massachusetts Shoreline Change Project. Because of continued coastal population growth and the increased threat of coastal erosion, the Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. Maps of historic shoreline locations from the mid-1800s to 1978 were produced from multiple data sources, and in 2001, a 1994 shoreline ... |
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2013-2014 Northeast USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013-2014 Post� ... |
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2013-2014 profile-derived mean high water shorelines of the South Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 Dauphin Island USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 Dauphin Island ... |
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2013 Maine USACE/NAE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 Maine United States ... |
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2013 NOAA Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 National Oceanic ... |
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2013 profile-derived mean high water shorelines of Martha's Vineyard, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 profile-derived mean high water shorelines of Nantucket, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 profile-derived mean high water shorelines of the north shore of Martha's Vineyard, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 profile-derived mean high water shorelines of the north shore of Nantucket, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 profile-derived mean high water shorelines of the South Coast of MA used in shoreline change analysis.
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 USACE NAE Topobathy Lidar: Maine Point Cloud files with Orthometric Vertical Datum North American Vertical Datum of 1988 (NAVD88) using GEOID12B
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ... |
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2013 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 U.S. Army Corps of ... |
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2013 Vectorized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on July 12-14, 2013 over Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana and published in USGS Data Series 838. Photo Science, Inc., was contracted by the USGS to collect and process these data. Lidar data were acquired around portions of both the Alabama and Louisiana barrier islands; however, this dataset only contains shorelines created from data acquired from ... |
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2014–2015 Ocean Current and Pressure Time Series Data from the Upper Florida Keys: Crocker Reef, FL
Three Acoustic Doppler current profilers (ADCP), a current meter and a pressure logger were deployed at Crocker Reef, a senile (dead) barrier reef located in the northern portion of the Florida Reef Tract from December 12, 2014 to January 30, 2015 to quantify flow characteristics in various sub-regions. A RDI Workhorse Monitor current profiler was deployed on the reef flat and configured to measure three-dimensional flow velocities throughout the water column. Current measurements were taken at a rate of 1 ... |
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2014–2015 Ocean Current and Pressure Time Series Data from the Upper Florida Keys: Crocker Reef, FL
Three Acoustic Doppler current profilers (ADCP), a current meter and a pressure logger were deployed at Crocker Reef, a senile (dead) barrier reef located in the northern portion of the Florida Reef Tract from December 12, 2014 to January 30, 2015 to quantify flow characteristics in various sub-regions. A Nortek Aquadopp current meter was deployed on the reef flat and configured to measure three-dimensional flow velocities at the middle of the water column. Current measurements were taken at a rate of 1 ... |
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2014–2015 Ocean Current and Pressure Time Series Data from the Upper Florida Keys: Crocker Reef, FL
Three Acoustic Doppler current profilers (ADCP), a current meter and a pressure logger were deployed at Crocker Reef, a senile (dead) barrier reef located in the northern portion of the Florida Reef Tract from December 12, 2014 to January 30, 2015 to quantify flow characteristics in various sub-regions. Two SonTek Argonaut-XR current profilers were deployed on the reef flat and configured to measure three-dimensional flow velocities throughout the water column. Both current profilers sampled at a rate of ... |
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2014–2015 Ocean Current and Pressure Time Series Data from the Upper Florida Keys: Crocker Reef, FL
Three Acoustic Doppler current profilers (ADCP), a current meter and a pressure logger were deployed at Crocker Reef, a senile (dead) barrier reef located in the northern portion of the Florida Reef Tract from December 12, 2014 to January 30, 2015 to quantify flow characteristics in various sub-regions. A RBR pressure logger was deployed on the fore reef and configured to measure pressure at a rate of 2 hertz (Hz). The logger was positioned at a water depth of 14 meters, the deepest part of the fore reef) ... |
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2014 East Coast Maine USACE/NAE ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast Maine ... |
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2014 East Coast New Hampshire USACE/NAE ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast New ... |
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2014 East Coast Rhode Island NOAA/NGS ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast Rhode ... |
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2014 Mobile County, Alabama Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 Mobile County, ... |
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2014 Post-Hurricane Sandy SC to NY NOAA NGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2014 profile-derived mean high water shorelines of Cape Cod Bay, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2014 profile-derived mean high water shorelines of the North Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2014 profile-derived mean high water shorelines of the Outer Cape of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2014 profile-derived mean high water shorelines of the south shore of Cape Cod, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2014 USGS CMGP Post-Sandy Long Island Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 USGS CMGP Post ... |
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2014 Vectorized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on January 16-18, 2014 over Breton Island, Louisiana and released under USGS field activity number 14LGC01. Quantum Spatial was contracted by the USGS to collect and process these data. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital ... |
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2015-330-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2015-330-FA Offshore of Dauphin Island, Alabama, September 2015
From September 16 through 23, 2015, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Dauphin Island, Alabama. Geophysical data were collected as part of the Alabama Barrier Island Restoration Feasibility Study. This shapefile represents a point dataset of field activity number (FAN) 2015-330-FA chirp subbottom profile 1,000-shot-interval locations. |
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2015-330-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2015-330-FA Offshore of Dauphin Island, Alabama, September 2015
From September 16 through 23, 2015, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Dauphin Island, Alabama. Geophysical data were collected as part of the Alabama Barrier Island Restoration Feasibility Study. This shapefile represents a point dataset of field activity number (FAN) 2015-330-FA chirp subbottom profile start of trackline locations. |
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2015-330-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2015-330-FA Offshore of Dauphin Island, Alabama, September 2015
From September 16 through 23, 2015, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Dauphin Island, Alabama. Geophysical data were collected as part of the Alabama Barrier Island Restoration Feasibility Study. This shapefile represents a line dataset of field activity number (FAN) 2015-330-FA chirp tracklines. |
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2015 Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2015 Mississippi and Alabama USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2015 Mississippi and ... |
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2015 USACE Florida Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2015 U.S. Army Corps of ... |
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2016 Florida East Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ... |
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2016 Massachusetts NOAA Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2016 NOAA Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2016 USACE Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ... |
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2016 USACE Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2016 USACE Post-Hurricane Matthew Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2017-2018 lidar-derived mean high water shoreline for the coast of South Carolina
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 East Coast USACE/FEMA ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Irma
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2017 Atlantic Coast ... |
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2017 Florida West Coast NOAA Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches.Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2017 Georgia through New York USACE NCMP Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline ... |
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2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Fear to the South Carolina border (NCwest)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the coast of North Carolina from the Virginia border to Cape Hatteras (NCnorth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 USGS Lidar: Chenier Plain, LA Point Cloud files with Orthometric Vertical Datum North American Vertical Datum of 1988 (NAVD88) using GEOID12B
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ... |
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2018 Alabama and Florida USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 East Coast (NC) USACE NCMP Topobathy Lidar Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 East Coast (VA, NC, SC) USACE NCMP Post-Florence Topobathy Lidar-Derived Dune Crest, Toe, and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 Florida USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 mean high water shoreline of the coast of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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2018 Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2018 Mississippi and Alabama USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 Puerto Rico USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 South Texas USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2018 United States ... |
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2018 USGS Florida Panhandle Post-Michael Lidar-derived Dune Crest, Toe, and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2018 United States Army ... |
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2019-333-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2019-333-FA Offshore of the Rockaway Peninsula, New York, September–October 2019
From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a point dataset of field activity number (FAN) 2019-333-FA chirp subbottom profile 1,000-shot-interval locations. |
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2019-333-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2019-333-FA Offshore of the Rockaway Peninsula, New York, September–October 2019
From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a point dataset of field activity number (FAN) 2019-333-FA chirp subbottom profile start of trackline locations. |
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2019-333-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2019-333-FA Offshore of the Rockaway Peninsula, New York, September–October 2019
From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a line dataset of field activity number (FAN) 2019-333-FA chirp tracklines. |
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2019 North Carolina and Virginia Post-Dorian USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2019 North Carolina and Virginia USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (L=lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2020 New Jersey and New York USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2020 New Jersey USACE USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2021-322-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2021-322-FA Offshore of Pensacola Beach, Florida, June 2021
From June 2 through 9, 2021, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and determine Holocene stratigraphy near Santa Rosa Island, Florida (FL). This shapefile represents a point dataset of field activity number (FAN) 2021-322-FA chirp subbottom profile 1,000-shot-interval locations collected inshore and offshore of Pensacola Beach, FL. |
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2021-322-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2021-322-FA Offshore of Pensacola Beach, Florida, June 2021
From June 2 through 9, 2021, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and determine Holocene stratigraphy near Santa Rosa Island, Florida (FL). This shapefile represents a point dataset of field activity number (FAN) 2021-322-FA chirp subbottom profile start of trackline locations collected inshore and offshore of Pensacola Beach, FL. |
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2021-322-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2021-322-FA Offshore of Pensacola Beach, Florida, June 2021
From June 2 through 9, 2021, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and determine Holocene stratigraphy near Santa Rosa Island, Florida (FL). This shapefile represents a line dataset of field activity number (FAN) 2021-322-FA chirp tracklines collected inshore and offshore of Pensacola Beach, FL. |
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2021 New York State Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline ... |
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2022-309-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-309-FA Offshore of Seven Mile Island, New Jersey, April and May 2022
From April 29 through May 2, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Seven Mile Island, New Jersey. Geophysical data were collected as part of the Coastal Sediment Availability and Flux project. This shapefile represents a point dataset of field activity number (FAN) 2022-309-FA chirp subbottom profile 1,000-shot-interval locations. |
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2022-309-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-309-FA Offshore of Seven Mile Island, New Jersey, April and May 2022
From April 29 through May 2, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Seven Mile Island, New Jersey. Geophysical data were collected as part of the Coastal Sediment Availability and Flux project. This shapefile represents a point dataset of field activity number (FAN) 2022-309-FA chirp subbottom profile start of trackline locations. |
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2022-309-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-309-FA Offshore of Seven Mile Island, New Jersey, April and May 2022
From April 29 through May 2, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Seven Mile Island, New Jersey. Geophysical data were collected as part of the Coastal Sediment Availability and Flux project. This shapefile represents a line dataset of field activity number (FAN) 2022-309-FA chirp tracklines. |
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2022-312-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-312-FA Near Panama City, Florida, November 2022
From June 20-30, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport near Panama City, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-312-FA chirp subbottom profile 1,000-shot-interval locations. |
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2022-312-FA _sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-312-FA Near Panama City, Florida, June 2022
From June 20-30, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport near Panama City, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-312-FA chirp subbottom profile start of trackline locations. |
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2022-312-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-312-FA Near Panama City, Florida, November 2022
From June 20-30, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport near Panama City, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a line dataset of field activity number (FAN) 2022-312-FA chirp tracklines. |
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2022-328-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-328-FA Offshore of Breton Island, Louisiana, August 2022
On August 5, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Breton Island, Louisiana (LA). Geophysical data were collected as part of the Breton Island Post Construction Monitoring project. This shapefile represents a point dataset of field activity number (FAN) 2022-328-FA chirp subbottom profile 1,000-shot-interval locations. |
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2022-328-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-328-FA Offshore of Breton Island, Louisiana, August 2022
On August 5, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Breton Island, Louisiana (LA). Geophysical data were collected as part of the Breton Island Post Construction Monitoring project. This shapefile represents a point dataset of field activity number (FAN) 2022-328-FA chirp subbottom profile start of trackline locations. |
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2022-328-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-328-FA Offshore of Breton Island, Louisiana, August 2022
On August 5, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Breton Island, Louisiana (LA). Geophysical data were collected as part of the Breton Island Post Construction Monitoring project. This shapefile represents a line dataset of field activity number (FAN) 2022-328-FA chirp tracklines. |
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2022-334-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-334-FA Offshore of Boca Chica Key, Florida, November 2022
From November 8-13, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Boca Chica Key, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-334-FA chirp subbottom profile 1,000-shot-interval ... |
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2022-334-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-334-FA Offshore of Boca Chica Key, Florida, November 2022
From November 8-13, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Boca Chica Key, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-334-FA chirp subbottom profile start of trackline ... |
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2022-334-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-334-FA Offshore of Boca Chica Key, Florida, November 2022
From November 8-13, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Boca Chica Key, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a line dataset of field activity number (FAN) 2022-334-FA chirp tracklines. |
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2022 New Jersey and New York USACE USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline ... |
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2023-310-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2023-310-FA Offshore of Kailua, Hawaii, May 2023
From May 7-13, 2023, the U.S. Geological Survey (USGS) conducted a geologic assessment, including bathymetric mapping, near Kailua, Hawaii in support of efforts to construct an artificial coral reef offshore of Marine Corps Base Hawaii (MCBH). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2023-310-FA chirp subbottom ... |
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2023-310-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2023-310-FA Offshore of Kailua, Hawaii, May 2023
From May 7-13, 2022, the U.S. Geological Survey (USGS) conducted a geologic assessment, including bathymetric mapping, near Kailua, Hawaii in support of efforts to construct an artificial coral reef offshore of Marine Corps Base Hawaii (MCBH). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced ... |
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2023-310-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2023-310-FA Offshore of Kailua, Hawaii, May 2023
From May 7-13, 2022, the U.S. Geological Survey (USGS) conducted a geologic assessment, including bathymetric mapping, near Kailua, Hawaii in support of efforts to construct an artificial coral reef offshore of Marine Corps Base Hawaii (MCBH). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a line dataset of field activity number (FAN) 2023-310-FA chirp tracklines. |
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95th percentile of wave-current bottom shear stress in the Middle Atlantic Bight for May, 2010 - May, 2011 (MAB_95th_perc.SHP)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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Acidification and Increasing CO2 Flux Associated with Five, Springs Coast, Florida Springs (1991-2014)
Scientists from the South West Florida Management District (SWFWMD) acquired and analyzed over 20 years of seasonally-sampled hydrochemical data from five first-order-magnitude (springs that discharge 2.83 m3 s-1 or more) coastal springs located in west-central Florida. These data were subsequently obtained by the U.S. Geological Survey (USGS) for further analyses and interpretation. The spring study sites (Chassahowitzka, Homosassa, Kings Bay, Rainbow, and Weeki Wachee), which are fed by the Floridan ... |
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Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2019-10-11
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2019-11-26
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial_Shorelines_1940_2015.shp - Dauphin Island, Alabama Shoreline Data Derived from Aerial Imagery from 1940 to 2015
Aerial_WDL_Shorelines.zip features digitized historic shorelines for the Dauphin Island coastline from October 1940 to November 2015. This dataset contains 10 Wet Dry Line (WDL) shorelines separated into 58 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open-ocean, back-barrier, marsh shoreline. Imagery of Dauphin Island, Alabama was acquired from several sources including the United States Geological Survey ... |
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A GIS compilation of vector shorelines for the Virginia coastal region from the 1840s to 2010s
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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ALASKA1964_INUNDATION - Alaska 1964 Estimated Tsunami Inundation Line at Seaside, Oregon
This data set is a polyline shapefile representing the tsunami inundation line for the Alaska 1964 event based on observations and associated information obtained by Tom Horning (1997). The polyline was digitized from a line drawn by Tom Horning on an orthophoto taken in 1997. |
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ALASKA1964_OBS - Alaska 1964 Tsunami Observations at Seaside, Oregon
This data set is a point shapefile representing observations of inundation and water levels from the Alaska 1964 event obtained by Tom Horning (1997). The geospatial dataset were derived from a spreadsheet provided by Bruce Jaffe. |
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ALASKA1964_RUNUP - Alaska 1964 Tsunami Runup Heights at Seaside, Oregon (alaska1964_runup.shp)
This data set is a point shapefile representing tsunami inundation runup heights for the Alaska 1964 event based on observations and associated information obtained by Tom Horning (1997). The geospatial data was digitized from a points drawn by Tom Horning on an orthophoto taken in 1997. |
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AllScenarios_Bin1thru18_SSC: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Initial_and_Final_Bed_Elevations: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Sediment_Fluxes: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Spatial_Flow: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Spatial_Waves: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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Archive of Chirp Subbottom Profile Data Collected in 2015 from the Northern Chandeleur Islands, Louisiana
From September 14 to 28, 2015, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales ... |
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Archive of Chirp Subbottom Profile Data Collected in 2016 from the Northern Chandeleur Islands, Louisiana
From June 10 to 19, 2016, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months to ... |
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Archive of Chirp Subbottom Profile Data Collected in 2017 from the Louisiana Chenier Plain
June 2–10 and July 2, 2017, the U.S. Geological Survey (USGS) conducted geophysical surveys offshore of the Louisiana Chenier Plain to document the changing morphology of the coastal environment. Data were collected under the Barrier Island Coastal Monitoring (BICM) program, an ongoing collaboration between the State of Louisiana Coastal Protection and Restoration Authority (CPRA), the University of New Orleans (UNO) Pontchartrain Institute for Environmental Sciences (PIES), and the USGS. Project ... |
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Archive of Chirp Subbottom Profile Data Collected in 2017 From the Northern Chandeleur Islands, Louisiana
From August 7 to 16, 2017, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months ... |
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Archive of Chirp Subbottom Profile Data Collected in 2018 from the Northern Chandeleur Islands, Louisiana
From August 16 to 21, 2018, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months ... |
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Archive of Chirp Subbottom Profile Data Collected in 2019 from Cedar Island, Virginia
From August 9 to 14, 2019, researchers from the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate shoreface morphology and geology near Cedar Island, Virginia. The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. This publication serves as an archive of high-resolution ... |
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Archive of Chirp Subbottom Profile Data Collected in June 2018 From Fire Island, New York
Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal System Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. From ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2015 Offshore of Dauphin Island, Alabama
From September 16 through 23, 2015, researchers from the U.S. Geological Survey (USGS) conducted an offshore geophysical survey to map the shoreface and determine Holocene stratigraphy near Dauphin Island, Alabama (AL). The Alabama Barrier Island Restoration Feasibility Study project objective includes the investigation of nearshore geologic controls on surface morphology. This publication serves as an archive of high-resolution chirp subbottom trace data, survey trackline map, navigation files, geographic ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2019 from Rockaway Peninsula, New York
From September 27 through October 5, 2019, researchers from the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate shoreface morphology and geology near the Rockaway Peninsula, New York. The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. This publication serves as an ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2021 Near Pensacola Beach, Florida
From June 2 through 9, 2021, researchers from the U.S. Geological Survey (USGS) conducted an inshore and offshore geophysical survey to map the shoreface and determine Holocene stratigraphy near Pensacola Beach, Florida (FL). The Coastal Resource Evaluation for Management Applications (CREMA) project objective includes the investigation of nearshore geologic controls on surface morphology. This publication serves as an archive of high-resolution chirp subbottom trace data, survey trackline map, navigation ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2022 from Boca Chica Key, Florida
As part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey at the nearshore ledge offshore of Boca Chica Key, Florida (FL) November 8-13, 2022. The objective of the project was to collect bathymetric maps and conduct a geologic assessment of the nearshore ledge off Boca Chica Key in support ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2022 from Seven Mile Island, New Jersey
From April 29 through May 2, 2022, researchers from the U.S. Geological Survey (USGS) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterizing stratigraphy near Seven Mile Island, New Jersey (NJ). The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2022 Offshore of Breton Island, Louisiana
On August 5, 2022, researchers from the U.S. Geological Survey (USGS) conducted an offshore geophysical survey to map the shoreface and determine Holocene stratigraphy near Breton Island, Louisiana (LA). The Breton Island Post Construction Monitoring project objective includes the investigation of nearshore geologic controls on surface morphology in addition to mapping the seafloor to evaluate coastal change. This publication (Forde and others, 2023) serves as an archive of high-resolution chirp subbottom ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in June 2022 Near Panama City, Florida
As part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map back-barrier and lagoonal areas, as well as characterizing stratigraphy near Panama City, Florida (FL) in June 2022. The purpose of this study was to conduct a geologic assessment (including bathymetric mapping) of the environs ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in May 2023 from Oahu, Hawaii
As part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterizing stratigraphy near Oahu, Hawaii (HI) May 7-13, 2023. The purpose of this study was to conduct a geologic assessment (including bathymetric mapping) near Fort Hase Beach, ... |
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Archive of Chirp Sub-Bottom Profile, Imagery, and Navigational Data Collected in June and August 2023 from the Chandeleur Islands, Louisiana
As part of the 2022 Disaster Supplemental project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterize stratigraphy near the Chandeleur Islands, Louisiana (LA) in June and August 2023. The purpose of this study was to conduct a morphologic and geologic assessment of the impacts of the 2020 and 2021 hurricane seasons within part of the Breton National ... |
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Archive of Digitized Analog Boomer and Minisparker Seismic Reflection Data Collected from the Northern Gulf of Mexico: 1981, 1990 and 1991
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (https:/ ... |
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Archive of digitized analog boomer seismic reflection data collected along the Louisiana Shelf, 1982–1984
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (https:/ ... |
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Archive of digitized analog boomer seismic reflection data collected during U.S. Geological S cruises Erda 90-1_HC, Erda 90-1_PBP, and Erda 91-3 in Mississippi Sound, June 1990 and September 1991
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic-reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. A large portion of this data resides in a single repository with minimal metadata. As part of the ... |
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Archive of digitized analog boomer seismic reflection data collected during U.S. Geological Survey cruise Acadiana 87-2 in the northern Gulf of Mexico, June 1987
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic-reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. A large portion of this data resides in a single repository with minimal metadata. As part of the ... |
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Archive of Digitized Analog Boomer Seismic-Reflection Data Collected During U.S. Geological Survey Cruises Erda 92-2 and Erda 92-4 in Mississippi Sound, June and August 1992
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP) (https:/ ... |
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Archive of digitized analog boomer seismic reflection data collected during USGS Cruise Kit Jones 92-1 along the Florida Shelf, July 1992
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP; https:/ ... |
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Archive of digitized analog boomer seismic reflection data collected during USGS Cruise USFHC in Mississippi Sound and Bay St. Louis, September 1989
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP, https:/ ... |
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Archive of Digitized Analog Boomer Seismic Reflection Data Collected from the Northern Gulf of Mexico: 1982, 1985, 1986, 1989, 1991, and 1992
The U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program (CMHRP) has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program ... |
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Archive of Digitized Analog Boomer Seismic Reflection Data Collected from the Northern Gulf of Mexico: Intersea 1980
The U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program (CMHRP) has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters (m) long. As part of the National Geological and Geophysical Data Preservation ... |
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Archive of Ground Penetrating Radar and Differential Global Positioning System Data Collected in April 2016 from Fire Island, New York
Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Initial_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Initial_Elevations_N.txt)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_114_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_114_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_134_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_134_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_152_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_152_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_155_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_155_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_158_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_158_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_186_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_186_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_191_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_191_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_23_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_23_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_257_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_257_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_4_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_4_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_71_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_71_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_95_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_95_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Year_30_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Year_30_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Atlantic and Gulf coast sandy coastline topo-bathy profile and characteristic database
Seamless topographic-bathymetric (topo-bathy) profiles and their derived morphologic characteristics were developed for sandy coastlines along the Atlantic and Gulf coasts of the United States. As such, the rocky coasts of Maine, the coral reefs in southern Florida and the Keys, and the marsh coasts in the Mississippi Delta and the Florida Big Bend region and are not included in this dataset. Topographic light detection and ranging (lidar) data (dune crest, dune toe, and shorelines) from Doran and others ... |
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ATLANTIC - Coastal Vulnerability to Sea-Level Rise: A Preliminary Database for the U.S. Atlantic Coast
The goal of this project is to provide a preliminary overview, at a National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of ... |
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Attendee Survey Results from the April and May 2020 Gulf Islands National Seashore Workshop
In early 2020, scientists gathered to advance sediment budget modeling efforts by conducting a “Needs Assessment Workshop” for the Gulf Island National Seashore (GINS) to understand the coastal processes affecting island resiliency. The “Gulf Islands Sediment Budget Needs Assessment Workshop” was held, virtually, April 23–24 and May 27–28, 2020. The workshop series was organized by researchers from North Carolina State University in collaboration with National Park Service (NPS) and U.S. ... |
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Autonomous Flow-Thru (AFT) CO2 data of the West Florida Shelf: USGS Cruise 11BHM01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, ... |
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Autonomous Flow-Thru (AFT) CO2 data of the West Florida Shelf: USGS Cruise 11BHM02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25 - 30, ... |
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Autonomous Flow-Thru (AFT) pH data of the West Florida Shelf: USGS Cruise 11BHM01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, ... |
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Autonomous Flow-Thru (AFT) pH data of the West Florida Shelf: USGS Cruise 11BHM02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25 - 30, ... |
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Autonomous Flow-Thru (AFT) pH data of the West Florida Shelf: USGS Cruise 11BHM04
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Barrier island geomorphology and seabeach amaranth metrics at 50-m alongshore transects, and 5-m cross-shore points for 2008 — Assateague Island, MD and VA.
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as piping plover ... |
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Baseline_BackBarrier.shp - Baseline Along the Back-Barrier (North-Facing) Coast of Dauphin Island, Alabama, Generated to Calculate Shoreline Change Rates.
Analysis of shoreline change for Dauphin Island, Alabama was conducted using the U.S. Geological Survey (USGS) Digital Shoreline Analysis System (DSAS) v.4.3 for ArcMap (Thieler and others, 2009) and vector shorelines derived from air photos and lidar elevation surveys. DSAS-generated transects were cast at 100-meter intervals along a user defined shore-parallel baseline. The intersections of transects with the mean high water (MHW) shoreline positions are identified by intercept points. The rate of ... |
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Baseline coastal oblique aerial photographs collected at Breton Island and the Chandeleur Islands, Louisiana, January 22, 2011
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On January 22, 2011, the USGS conducted an oblique aerial photographic survey at Breton Island and the Chandeleur Islands, LA, aboard a Cessna 210 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and ... |
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Baseline coastal oblique aerial photographs collected at the Chandeleur Islands, Louisiana, and Dauphin Island, Alabama, July 24, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On July 24, 2010, the USGS conducted an oblique aerial photographic survey at the Chandeleur Islands, Louisiana, and Dauphin Island, Alabama, aboard a Beechcraft BE90 King Air aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from Breton Island to the Chandeleur Islands, Louisiana, September 3, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 3, 2010, the USGS conducted an oblique aerial photographic survey from Breton Island to the Chandeleur Islands, Louisiana, aboard a Cessna 210 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the ... |
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Baseline coastal oblique aerial photographs collected from Dauphin Island, Alabama, to Breton Island, Louisiana, July 26–27, 2007
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On July 26-27, 2007, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from Dauphin Island, Alabama, to Breton Island, Louisiana, September 26–27, 2006
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 26-27, 2006, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from Dog Island, Florida, to Breton Island, Louisiana, June 24–25, 2008
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 24–25, 2008, the USGS conducted an oblique aerial photographic survey from Dog Island, Florida, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental ... |
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Baseline coastal oblique aerial photographs collected from False Cape State Park, Virginia, to Myrtle Beach, South Carolina, May 6, 2008
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On May 6, 2008, the USGS conducted an oblique aerial photographic survey from False Cape State Park, Virginia, to Myrtle Beach, South Carolina, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission (08CH01) was conducted to collect data ... |
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Baseline coastal oblique aerial photographs collected from Fenwick Island State Park, Delaware, to Corolla, North Carolina, March 27, 1998
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On March 27, 1998, the USGS conducted an oblique aerial photographic survey from Fenwick Island State Park, Delaware, to Corolla, North Carolina, aboard a U.S. Coast Guard HH60 Helicopter at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline Coastal oblique aerial photographs collected from Horseshoe Beach, Florida, to East Cape, Florida, May 19-20, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On May 19-20, 2010, the USGS conducted an oblique aerial photographic survey from Horseshoe Beach, Florida, to East Cape, Florida, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes ... |
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Baseline coastal oblique aerial photographs collected from Navarre Beach, Florida, to Breton Island, Louisiana, September 7, 2016
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 7, 2016, the USGS conducted an oblique aerial photographic survey from Navarre Beach, Florida, to Breton Island, Louisiana, aboard a Maule MT57 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the ... |
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Baseline coastal oblique aerial photographs collected from Navarre, Florida, to the Chandeleur Islands, Louisiana, and from Grand Point, Alabama, to St. Joseph Point, Mississippi, June 6, 2006
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 6, 2006, the USGS conducted an oblique aerial photographic survey from Navarre, Florida, to the Chandeleur Islands, Louisiana, and from Grand Point, Alabama, to St. Joseph Point, Mississippi, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore ... |
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Baseline coastal oblique aerial photographs collected from Ponte Vedra, Florida, to the South Carolina/North Carolina border, August 24, 2011
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On August 24, 2011, the USGS conducted an oblique aerial photographic survey from Ponte Vedra, Florida, to the South Carolina/North Carolina border, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from Tampa Bay to the Marquesas Keys, Florida, June 22–23, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 22–23, 2010, the USGS conducted an oblique aerial photographic survey from Tampa Bay to the Marquesas Keys, Florida, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in ... |
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Baseline coastal oblique aerial photographs collected from the Harney River, Everglades National Park, Florida to Anclote Key, Florida, November 14, 2006
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On November 14, 2006, the USGS conducted an oblique aerial photographic survey from the Harney River, Everglades National Park, Florida to Anclote Key, Florida, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect ... |
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Baseline coastal oblique aerial photographs collected U.S. Army Corps of Engineers Field Research Facility, Duck, North Carolina, June 9, 2017
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 09, 2017, the USGS conducted an oblique aerial photographic survey of the U.S. Army Corps of Engineers Field Research Facility (USACE FRF), located in Duck, North Carolina, aboard a Cessna 182 aircraft at an altitude of approximately 1000 feet (ft). This mission was conducted to collect data for USACE FRF ... |
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Baseline for Buzzards Bay coastal region generated to calculate shoreline change rates from Nobska Point in Woods Hole to Westport at the Massachusetts-Rhode Island border (BuzzardsBay_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline for Elizabeth Islands coastal region generated to calculate shoreline change rates from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline for the backshore of Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the Cape Cod Bay coastal region in Massachusetts, generated to calculate shoreline change rates (without the proxy-datum bias) using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the Cape Cod Bay coastal region in Massachusetts, generated to calculate shoreline change rates (with the proxy-datum bias) using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the coastal region south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the coast of Puerto Rico's main island generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States' coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Baseline for the coast south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the east facing coast of Cape Cod, Massachusetts, from Monomoy to Provincetown, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the Florida east coast (FLec) coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the Florida panhandle (FLph) coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the Florida west coast (FLwc) coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the Georgia coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the islands of Vieques and Culebra, Puerto Rico, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Baseline for the North Carolina coastal region from Cape Fear to the South Carolina border (NCwest)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the North Carolina coastal region from Cape Hatteras to Cape Lookout (NCcentral)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the North Carolina coastal region from Cape Lookout to Cape Fear (NCsouth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the North Carolina coastal region from the Virginia border to Cape Hatteras (NCnorth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the northern coast of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the northern coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the South Cape Cod coastal region generated to calculate shoreline change rates from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline for the South Carolina coastal region, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the southern coast Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the southern coast of Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the southern coast of Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baseline for the southern coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the Virginia coastal region, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline_OpenOcean.shp - Baseline Along the Open-Ocean (South-Facing) Coast of Dauphin Island, Alabama, Generated to Calculate Shoreline Change Rates.
Analysis of shoreline change for Dauphin Island, Alabama was conducted using the U.S. Geological Survey (USGS) Digital Shoreline Analysis System (DSAS) v.4.3 for ArcMap (Thieler and others, 2009) and vector shorelines derived from air photos and lidar elevation surveys. DSAS-generated transects were cast at 100-meter intervals along a user defined shore-parallel baseline. The intersections of transects with the mean high water (MHW) shoreline positions are identified by intercept points. The rate of ... |
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Baselines for the coast of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baselines for the coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Baselines for the Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Bathymetric change analyses of the Sacramento River near Rio Vista, California, and the junction of Cache and Steamboat sloughs, from 1992 to 2004
Bathymetric change grids covering the periods of time from 1992 to 1998 and from 1994 to 2004 are presented. The grids cover a portion of the Sacramento River near Rio Vista, California, extending partially upstream on Cache and Steamboat sloughs by the Ryer Island Ferry, as well as continuing up the Sacramento River towards Isleton. Positive grid values indicate accretion, or a shallowing of the surface bathymetric surface, and negative grid values indicate erosion, or a deepening of the bathymetric ... |
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Bathymetric change analyses of the southernmost portion of the Mokelumne River, California, from 1934 to 2018
Bathymetric change grids covering the periods of time from 1934 to 2011, from 2011 to 2018, and from 1934 to 2018 are presented. The grids cover a portion of the Mokelumne River, California, starting at its terminus at the San Joaquin River and moving upriver to the confluences of the north and south branches of the Mokelumne. Positive grid values indicate accretion, or a shallowing of the surface bathymetric surface, and negative grid values indicate erosion, or a deepening of the bathymetric surface. ... |
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Bathymetric change map of the nearshore around Ship, Horn, and Petit Bois islands, Mississippi: 1916-1920 to 2008-2009
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Data sets include 1916 through 1920 soundings collected by the United States Coast and ... |
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Bathymetric change map of the nearshore around Ship, Horn, and Petit Bois islands, Mississippi: 1916-1920 to 2016
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and ... |
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Bathymetric change map of the nearshore around Ship, Horn, and Petit Bois islands, Mississippi: 2008-2009 to 2016
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Data sets include 1916 through 1920 soundings collected by the United States Coast and ... |
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Bathymetric change of Central San Francisco Bay, California: 1971 to 2020
This 25-m-resolution surface presents bathymetric change of Central San Francisco Bay, California (hereafter referred to as Central Bay). This surface compares a 1-m-resolution digital elevation model (DEM) of the central portion of San Francisco Bay (Fregoso and others, 2021), comprised of bathymetry data in the Central Bay region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 25-m-resolution DEM of Central Bay comprised of historic surveys from ... |
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Bathymetric change of San Pablo Bay, California: 1983 to 2015
This 25-m-resolution surface presents bathymetric change of San Pablo Bay, California, from 1983 to 2015. This surface compares a 1-m-resolution digital elevation model (DEM) of the northern portion of San Francisco Bay (Fregoso and others, 2020), comprised of bathymetry data in the San Pablo Bay region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 25-m-resolution bathymetric DEM of San Pablo Bay comprised of historic surveys from 1983 to 1986 ... |
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Bathymetric change of South San Francisco Bay, California: 1979 to 2020
This 50-m-resolution surface presents bathymetric change of South San Francisco Bay, California (hereafter referred to as South Bay). This surface compares a 1-m-resolution digital elevation model (DEM) of the southern portion of San Francisco Bay (Fregoso and others, 2021), comprised of bathymetry data in the South Bay region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 50-m-resolution DEM of South Bay comprised of historic surveys from 1979 to ... |
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Bathymetric change of Suisun Bay, California: 1988 to 2016
This 25-m-resolution surface presents bathymetric change of Suisun Bay, California, from 1988 to 2016. This surface compares a 1-m-resolution digital elevation model (DEM) of the northern portion of San Francisco Bay (Fregoso and others, 2020), comprised of bathymetry data in the Suisun region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 25-m-resolution bathymetric DEM of Suisun Bay comprised of historic surveys from 1988 to 1990 (referred to as ... |
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Bathymetric Grid for a Wave Exposure Model of Grand Bay, Mississippi
Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change they can also influence shore-proximal vertical accretion through sediment delivery. For these reasons, estimates of wave energy can provide a ... |
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Beach foreshore slope for the West Coast of the United States
This data release contains foreshore slopes for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each ... |
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Beach profile data collected in 2010 and 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Beach elevation profiles were measured along 29 shore-normal transects on and around Arey and Barter Islands, Alaska in August 2010 and July 2011. Profile data are available in a single comma-delimited file and a zip file including multiple .jpg images that show a visual representation of the individual profiles. |
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Beach Topography—Fire Island, New York, Pre-Hurricane Sandy, January 2012: Ground Based Lidar (1-Meter Digital Elevation Model)
The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) and the U.S. Army Corps of Engineers Field Research Facility (USACE-FRF) of Duck, North Carolina collaborated to gather alongshore ground-based lidar beach topography at Fire Island, New York. This high-resolution, elevation dataset was collected on January 30, 2012, and was funded by SPCMSC. The USGS data release containing the aforementioned dataset includes the resulting, processed elevation point data (XYZ) and ... |
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Beach Topography—Fire Island, New York, Pre-Hurricane Sandy, January 2012: Ground Based Lidar (ASCII XYZ Point Data)
The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) and the U.S. Army Corps of Engineers Field Research Facility (USACE-FRF) of Duck, North Carolina collaborated to gather alongshore ground-based lidar beach topography at Fire Island, New York. This high-resolution, elevation dataset was collected on January 30, 2012, and was funded by SPCMSC. The USGS data release containing the aforementioned dataset includes the resulting, processed elevation point data (XYZ) and an ... |
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BEWARE database: A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts
A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the central coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the northern coast of North Carolina from the Virginia border to Cape Hatteras (NCnorth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the western coast of North Carolina from Cape Fear to the South Carolina border (NCwest)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias Feature for the Florida east coast (FLec) coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Bias Feature for the Florida panhandle (FLph) coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Bias Feature for the Florida west coast (FLwc) coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Bias Feature for the Georgia coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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BocaChica_2022_MBES: High-resolution Geophysical and Imagery Data Collected in November 2022 Offshore of Boca Chica Key, FL
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Boca Chica Key, the Florida Keys, from November 8-13, 2022. This dataset, BocaChica_2022_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid, and the dataset BocaChica_2022_MBES_Backscatter.zip ... |
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Cape Canaveral, Florida, backscatter data collected in 2016 by Coastal Carolina University: Processed GeoTIFF Image
A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data. |
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Cape Canaveral, Florida, multibeam bathymetry collected in 2016 by Coastal Carolina University: Processed elevation point data (XYZ)
A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data. |
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Cape Canaveral, Florida, multibeam bathymetry collected in 2016 by Coastal Carolina University: Processed GeoTIFF Image
A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data. |
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Cape Canaveral, Florida, seismic chirp collected in 2016 by Coastal Carolina University
A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data. |
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Cape Canaveral, Florida side scan sonar data collected in 2016 by Coastal Carolina University: Processed GeoTIFF Image
A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data. |
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Cape Canaveral tracklines of geophysical data collected in 2016 by Coastal Carolina University
A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data. |
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CatIsland_2010_Bathy_NAVD88_grid.tif
In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic ... |
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CatIsland_2010_Bathy_Swath_tracklines
In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic ... |
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CatIsland 2010 single-beam bathymetry tracklines
In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic ... |
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CENCAL1853_1910 - Vectorized Shoreline of Central California Derived from 1853-1910 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL1929_1942 - Vectorized Shoreline of Central Califonia Derived from 1929-1942 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL1945_1976 - Vectorized Shoreline of Central California Derived from 1945-1976 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL_1998_2002 - Vectorized Shoreline of Central California Derived from 1998-2002 Lidar Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL_BASELINE - Offshore Baseline for Central California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_BIASVALUES - Central California Shoreline Bias Values
The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS ... |
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CENCAL_INTERSECTS_LT - Long-Term Transect-Shoreline Intersection Points for Central California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_INTERSECTS_ST - Short-Term Transect-Shoreline Intersection Points for Central California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_TRANSECTS_LT - Long-Term Shoreline Change Rates for Central California Generated at a 50 m Transect Spacing, 1853-2002
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_TRANSECTS_ST - Short-Term Shoreline Change Rates for Central California Generated at a 50m Transect Spacing, 1971-1998
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CentralBeaufort_shorelines.shp - Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Central California CoSMoS v3.1 projections of coastal cliff retreat due to 21st century sea-level rise
This dataset contains spatial projections of coastal cliff retreat (and associated uncertainty) for future scenarios of sea-level rise (SLR) in Central California. Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical models and field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS). Read metadata and references ... |
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Central California CoSMoS v3.1 projections of shoreline change due to 21st century sea-level rise
This dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model forced with global-to-local nested wave models and assimilated with lidar-derived shoreline vectors. Read metadata carefully. Details: Projections of shoreline position in the Central Coast of California are made for scenarios of 25, 50, 75, 92, 100 ... |
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Change in salinity exposure of salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey during Hurricane Sandy
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Change in salinity in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey during Hurricane Sandy
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Change in suspended sediment concentration over the salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey during Hurricane Sandy
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Characterization of seafloor photographs near the mouth of the Elwha River during the first two years of dam removal (2011-2013)
We characterized seafloor sediment conditions near the mouth of the Elwha River from underwater photographs taken every four hours from September 2011 to December 2013. A digital camera was affixed to a tripod that was deployed in approximately 10 meters of water. Each photograph was qualitatively characterized as one of six categories: (1) base, or no sediment; (2) low sediment; (3) medium sediment; (4) high sediment; (5) turbid; or (6) kelp. For base conditions, no sediment was present on the seafloor. ... |
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Climatological wave height, wave period and wave power along coastal areas of the East Coast of the United States and Gulf of Mexico
This U.S. Geological Survey data release provides data on spatial variations in climatological wave parameters (significant wave height, peak wave period, and wave power) for coastal areas along the United States East Coast and Gulf of Mexico. Significant wave height is the average wave height, from crest to trough, of the highest one-third of the waves in a specific time period. Peak wave period is the wave period associated with the most energetic waves in the wave spectrum in a specific time period. Wave ... |
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Coastal bathymetry data collected between 2008 and 2009 offshore of the Mississippi and Alabama barrier islands: Processed elevation point data
During the summers of 2008 and 2009 the United States Geological Survey (USGS) conducted bathymetric surveys from West Ship Island, Mississippi, to Dauphin Island, Alabama, as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately two kilometers and included the adjacent passes. These findings were originally published in Dewitt and others (2012). This USGS data release includes updated elevation point ... |
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Coastal Bathymetry Data Collected in June 2018 from Fire Island, New York: Wilderness Breach and Shoreface
Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida, conducted a bathymetric survey of Fire Island, New York, June 2?17, 2018. The U.S. Geological Survey is involved in a post-Hurricane Sandy effort to map and monitor the morphologic evolution of the wilderness breach and the adjacent shoreface environment. During this study, bathymetry data were collected aboard two personal watercraft (PWC) outfitted with single-beam echosounders, as well ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Hazard Impact Type
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazards
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Multibeam Bathymetry and Backscatter Data Collected in June 2021 from Rockaway Peninsula, New York
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore, seaward side of Rockaway Peninsula, New York (NY), June 18-25, 2021. This dataset, Rockaway_2021_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid and the dataset Rockaway_2021_MBES ... |
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Coastal Multibeam Bathymetry and Backscatter Data Collected in May 2021 From Seven Mile Island, New Jersey
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore extent of Seven Mile Island, New Jersey, from May 19-23, 2021. The download file, 7Mile_2021_MBES_xyz.zip, includes processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. The download file, 7Mile_2021_MBES ... |
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Coastal Multibeam Bathymetry and Backscatter Data Collected in May 2023 from Rockaway Peninsula, New York
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore, seaward side of Rockaway Peninsula, New York (NY), from May 6-16, 2023. This dataset, Rockaway_2023_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid and the dataset Rockaway_2023_MBES ... |
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Coastal Multibeam Bathymetry and Backscatter Data Collected in May 2023 From Seven Mile Island, New Jersey
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore extent of Seven Mile Island, New Jersey (NJ), from May 18-27, 2023. The download file, 7Mile_2023_MBES_xyz.zip, includes processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. The download file, 7Mile_2023_MBES ... |
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Coastal Multibeam Bathymetry and Backscatter Data Collected in October 2019 from Rockaway Peninsula, New York: Leg 1
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore, seaward side of Rockaway Peninsula, New York (NY), from October 4-6, 2019. This dataset, Rockaway_2019_MBES_Leg1_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid from the first leg of the ... |
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Coastal Multibeam Bathymetry and Backscatter Data Collected in October 2019 from Rockaway Peninsula, New York: Leg 2
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore, seaward side of Rockaway Peninsula, New York (NY), from October 24-29, 2019. This dataset, Rockaway_2019_MBES_Leg2_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid from the second leg of ... |
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Coastal Multibeam Bathymetry Data Collected in 2018 Offshore of Seven Mile Island, New Jersey
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of Seven Mile Island, New Jersey, September 6-8, 2018 and September 21-23, 2018. This dataset, presented as Seven_Mile_Island_2018_MBES_WGS84_UTM18N_xyz.zip and Seven_Mile_Island_2018_MBES_NAD83_NAVD88_GEOID12B_xyz.zip, includes the processed elevation point data ... |
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Coastal Multibeam Bathymetry Data Collected in 2019 off of Santa Rosa Island, Florida
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of Santa Rosa Island, Florida (FL), June 15-29, 2019. This dataset, Santa_Rosa_Island_2019_MBES_UTM16N_xyz.zip, includes the processed elevation point data (XYZ) as derived from a 1-meter (m) bathymetric grid. |
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Coastal Multibeam Bathymetry Data Collected in August 2017 from the Chandeleur Islands, Louisiana
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of the Chandeleur Islands, Louisiana, August 9-12, 2017. This dataset, Chandeleur_Islands_2017_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Coastal Multibeam Bathymetry Data Collected in August 2018 from the Chandeleur Islands, Louisiana
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of the Chandeleur Islands, Louisiana, August 16-21, 2018. This dataset, Chandeleur_ Islands_2018_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Coastal Multibeam Bathymetry Data Collected in August 2019 from Cedar Island, Virginia
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), covering the nearshore, seaward of Cedar Island, Virginia, from August 14-21, 2019. This dataset, Cedar_ Island_2019_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. Additionally, the dataset Cedar_Island ... |
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Coastal Single-beam Bathymetry Data Collected in 2022 From Breton Island, Louisiana
As part of the restoration monitoring component of the Deepwater Horizon early restoration project, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted single-beam and multibeam bathymetry surveys around Breton Island, Louisiana (LA), from August 3-5, 2022, for Field Activity Number (FAN) 2022-328-FA. The purpose of data collection was to develop a baseline digital elevation model of the seafloor around Breton Island for comparison with both ... |
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Coastal Single-beam Bathymetry Data Collected in 2022 off Seven Mile Island, New Jersey
To determine continued change to the shoreface morphology and evolution at Seven Mile Island, New Jersey, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) in St. Petersburg, Florida, conducted a single-beam bathymetric survey of Seven Mile Island, New Jersey, from April 29 - May 2, 2022. During this study, single-beam bathymetry data were collected using a personal watercraft (PWC) and a floating-towed-seismic sled. Both the PWC and the seismic sled ... |
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Coastal Single-beam Bathymetry Data Collected in August 2018 from the Chandeleur Islands, Louisiana
Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS - SPCMSC) in St. Petersburg, Florida, conducted a single-beam bathymetric survey of the northern Chandeleur Islands, August 17-21, 2018. During this study, bathymetry data were collected aboard the research vessel (R/V) Jabba Jaw, a 21-foot (ft) twin hulled vessel outfitted with a single-beam echosounder. |
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Coastal Single-beam Bathymetry Data Collected in August 2019 from Cedar Island, Virginia
Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS - SPCMSC) in St. Petersburg, Florida, conducted a single-beam bathymetric survey of Cedar Island, Virginia, August 9-15, 2019. During this study, bathymetry data were collected aboard a towed seismic sled outfitted with a single-beam echosounder. |
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Coastal Single-beam Bathymetry Data Collected in September and October 2019 from Rockaway Peninsula, New York
Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS - SPCMSC) in St. Petersburg, Florida, conducted a single-beam bathymetric survey of Rockaway Peninsula, New York September 27 - October 6, 2019. During this study, bathymetry data were collected aboard two personal watercraft (PWC) outfitted with single-beam echosounders, as well as a towed seismic sled with similar instrumentation. |
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Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation
Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, ... |
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CoconutIsland_2023_MBES: High-resolution Geophysical and Imagery Data Collected in May 2023 Near Fort Hase, Marine Corps Base Hawaii
An Ellipsoidally Referenced Survey (ERS) using a Norbit Winghead multibeam echosounder, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Coconut Island, on the island of Oahu, May 7, 2023. This dataset, CoconutIsland_2023_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 0.25 meter (m) bathymetric grid and the dataset CoconutIsland_2023_MBES_Backscatter.zip includes the acoustic backscatter intensity ... |
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Collection, analysis, and age-dating of sediment cores from a salt marsh platform and ponds, Rowley, Massachusetts, 2014-15
Sediment cores were collected from three sites within the Plum Island Ecosystems Long-Term Ecological Research (PIE-LTER) domain in Massachusetts to obtain estimates of long-term marsh decomposition and evaluate shifts in the composition and reactivity of sediment organic carbon in disturbed marsh environments. Paired sediment cores were collected from three sites on the marsh platform and from three ponds; these cores were about 100 and 50 centimeters in length, respectively. The marsh sites had similar ... |
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Collection, analysis, and age-dating of sediment cores from Herring River wetlands and other nearby wetlands in Wellfleet, Massachusetts, 2015–17
The Herring River estuary in Wellfleet, Cape Cod, Massachusetts, has been tidally restricted for more than a century by a dike constructed near the mouth of the river. Upstream from the dike, the tidal restriction has caused the conversion of salt marsh wetlands to various other ecosystems including impounded freshwater marshes, flooded shrub land, drained forested upland, and brackish wetlands dominated by Phragmites australis. This estuary is now managed by the National Park Service, which plans to ... |
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Collection, analysis, and age-dating of sediment cores from mangrove and salt marsh ecosystems in Tampa Bay, Florida, 2015
Coastal wetlands in Tampa Bay, Florida, are important ecosystems that deliver a variety of ecosystem services. Key to ecosystem functioning is wetland response to sea-level rise through accumulation of mineral and organic sediment. The organic sediment within coastal wetlands is composed of carbon sequestered over the time scale of the wetland’s existence. This study was conducted to provide information on soil accretion and carbon storage rates across a variety of coastal ecosystems that was utilized in ... |
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Collection, analysis, and age-dating of sediment cores from mangrove wetlands in San Juan Bay Estuary, Puerto Rico, 2016
The San Juan Bay Estuary, Puerto Rico, contains mangrove forests that store significant amounts of organic carbon in soils and biomass. There is a strong urbanization gradient across the estuary, from the highly urbanized and clogged Caño Martin Peña in the western part of the estuary, a series of lagoons in the center of the estuary, and a tropical forest reserve (Piñones) in the easternmost part with limited urbanization. We collected sediment cores to determine carbon burial rates and vertical ... |
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Collection, analysis, and age-dating of sediment cores from natural and restored salt marshes on Cape Cod, Massachusetts, 2015-16
Nineteen sediment cores were collected from five salt marshes on the northern shore of Cape Cod where previously restricted tidal exchange was restored to part of the marshes. Cores were collected in duplicate from two locations within each marsh complex: one upstream and one downstream from the former tidal restriction (typically caused by an undersized culvert or a berm). The unaltered, natural downstream sites provide a comparison against the historically restricted upstream sites. The sampled cores ... |
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Collection, Analysis, and Age-Dating of Sediment Cores from Salt Marshes on the South Shore of Cape Cod, Massachusetts, From 2013 Through 2014
The accretion history of fringing microtidal salt marshes located on the south shore of Cape Cod, Massachusetts, was reconstructed from sediment cores collected in low and high marsh vegetation zones. The location of these marshes within protected embayments and the absence of large rivers on Cape Cod result in minimal sediment supply and a dominance of organic matter contribution to sediment peat. Age models based on 210-lead and 137-cesium were constructed to evaluate how vertical accretion and carbon ... |
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Collection, Analysis, and Age-Dating of Sediment Cores from Salt Marshes, Rhode Island, 2016
The accretion history of fringing salt marshes in Narragansett Bay, Rhode Island, was reconstructed from sediment cores. Age models, based on excess lead-210 and cesium-137 radionuclide analysis, were constructed to evaluate how vertical accretion and carbon burial rates have changed during the past century. The Constant Rate of Supply (CRS) age model was used to date six cores collected from three salt marshes. Both vertical accretion rates and carbon burial increased from 1900 to 2016, the year the data ... |
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Comparison of methane concentration and stable carbon isotope data for natural samples analyzed by discrete sample introduction module - cavity ring down spectroscopy (DSIM-CRDS) and traditional methods
A discrete sample introduction module (DSIM) was developed and interfaced to a cavity ring-down spectrometer to enable measurements of methane and CO2 concentrations and 13C values with a commercially available cavity ring-down spectrometer (CRDS). The DSIM-CRDS system permits the analysis of limited volume (5 - 100-ml) samples ranging six orders-of-magnitude from 100% analyte to the lower limit of instrument detection (2 ppm). We demonstrate system performance for methane by comparing concentrations and ... |
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Computed Tomography (CT) scans of sediment cores collected from Montague Island, AK
This dataset includes computed tomography (CT) scans of sediment cores collected from coastal environments on Montague Island, Alaska. The cores were collected with hand driven peat augers to assess environmental changes related to tectonic uplift caused by historic and prehistoric earthquakes. |
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Computed tomography (CT) scans of sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset includes computed tomography (CT) scan imagery of sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. |
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Computed tomography (CT) scans of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes computed tomography (CT) scans of sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
The salt marsh complex of Assateague Island National Seashore (ASIS) and Chincoteague Bay was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy ... |
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Conceptual marsh units for Cape Cod National Seashore salt marsh complex, Massachusetts
The salt marsh complex of Cape Cod National Seashore (CACO), Massachusetts, USA and approximal wetlands were delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the ... |
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Conceptual marsh units for Fire Island National Seashore and central Great South Bay salt marsh complex, New York
The salt marsh complex of Fire Island National Seashore (FIIS) and central Great South Bay was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy ... |
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Conceptual marsh units for Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Conceptual marsh units for Plum Island Estuary and Parker River salt marsh complex, Massachusetts
The salt marsh complex of Plum Island Estuary and Parker River (PIEPR) was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location was used to determine the ridge lines that separate each marsh unit while the surface slope was used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. ... |
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Conceptual marsh units of Blackwater salt marsh complex, Chesapeake Bay, Maryland
This data release contains coastal wetland synthesis products for the geographic region of Blackwater, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to ... |
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Conceptual marsh units of Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Conceptual marsh units of Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Conceptual marsh units of eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Conceptual marsh units of Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Conceptual marsh units of Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Conceptual marsh units of Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Conceptual marsh units of north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Conceptual marsh units of salt marshes on the Eastern Shore of Virginia
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Conceptual salt marsh units for wetland synthesis: Edwin B. Forsythe National Wildlife Refuge, New Jersey
The salt marsh complex of the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay (New Jersey, USA), was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain ... |
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Conductivity, temperature and depth time-series data collected in 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, ... |
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Continuous core photographs of sediment cores collected in 2009 offshore from Palos Verdes, California
This part of the data release includes continuous core photographs in bmp format of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan ... |
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Continuous core photographs of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California
This part of the data release includes continuous core photographs in bmp format of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely ... |
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Continuous Resistivity Profiling, Electrical Resistivity Tomography and Hydrologic Data Collected in 2017 from Indian River Lagoon, Florida
Extending 200 kilometers (km) along the Atlantic Coast of Central Florida, Indian River Lagoon (IRL) is one of the most biologically diverse estuarine systems in the continental United States. The lagoon is characterized by shallow, brackish waters and a width that varies between 0.5 and 9.0 km; there is significant human development along both shores. Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center used continuous resistivity profiling (CRP, a towed ... |
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Coordinates of vibracores collected offshore central California, during field activity 2019-651-FA (ver 2.0, August 2023)
This dataset includes coordinate information for sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Coral geochemistry time series from Kahekili, west Maui
Geochemical analysis (including stable boron, boron:calcium ratio, and carbon and oxygen isotopes) were measured from coral cores collected in July 2013 from the shallow reef at Kahekili in Kaanapali, west Maui, Hawaii from scleractinian Porites lobata. |
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Coral growth parameters, Kahekili, west Maui
Surface runoff and submarine groundwater discharge in particular are known vectors to the coastal ocean of elevated nutrients and contaminants leading to eutrophication, algal overgrowth, and coral disease. Freshwater discharging directly from submarine groundwater vents off of Kahekili Beach Park, Kaanapali, in West Maui contains elevated nutrient concentrations and lower pH values. Coral cores were collected in July 2013 from the shallow reef at Kahekili in Kaanapali, West Maui, Hawaii from ... |
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Coral reef profiles for wave-runup prediction
This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the data were reduced by clustering cross-shore profiles based on morphology and hydrodynamic ... |
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Core descriptions and sedimentologic data from vibracores and sand augers collected in 2021 and 2022 from Fire Island, New York
In 2021 and 2022, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) and the USGS New York Water Science Center (NYWSC), on behalf of SPCMSC, conducted sediment sampling and ground penetrating radar (GPR) surveys at Point O' Woods and Ho-Hum Beach (NYWSC, 2021) and Watch Hill, Long Cove, and Smith Point (SPCMSC, 2022), Fire Island, New York. These data complement previous SPCMSC GPR and sediment sampling surveys conducted at Fire Island in 2016 ... |
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CoSMoS 3.2 Northern California sub-regional tier 2 FLOW-WAVE model input files
This data set consists of physics-based Delft3D-FLOW and WAVE hydrodynamic model input files used for Coastal Storm Modeling System (CoSMoS) sub-regional tier 2 simulations. Sub-regional tier 2 simulations cover portions of the Northern California open-coast region, from Point Arena to the California/Oregon state border, and they provide boundary conditions to higher-resolution simulations. Simulations are run for several storm events (covering a range of no storm, 1-year, 20-year, and 100-year coastal ... |
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CoSMoS 3.2 Northern California Tier 1 FLOW-WAVE model input files
This data set consists of physics-based Delft3D-FLOW and WAVE hydrodynamic model input files used for Coastal Storm Modeling System (CoSMoS) Tier 1 simulations. Tier 1 simulations cover the Northern California open-coast region, from the Golden Gate Bridge to the California/Oregon state border, and they provide boundary conditions to higher-resolution simulations. Simulations are run for several storm events (covering a range of no storm, 1-year, 20-year, and 100-year coastal storm conditions) and sea-level ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 100-year storm in Monterey County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 100-year storm in San Francisco County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 100-year storm in San Luis Obispo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 100-year storm in San Mateo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 100-year storm in Santa Barbara County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 100-year storm in Santa Cruz County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 1-year storm in Monterey County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 1-year storm in San Francisco County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 1-year storm in San Luis Obispo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 1-year storm in San Mateo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 1-year storm in Santa Barbara County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 1-year storm in Santa Cruz County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 20-year storm in Monterey County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 20-year storm in San Francisco County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 20-year storm in San Luis Obispo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 20-year storm in San Mateo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 20-year storm in Santa Barbara County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: 20-year storm in Santa Cruz County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: average conditions in Monterey County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: average conditions in San Francisco County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: average conditions in San Luis Obispo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: average conditions in San Mateo County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: average conditions in Santa Barbara County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood depth and duration projections: average conditions in Santa Cruz County
This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood hazard projections: 100-year storm in Monterey County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 100-year storm in San Francisco County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 100-year storm in San Luis Obispo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 100-year storm in San Mateo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 100-year storm in Santa Barbara County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 100-year storm in Santa Cruz County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood hazard projections: 1-year storm in Monterey County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 1-year storm in San Francisco County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 1-year storm in San Luis Obispo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 1-year storm in San Mateo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 1-year storm in Santa Barbara County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 1-year storm in Santa Cruz County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood hazard projections: 20-year storm in Monterey County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 20-year storm in San Francisco County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 20-year storm in San Luis Obispo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 20-year storm in San Mateo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 20-year storm in Santa Barbara County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: 20-year storm in Santa Cruz County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood hazard projections: average conditions in Monterey County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: average conditions in San Francisco County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: average conditions in San Luis Obispo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: average conditions in San Mateo County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: average conditions in Santa Barbara County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 flood-hazard projections: average conditions in Santa Cruz County
This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 100-year storm in Monterey County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 100-year storm in San Francisco County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 100-year storm in San Luis Obispo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 100-year storm in San Mateo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 100-year storm in Santa Barbara County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 100-year storm in Santa Cruz County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 1-year storm in Monterey County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 1-year storm in San Francisco County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 1-year storm in San Luis Obispo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 1-year storm in San Mateo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 1-year storm in Santa Barbara County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 1-year storm in Santa Cruz County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 20-year storm in Monterey County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 20-year storm in San Francisco County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 20-year storm in San Luis Obispo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 20-year storm in San Mateo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 20-year storm in Santa Barbara County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: 20-year storm in Santa Cruz County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: average conditions in Monterey County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: average conditions in San Francisco County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: average conditions in San Luis Obispo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: average conditions in San Mateo County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: average conditions in Santa Barbara County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 ocean-currents projections: average conditions in Santa Cruz County
This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 100-year storm in Monterey County
This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 100-year storm in San Francisco County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 100-year storm in San Luis Obispo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 100-year storm in San Mateo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 100-year storm in Santa Barbara County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 100-year storm in Santa Cruz County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 1-year storm in Monterey County
This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 1-year storm in San Francisco County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 1-year storm in San Luis Obispo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 1-year storm in San Mateo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 1-year storm in Santa Barbara County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 1-year storm in Santa Cruz County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 20-year storm in Monterey County
This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 20-year storm in San Francisco County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 20-year storm in San Luis Obispo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 20-year storm in San Mateo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 20-year storm in Santa Barbara County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: 20-year storm in Santa Cruz County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: average conditions in Monterey County
This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: average conditions in San Francisco County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: average conditions in San Luis Obispo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: average conditions in San Mateo County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: average conditions in Santa Barbara County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 water-level projections: average conditions in Santa Cruz County
This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 100-year storm in Monterey County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 100-year storm in San Francisco County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 100-year storm in San Luis Obispo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 100-year storm in San Mateo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 100-year storm in Santa Barbara County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 100-year storm in Santa Cruz County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 1-year storm in Monterey County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 1-year storm in San Francisco County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 1-year storm in San Luis Obispo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 1-year storm in San Mateo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 1-year storm in Santa Barbara County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 1-year storm in Santa Cruz County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 20-year storm in Monterey County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 20-year storm in San Francisco County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 20-year storm in San Luis Obispo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 20-year storm in San Mateo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 20-year storm in Santa Barbara County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: 20-year storm in Santa Cruz County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: average conditions in Monterey County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: average conditions in San Francisco County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: average conditions in San Luis Obispo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: average conditions in San Mateo County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: average conditions in Santa Barbara County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
Info |
CoSMoS (Coastal Storm Modeling System) Central California v3.1 wave-hazard projections: average conditions in Santa Cruz County
This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 coastal squeeze projections
Projected coastal squeeze derived from CoSMoS Phase 2 shoreline change and cliff retreat projections. Projected coastal squeeze extents illustrate the available area between shoreline (mean high water; MHW) positions and man-made structures and barriers (referred to as non-erodible structures) or cliff-top retreat, as applicable, for a range of sea-level rise scenarios. The coastal squeeze polygons include results from the Coastal Storm Modeling System (CoSMoS) shoreline change (CoSMoS-COAST; Vitousek and ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 100-year storm in Los Angeles County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 100-year storm in Orange County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 100-year storm in San Diego County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 100-year storm in Santa Barbara County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 100-year storm in the Channel Islands
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 100-year storm in Ventura County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 1-year storm in Los Angeles County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 1-year storm in Orange County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 1-year storm in San Diego County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 1-year storm in Santa Barbara County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 1-year storm in the Channel Islands
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 1-year storm in Ventura County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 20-year storm in Los Angeles County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 20-year storm in Orange County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 20-year storm in San Diego County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 20-year storm in Santa Barbara County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 20-year storm in the Channel Islands
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: 20-year storm in Ventura County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: average conditions in Los Angeles County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: average conditions in Orange County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: average conditions in San Diego County
Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: average conditions in Santa Barbara County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: average conditions in the Channel Islands
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard depth and duration projections: average conditions in Ventura County
Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 100-year storm in Los Angeles County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 100-year storm in Orange County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 100-year storm in San Diego County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 100-year storm in Santa Barbara County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 100-year storm in the Channel Islands
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 100-year storm in Ventura County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in Los Angeles County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in Orange County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in San Diego County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in Santa Barbara County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in the Channel Islands
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in Ventura County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 20-year storm in Los Angeles County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 20-year storm in Orange County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 20-year storm in San Diego County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 20-year storm in Santa Barbara County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 20-year storm in the Channel Islands
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 20-year storm in Ventura County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: average conditions in Los Angeles County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: average conditions in Orange County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: average conditions in San Diego County
Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: average conditions in Santa Barbara County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: average conditions in the Channel Islands
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: average conditions in Ventura County
Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 100-year storm in Los Angeles County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 100-year storm in Orange County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 100-year storm in San Diego County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 100-year storm in Santa Barbara County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 100-year storm in the Channel Islands
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 100-year storm in Ventura County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 1-year storm in Los Angeles County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 1-year storm in Orange County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 1-year storm in San Diego County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 1-year storm in Santa Barbara County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 1-year storm in the Channel Islands
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 1-year storm in Ventura County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 20-year storm in Los Angeles County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 20-year storm in Orange County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 20-year storm in San Diego County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 20-year storm in Santa Barbara County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
Info |
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 20-year storm in the Channel Islands
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: 20-year storm in Ventura County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: average conditions in Los Angeles County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: average conditions in Orange County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: average conditions in San Diego County
Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: average conditions in Santa Barbara County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: average conditions in the Channel Islands
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 ocean-currents projections: average conditions in Ventura County
Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 runup projections
Geographic extent of projected runup associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 100-year storm in Los Angeles County
Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 100-year storm in Orange County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 100-year storm in San Diego County
Projected Hazard: Model-derived water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 100-year storm in Santa Barbara County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 100-year storm in the Channel Islands
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 100-year storm in Ventura County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 1-year storm in Los Angeles County
Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 1-year storm in Orange County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 1-year storm in San Diego County
Projected Hazard: Model-derived water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 1-year storm in Santa Barbara County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 1-year storm in the Channel Islands
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 1-year storm in Ventura County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in Los Angeles County
Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in Orange County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in San Diego County
Projected Hazard: Model-derived water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in Santa Barbara County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in the Channel Islands
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in Ventura County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: average conditions in Los Angeles County
Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: average conditions in Orange County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: average conditions in San Diego County
Projected Hazard: Model-derived water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: average conditions in Santa Barbara County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: average conditions in the Channel Islands
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: average conditions in Ventura County
Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 100-year storm in Channel Islands
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 100-year storm in Los Angeles County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 100-year storm in Orange County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 100-year storm in San Diego County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 100-year storm in Santa Barbara County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 100-year storm in Ventura County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 1-year storm in Channel Islands
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 1-year storm in Los Angeles County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 1-year storm in Orange County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 1-year storm in San Diego County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 1-year storm in Santa Barbara County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 1-year storm in Ventura County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 20-year storm in Channel Islands
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 20-year storm in Los Angeles County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 20-year storm in Orange County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 20-year storm in San Diego County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 20-year storm in Santa Barbara County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: 20-year storm in Ventura County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: average conditions in Channel Islands
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: average conditions in Los Angeles County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: average conditions in Orange County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: average conditions in San Diego County
Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: average conditions in Santa Barbara County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 wave-hazard projections: average conditions in Ventura County
Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal ... |
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CoSMoS Southern California v3.0 Phase 2 projections of coastal cliff retreat due to 21st century sea-level rise
This dataset contains projections of coastal cliff-retreat rates and positions for future scenarios of sea-level rise (SLR). Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical and statistical models based on field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS) v.3.0 Phase 2 in Southern California. Details: Cliff ... |
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CoSMoS Southern California v3.0 projections of shoreline change due to 21st century sea-level rise
This dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using CoSMoS-COAST, a numerical model forced with global-to-local nested wave models and assimilated with lidar-derived shoreline vectors. Details: Projections of shoreline position in Southern California are made for scenarios of 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, and 5.0 meters of sea-level rise by the year 2100. Four datasets are available for different ... |
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CoSMoS Whatcom County model input files
This data set consists of physics-based XBeach and SFINCS hydrodynamic model input files used for Coastal Storm Modeling System (CoSMoS) Tier 3 simulations. This data release is for Whatcom County in Washington State and presents the final tier 3 models used to produce output data that is then post-processed into final CoSMoS products. Example model input and configuration files are included for a single domain and SLR scenario, with the full modelling framework iterating on this process to simulate ... |
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CTD_casts
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months, when ice melt is at its greatest extent. However, few comprehensive datasets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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CTD_casts
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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CTD (conductivity-temperature-depth) data collected by the U.S. Geological Survey on Stellwagen Bank during six surveys aboard the R/V Auk, May 2016 to April 2019
These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species ... |
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CTD (conductivity-temperature-depth) data collected by the U.S. Geological Survey on Stellwagen Bank during three surveys aboard the R/V Auk, September 2020 to August 2021
These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2013-044-FA, aboard the R/V Auk, November 5, 15, and 21, 2013
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2014-015-FA, aboard the R/V Auk, May 22-23 and 29-30, 2014
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2014-055-FA, aboard the R/V Auk, September 23 and 24, 2014
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2014-066-FA, aboard the R/V Auk, November 10, 2014
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2014-070-FA, aboard the R/V Auk, December 12, 2014
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2015-017-FA, aboard the R/V Auk, May 18-19, 29, and June 3, 2015
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2015-074-FA, aboard the R/V Auk, December 1, 2015
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2016-004-FA, aboard the R/V Auk, January 28, 2016
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2016-038-FA, aboard the R/V Auk, Sept. 16 and 19, 2016
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2017-030-FA, aboard the R/V Auk, May 18-23, 2017
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2017-043-FA, aboard the R/V Auk, Aug. 22 and 23, 2017
This field activity is part of an effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000-scale) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The data collected in this study will aid research on the ecology of fish and invertebrate species that inhabit the region. On August 22 and 23, 2017, the U.S. Geological ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2017-044-FA, aboard the R/V Auk, September 12-14, 2017
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank during U.S. Geological Survey field activity 2019-008-FA, aboard the R/V Auk, July 30, 31, and August 1, 2019
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank on U.S. Geological Survey field activity 2015-062-FA, aboard the R/V Auk, Oct. 21 and 22 and Nov. 3 and 4 2015
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD (conductivity-temperature-depth) data collected on Stellwagen Bank on U.S. Geological Survey field activity 2017-009-FA, aboard the R/V Auk, Jan. 30, 2017
This field activity is part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. This cruise was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate ... |
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CTD_DATABASE - Cascadia tsunami deposit database
The Cascadia Tsunami Deposit Database contains data on the location and sedimentological properties of tsunami deposits found along the Cascadia margin. Data have been compiled from 52 studies, documenting 59 sites from northern California to Vancouver Island, British Columbia that contain known or potential tsunami deposits. Bibliographical references are provided for all sites included in the database. Cascadia tsunami deposits are usually seen as anomalous sand layers in coastal marsh or lake sediments. ... |
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Current profiler time-series data collected in 2009 offshore of Wainwright, Alaska
A time-series of binned current-velocities and recorded ping amplitudes were collected offshore Wainwright, Alaska, from August 24 to October 02, 2009 (UTC). Measurements were collected using a 1 MHz NortekTM AWAC acoustic Doppler current profiler mounted on a frame in approximately 10 m of water. The profiler was mounted on the frame 0.55 m off the bottom of the seafloor, and collected data in 8 vertical bins, centered at 1.95(bin1), 2.95, 3.95, 4.95, 5.95, 6.95, 7.95, and 8.95(bin8) meters above the ... |
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Current-velocity time-series data collected in 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, ... |
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Daily sediment loads during and after dam removal in the Elwha River, Washington, 2011 to 2016
Daily values of discharge and sediment loads were measured and estimated at U.S. Geological Survey gaging station 12046260, on the Elwha River at the diversion near Port Angeles, Washington. Daily data are reported from September 15, 2011 to September 30, 2016. Specific data include (1) date; (2) discharge; (3) suspended-sediment concentration and one standard-deviation bounds; (4) percentage of fine-grained particles (silts and clays) in suspension; (5) loads of total suspended-sediment, fine-grained ... |
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Data and calculations to support the study of the sea-air flux of methane and carbon dioxide on the West Spitsbergen margin in June 2014
A critical question for assessing global greenhouse gas budgets is how much of the methane that escapes from seafloor cold seep sites to the overlying water column eventually crosses the sea-air interface and reaches the atmosphere. The issue is particularly important in Arctic Ocean waters since rapid warming there increases the likelihood that gas hydrate--an ice-like form of methane and water stable at particular pressure and temperature conditions within marine sediments--will break down and release its ... |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration alternative for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration alternative for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration alternative for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration measures for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 2 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 3 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 4 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 5 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 6 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 7 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Initial DEMs with and without restoration alternatives R2-R7
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Hindcast Model Inputs and Results: Final DEM
The model output of bathymetry and topography values resulting from a deterministic simulation at Dauphin Island, Alabama, as described in USGS Open-File Report 2019–1139 (https://doi.org/10.3133/ofr20191139), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry refer to Mickey and others (2020). |
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Dauphin Island Decadal Hindcast Model Inputs and Results: Initial DEM
The model input for the bathymetry and topography values resulting from a deterministic simulation at Dauphin Island, Alabama, as described in U.S. Geological Survey (USGS) Open-File Report 2019-1139 (https://doi.org/10.3133/ofr20191139), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry refer to Mickey and others (2020). |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Ivan Intermediate-Low Sea Level Rise (SLR) Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Ivan Low Sea Level Rise (SLR) Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Ivan Present-Day Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Ivan Static Intermediate-Low Sea Level Rise Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Ivan Static Low Sea Level Rise Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Katrina Intermediate-Low Sea Level Rise Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Katrina Low Sea Level Rise Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Dauphin Island Storms and Sea Level Rise Assessment: XBeach Model Input and Results for the Hurricane Katrina Present-Day Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) ... |
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Delineated Coastal Cliff Toes Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018
The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff toes that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects ... |
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Delineated Coastal Cliff Tops Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018
The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff tops that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects ... |
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Delineated Coastal Cliff Transects Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018
The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff transects that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) ... |
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Deployments of autonomous, GPS ocean ocean-surface drifters, Makua, Kauai, USA, August 2016
Satellite-tracked, DGPS-equipped Lagrangian surface-current drifter deployments were conducted over 6 days between 30 July and 4 August 2016 at various locations and stages of the tide over the coral reef off Makua, HI. The drifters internally logged their location every 1 minute, and they transmitted their positions to satellites every 5 minutes. A drogue was attached to the drifters at 1 m below sea level in order to track the currents at that depth. |
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Digital elevation models (DEMs) of coastal North Carolina, from 2019-08-30 to 2019-09-02, Pre-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected between August 30 and September 2, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions prior to Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface before Hurricane Dorian and were created to document inter-annual changes in ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected between September 08 and September 13, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions post-Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface after Hurricane Dorian and were created to document inter-annual changes in ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2020-02-08 to 2020-02-09
Digital elevation models (DEMs) were created from aerial imagery collected February 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers document inter-annual changes in shoreline position and coastal morphology in response to storm events using ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2020-05-08 to 2020-05-09
Digital elevation models (DEMs) were created from aerial imagery collected May 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers document inter-annual changes in shoreline position and coastal morphology in response to storm events using aerial ... |
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Digital elevation models (DEMs) of coastal North Carolina, on 2019-10-11, one month Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected October 11, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions one-month after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface one-month post-Hurricane Dorian and were created to document inter-annual changes in ... |
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Digital elevation models (DEMs) of coastal North Carolina, on 2019-11-26, two-months Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected November 26, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions two-months after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface one-month post-Hurricane Dorian and were created to document inter-annual changes in ... |
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Digital elevation models of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents digital elevation models (DEMs) spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the DEMs were created, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected ... |
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Digital seafloor images and sediment grain size from the mouth of the Columbia River, Oregon and Washington, 2014
This dataset includes 2,523 still images extracted from geo-referenced digital video imagery of the seafloor at the mouth of the Columbia River, OR and WA, USA, along with grain size analysis of the surface sediment. Underwater digital video was collected in September 2014 in the mouth of the Columbia River, USA, as part of the U.S. Geological Survey Coastal and Marine Geology Program contribution to the Office of Naval Research funded River and Inlets Dynamics experiment (RIVET II). Still images were ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between the Okpilak-Hulahula River Delta and Colville River Delta for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between the U.S.-Canadian border and the Okpilak-Hulahula river delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
Info |
Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
Info |
Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between the Okpilak-Hulahula River Delta and Colville River Delta for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
Info |
Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between the U.S.-Canadian border and the Okpilak-Hulahula River Delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term End Point Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2011. A reference baseline was used as the originating point ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term End Point Rate Calculations for the Sheltered East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the originating point ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between the Point Barrow and Icy Cape
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with long-term linear regression rate calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of long-term (less than 68 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with long-term linear regression rate calculations for the sheltered north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of long-term (less than 68 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline was used ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term end-point rate-of-change calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of short-term (less than 37 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term end-point rate-of-change calculations for the sheltered north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of short-term (less than 37 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term linear regression rate calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of short-term (less than 37 years) shoreline change rates for the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1980s and 2016. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 5.0 transects with bluff rate change calculations for the north coast of Barter Island Alaska, 1950 to 2020
This dataset consists of rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each bluff line establishing measurement points, which are then used to ... |
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Digital Shoreline Analysis System (DSAS) version 5.0 transects with shoreline rate change calculations at Barter Island Alaska, 1947 to 2020
This dataset consists of rate-of-change statistics for the shorelines at Barter Island, Alaska for the time period 1947 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Cape Cod region from Provincetown to the southern end of Monomoy Island, Massachusetts (CapeCod_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Delmarva North region from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia (DelmarvaN_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Delmarva South/Southern Virginia region from Wallops Island, Virginia to the Virginia/North Carolina border (DelmarvaS_SVA_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Greater Boston region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts (GreaterBoston_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Massachusetts Islands Region including Martha's Vineyard and Nantucket (MA_Islands_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New England North region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts (NewEnglandN_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New England South region from Dartmouth, Massachusetts to Napatree Point, Rhode Island (NewEnglandS_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New Jersey North region from Sandy Hook to Little Egg Inlet, New Jersey (NewJerseyN_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New Jersey South region from Little Egg Inlet to Cape, May, New Jersey (NewJerseyS_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Rate Calculations for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Cape Cod region from Provincetown to the southern end of Monomoy Island, Massachusetts (CapeCod_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Delmarva North region from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia (DelmarvaN_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Delmarva South/Southern Virginia region from Wallops Island, Virginia to the Virginia/North Carolina border (DelmarvaS_SVA_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Greater Boston region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts (GreaterBoston_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Massachusetts Islands Region including Martha's Vineyard and Nantucket (MA_Islands_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New England North region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts (NewEnglandN_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New England South region from Dartmouth, Massachusetts to Napatree Point, Rhode Island (NewEnglandS_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New Jersey North region from Sandy Hook to Little Egg Inlet, New Jersey (NewJerseyN_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New Jersey South region from Little Egg Inlet to Cape, May, New Jersey (NewJerseyS_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Long-Term Linear Regression Rate Calculations for Oregon (OR_transects_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Long-Term Linear Regression Rate Calculations for Washington (WA_transects_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Short-Term End Point Rate Calculations for Oregon (OR_transects_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Short-Term End Point Rate Calculations for Washington (WA_transects_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston harbor (NorthShore_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands Massachusetts-Rhode Island border (Nantucket_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970's and 1994 shorelines within the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Cape Cod Bay coastal region from Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate)shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term linear regression rate (LRR) shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Cape Cod Bay coastal region from Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available in the Boston coastal region Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available in the Buzzards Bay coastal region from Nobska Point in Woods Hole to Westport at the Rhode Island border (BuzzardsBay_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available in the North Shore coastal region from North Salisbury at the New Hampshire border to the to the west side of Deer Island in Boston Harbor (NorthShore_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 in the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 in the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 in the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970s and 1994 within the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point rate shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point rate shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point rate shoreline change statistics within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression rate shoreline change statistics within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for all data available in the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island including the Boston Harbor Islands (NorthShore_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term end point shoreline change statistics for all data available within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Texas east (TXeast)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Texas west (TXwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for western North Carolina (NCwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Texas east (TXeast)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Texas west (TXwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for western North Carolina (NCwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital surface models of Pea Island National Wildlife Refuge DUNEX Site, North Carolina in September and October 2021
The data in this part of the release are digital surface models (DSMs) that characterize the beach at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. DUNEX is a multi-agency, academic, and non-governmental organization collaborative community experiment designed to study nearshore coastal processes during storm events. USGS participation in DUNEX will contribute new measurements and models that will increase our understanding of storm impacts to coastal ... |
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Discharge measurements at Thompsons Beach, New Jersey, collected October 2018 and September 2022
In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science ... |
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Discharge measurements collected in the Stillaguamish River Delta, Port Susan, Washington, USA in March, April, and May 2014
Tidal water discharge within two breaches constructed in a former flood-control levee of a restored agricultural area in Port Susan, Washington, was measured repeatedly during several tidal cycles. Measurements were made on March 27, 2014, April 16, 2014, May 18, 2014, and May 29, 2014 at breach PSB1, and on May 29, 2014 at breach PSB2. These data were collected using a boat-mounted Teledyne RDI RiverRay 600 kHz acoustic Doppler current profiler (ADCP) or a Teledyne RDI StreamPro 2000 kHz ADCP, depending on ... |
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Discharge Measurements in Bayou Heron and Bayou Middle, Grand Bay, Mississippi, January 2017
Grand Bay, a 30-square-kilometer embayment of the Gulf of Mexico bordered by 20 square kilometers of salt marsh, is experiencing rapid lateral shoreline erosion at up to 5 meters per year. Determining whether the eroded sediment is exported to the deep ocean or imported via tidal channels and deposited on the marsh platform is critical to understanding the long-term response of the marsh to wave attack and sea-level rise. Quantifying water-column sediment flux helps to characterize the role of tidal ... |
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Discrete Carbonate System Parameter Measurements in Middle Tampa Bay, Florida and the Eastern Gulf of Mexico, USA
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida and eastern Gulf of Mexico. Discrete seawater samples were collected periodically (every few weeks to months) at repeat monitoring locations. Water samples were analyzed by the USGS Carbon Analytical Laboratory in St. ... |
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Discrete surface water data for samples collected in-transit along the West Florida Shelf in July and August, 2013
The United States Geological Survey (USGS) is studying the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises on the West Florida Shelf and northern Gulf of Mexico regions aboard the research vessel (R/V) Weatherbird II or Bellows, ships of opportunity led by Dr. Kendra Daly, of the University of South Florida (USF) in July and August, ... |
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Discrete water column sample data from predefined locations (stations) of the West Florida Shelf collected in July and August, 2013
The United States Geological Survey (USGS) is studying the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises on the West Florida Shelf and northern Gulf of Mexico regions aboard the research vessel (R/V) Weatherbird II or Bellows, ships of opportunity led by Dr. Kendra Daly, of the University of South Florida (USF) in July and August, ... |
Info |
Dynamically downscaled future wave projections from SWAN model results for the main Hawaiian Islands
Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project ... |
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EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: Bare Earth
ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation ... |
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EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: Bare Earth
A bare-earth topography Digital Elevation Model (DEM) mosaic for the Cape Cod National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system ... |
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EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: First Surface
ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation ... |
Info |
EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: First Surface
A first-surface topography Digital Elevation Model (DEM) mosaic for the Cape Cod National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging ... |
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Eddy covariance fluxes of carbon dioxide and methane from the Herring River in Wellfleet, MA (ver 2.0, June 2022)
Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted vast areas of coastal wetlands to tidal exchange. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls of carbon exchange in these understudied ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS_MP)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS_MP_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS_MP)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS_MP_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_MP)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_MP_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Elemental chemistry, radionuclides, and charcoal in watershed soil and reef sediment at Olowalu, Maui, 2022
Fine-sediment elemental chemistry, short-lived cosmogenic radionuclides (Beryllium-7, Cesium-137, and Lead-210), charcoal counts, and total organic carbon contents were quantified to describe urban and wildfire effects and land-based sediment sources and runoff to Olowalu Reef in February 2022. |
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Elevation data collected in 2009 on the beach and foreshore in the vicinity of Wainwright, Alaska
Beach and foreshore elevation data were collected in the vicinity of Wainwright, Alaska. The area from the mouth of the Kuk River to about 8 km to the northeast was measured in August 2009. The area from the mouth of the Kuk River to about 5 km to the northeast was measured in October 2009. The elevation data were collected with Real-Time Kinematic (RTK) Global Positioning System (GPS) systems mounted on all-terrain vehicles. The GPS sampling rate was 1 Hz with vehicle speeds maintained at less than 15 km ... |
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Elevation data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from July 2018 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Elevation data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Elevation of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
Elevation distribution in the Assateague Island National Seashore (ASIS) salt marsh complex and Chincoteague Bay is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal ... |
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Elevation of marsh units in Blackwater salt marsh complex, Chesapeake Bay, Maryland
This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Elevation of marsh units in Cape Cod National Seashore salt marsh complex, Massachusetts
Elevation distribution in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2019). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED), where data gaps exist. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast ... |
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Elevation of marsh units in Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Elevation of marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Elevation of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Elevation of marsh units in Eastern Shore of Virginia salt marshes
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Elevation of marsh units in Fire Island National Seashore and central Great South Bay salt marsh complex, New York
Elevation distribution in the Fire Island National Seashore and central Great South Bay salt marsh complex is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands ... |
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Elevation of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Elevation of marsh units in Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Elevation of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Elevation of marsh units in Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Elevation of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Elevation of marsh units in Plum Island Estuary and Parker River salt marsh complex, Massachusetts
This data release provides elevation distribution in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex. Elevation distribution was calculated in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data was based on the 1-meter gridded Digital Elevation Model and supplemented by 1-meter resampled 1/9 arc-second resolution National Elevation Data, where data gaps exist. Through scientific efforts initiated with the Hurricane Sandy Science Plan, ... |
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Elevation of salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey
Elevation distribution in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2016). The elevation data is based on the 1-meter resampled 1/9 arc-second resolution USGS National Elevation Data. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and ... |
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Elevation of the late Wisconsinan to early Holocene regressive unconformity (Ur) offshore of western and southern Martha's Vineyard and north of Nantucket, Massachusetts
Geologic, sediment texture, and physiographic zone maps characterize the sea floor south and west of Martha's Vineyard and north of Nantucket, Massachusetts. These maps were derived from interpretations of seismic-reflection profiles, high-resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and surficial sediment samples. The interpretation of the seismic stratigraphy and mapping of glacial and Holocene marine units provided a foundation on which the surficial maps were created. This ... |
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Elevation point clouds of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents georeferenced elevation point clouds spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the point clouds were derived, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were ... |
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Experimental coral-growth rate, reef survey, and time-series imagery data collected between 1998 and 2017 to investigate construction and erosion of Orbicella coral reefs in the Florida Keys, U.S.A.
The USGS Coral Reef Ecosystems Studies project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. This data release contains data on coral-growth rates for Orbicella sp. coral colonies grown at five sites on the Florida Keys reef tract from 2013 to 2015, survey data for census-based carbonate budgeting at Hen and Chickens Reef (Islamorada, Florida) collected in 2017, and time-series photographs taken of permanent markers ... |
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Experimental coral-physiology data for Acropora palmata in Florida, U.S.A.
The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (https://coastal.er.usgs.gov/crest/) provides science that helps Department of Interior and other resource managers tasked with the stewardship of coral reef resources. This data release contains data on coral physiology of the elkhorn coral, Acropora palmata, grown at five sites along the Florida outer reef tract including in Biscayne National Park, the Florida Keys National Marine Sanctuary, and Dry Tortugas National Park, ... |
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Exposure potential of marsh units to environmental health stressors in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Exposure potential of marsh units to environmental health stressors in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Exposure potential of marsh units to environmental health stressors in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Exposure potential of marsh units to environmental health stressors in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge to environmental health stressors (polygon shapefile)
Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays ... |
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Extratropical Storm Jan2016 Assessment of Potential Coastal Change Impacts: 1200 PM EST FRI JAN 22 2016
This dataset defines storm-induced coastal erosion hazards for the Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct impact of the Extratropical Storm in January 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities ... |
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Extratropical Storm March 2018 Assessment of Potential Coastal Change Impacts: 0800 AM EST FRI MAR 02 2018
This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island, Massachusetts, New Hampshire and Maine coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of an Extratropical Storm in March 2018. Storm-induced water levels, due to both surge and waves, were ... |
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F4100300001D.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Gearhart
FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment. FIRMs also provide a basis for establishing flood insurance coverage premium rates offered through the National Flood Insurance Program (NFIP). These maps were published as paper documents, which have been scanned into image files (TIFF) as part of FEMA's FIRM modernization process. This is one of three scanned maps for the Seaside-Gearhart ... |
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F4100320001.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Seaside 1
FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment. FIRMs also provide a basis for establishing flood insurance coverage premium rates offered through the National Flood Insurance Program (NFIP). These maps were published as paper documents, which have been scanned into image files (TIFF) as part of FEMA's FIRM modernization process. This is one of three scanned maps for the Seaside-Gearhart ... |
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F4100320002C.TIF - FEMA Flood Insurance Rate Maps for the Seaside-Gearhart, Oregon, Area: Seaside 2
FEMA's Flood Insurance Rate Map (FIRM) depicts the spatial extent of Special Flood Hazard Areas (SFHAs) and other thematic features related to flood risk assessment. FIRMs also provide a basis for establishing flood insurance coverage premium rates offered through the National Flood Insurance Program (NFIP). These maps were published as paper documents, which have been scanned into image files (TIFF) as part of FEMA's FIRM modernization process. This is one of three scanned maps for the Seaside-Gearhart ... |
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Fall 2000 USGS Mid-Atlantic Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2000 Atlantic Coast U.S. ... |
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Faults--Drakes Bay and Vicinity, California
This part of DS 781 presents data of faults for the geologic and geomorphologic map of the Drakes Bay and Vicinity map area, California. The vector data file is included in "Faults_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., ... |
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Faults--Hueneme Canyon and Vicinity, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Faults_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K ... |
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Faults--Monterey Canyon and Vicinity Map Area, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Monterey Canyon and Vicinity map area, California. The vector data file is included in "Faults_MontereyCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G. ... |
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Faults--Offshore of Aptos Map Area, California
This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore Aptos map area, California. The vector data file is included in "Faults_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, ... |
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Faults--Offshore of Bodega Head Map Area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Bodega Head map area, California. The vector data file is included in "Faults_OffshoreBodegaHead.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreBodegaHead/data_catalog_OffshoreBodegaHead.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., ... |
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Faults--Offshore of Bolinas Map Area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Bolinas map area, California. The vector data file is included in "Faults_OffshoreBolinas.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreBolinas/data_catalog_OffshoreBolinas.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Manson, M.W., Sliter, R.W., Endris, C.A., Watt, J.T., ... |
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Faults--Offshore of Carpinteria, California
This part of DS 781 presents data for fault data for the Offshore of Carpinteria map area, California. The vector data file is included in "Faults_OffshoreCarpinteria.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreCarpinteria/data_catalog_OffshoreCarpinteria.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., ... |
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Faults--Offshore of Coal Oil Point, California
This part of DS 781 presents fault data for the Offshore of Coal Oil Point map area, California. The vector data file is included in "Faults_OffshoreCoalOilPoint.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreCoalOilPoint/data_catalog_OffshoreCoalOilPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Dieter, B.E., Conrad, J.E., Lorenson, T.D., Krigsman, L.M., Greene, H.G., ... |
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Faults--Offshore of Fort Ross Map Area, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Faults_OffshoreFortRoss.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., ... |
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Faults--Offshore of Gaviota Map Area, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Gaviota map area, California. The vector data file is included in "Faults_OffshoreGaviota.zip," which is accessible from https://doi.org/10.5066/F7TH8JWJ. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Hartwell, S.R., Golden, N.E., Kvitek, R.G., and Davenport, C.W. (S.Y. Johnson and S.A. Cochran, eds.), 2018, California State Waters Map Series—Offshore of Gaviota ... |
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Faults--Offshore of Half Moon Bay Map Area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Half Moon Bay map area, California. The vector data file is included in "Faults_OffshoreHalfMoonBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreHalfMoonBay/data_catalog_OffshoreHalfMoonBay.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Greene, H.G., Johnson, S.Y., Golden, N.E., Hartwell, S.R., Dieter, B.E., Manson, M.W., Sliter, R.W., Ross, S.L. ... |
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Faults--Offshore of Monterey, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Monterey map area, California. The vector data file is included in "Faults_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, ... |
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Faults--Offshore of Pacifica map area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Pacifica map area, California. The vector data file is included in "Faults_OffshorePacifica.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePacifica/data_catalog_OffshorePacifica.html. These data accompany the pamphlet and map sheets of Edwards, B.D., Phillips, E.L., Dartnell, P., Greene, H.G., Bretz, C.K., Kvitek, R.G., Hartwell, S.R., Johnson, S.Y., Cochrane, G.R., Dieter, B.E., Sliter, ... |
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Faults--Offshore of Point Conception Map Area, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Point Conception Map Area, California. The vector data file is included in "Faults_OffshorePointConception.zip," which is accessible from https://doi.org/10.5066/F7QN64XQ. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Hartwell, S.R., Golden, N.E., Kvitek, R.G., and Davenport, C.W. (S.Y. Johnson and S.A. Cochran, eds.), 2018, California State Waters Map Series� ... |
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Faults--Offshore of Point Reyes Map Area, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Point Reyes map area, California. The vector data file is included in "Faults_OffshorePointReyes.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePointReyes/data_catalog_OffshorePointReyes.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C ... |
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Faults--Offshore of Salt Point Map Area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Salt Point map area, California. The vector data file is included in "Faults_OffshoreSaltPoint.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSaltPoint/data_catalog_OffshoreSaltPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter ... |
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Faults--Offshore of San Francisco Map Area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore San Francisco map area, California. The vector data file is included in "Faults_OffshoreSanFrancisco.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Endris, C.A., Manson, M.W., Sliter, R.W ... |
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Faults--Offshore of San Gregorio Map Area, California
This part of SIM 3306 presents data for the faults for the geologic and geomorphic map of the Offshore of San Gregorio map area, California. The vector data file is included in "Faults_OffshoreSanGregorio.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSanGregorio/data_catalog_OffshoreSanGregorio.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Greene, H.G., Watt, J.T., Golden, N.E., Endris, C.A., Phillips, E.L., Hartwell, S.R., Johnson, S.Y., ... |
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Faults--Offshore of Santa Barbara, California
This part of DS 781 presents fault data for the Offshore of Santa Barbara map area, California. The vector data file is included in "Faults_OffshoreSantaBarbara.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSantaBarbara/data_catalog_OffshoreSantaBarbara.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Greene, H.G., Krigsman, L.M., Kvitek, R.G., Dieter, B.E., Endris, C.A., Seitz, G.G., ... |
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Faults--Offshore of Scott Creek map area, California
This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore of Scott Creek map area, California. The vector data file is included in "Faults_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., ... |
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Faults--Offshore of Tomales Point Map Area, California
This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Tomales Point map area, California. The vector data file is included in "Faults_OffshoreTomalesPoint.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreTomalesPoint/data_catalog_OffshoreTomalesPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Greene, H.G., Erdey, M.D., Cochrane, G.R., Watt, J.T., Kvitek, R.G., Manson, M ... |
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Faults--Offshore of Ventura, California
This part of SA 781 presents fault data for the Offshore of Ventura map area, California. The vector data file is included in "Faults_OffshoreVentura.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreVentura/data_catalog_OffshoreVentura.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Seitz, G.G., Gutierrez, C.I., Sliter, R.W., Erdey, M ... |
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Faults--Offshore Pigeon Point, California
This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore Pigeon Point map area, California. The vector data file is included in "Faults_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., ... |
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Faults--Offshore Refugio Beach, California
This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Refugio Beach map area, California. The vector data file is included in "Faults_OffshoreRefugioBeach.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreRefugioBeach/data_catalog_OffshoreRefugioBeach.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Krigsman, L.M., Dieter, B.E., Conrad, J.E., Greene, H ... |
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Faults--Offshore Santa Cruz, California
This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore of Santa Cruz map area, California. The vector data file is included in "Faults_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., ... |
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Faults—Point Sur to Point Arguello, California
This part of DS 781 presents data for the faults of the Point Sur to Point Arguello, California, region. The vector data file is included in the “Faults_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. Faults in the Point Sur to Point Arguello region are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, ... |
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Faults--Punta Gorda to Point Arena, California
This part of DS 781 presents data for the faults of the Punta Gorda to Point Arena, California, region. The vector data file is included in the "Faults_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. Faults in the Punta Gorda and Point Arena region are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, ... |
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Faults--Punta Gorda to Point Arena, California
This part of DS 781 presents data for the faults of the Punta Gorda to Point Arena, California, region. The vector data file is included in the "Faults_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. Faults in the Punta Gorda and Point Arena region are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, ... |
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FIIS_Breach_Shorelines.shp - Fire Island National Seashore Wilderness Breach Shoreline Data Collected from Fire Island, New York, October 2014 to October 2017
Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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FIIS_Breach_Shorelines.shp - Fire Island National Seashore Wilderness Breach Shoreline Data Collected from Fire Island, New York, October 2014 to September 2016
Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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FIIS_Shorelines_Oct2012_Oct2017.shp: Fire Island, NY pre- and post-storm shoreline data from October 2012 to October 2017
Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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Flooding extent polygons for modelled wave-driven water levels in Florida with and without projected coral reef degradation
This data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are 12 associated flood mask shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the degradation scenarios (Mean Elevation and Mean Erosion). |
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Folds--Drakes Bay and Vicinity Map Area, California
This part of DS 781 presents data of folds for the geologic and geomorphologic map of the Drakes Bay and Vicinity map area, California. The vector data file is included in "Folds_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., ... |
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Folds--Hueneme Canyon and Vicinity, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Folds_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B. ... |
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Folds--Monterey Canyon and Vicinity Map Area, California
This part of DS 781 presents fold data for the geologic and geomorphic map of the Monterey Canyon and Vicinity map area, California. The vector data file is included in "Folds_MontereyCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., ... |
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Folds--Offshore of Aptos Map Area, California
This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore Aptos map area, California. The vector data file is included in "Folds_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R ... |
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Folds--Offshore of Bodega Head Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Bodega Head map area, California. The vector data file is included in "Folds_OffshoreBodegaHead.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreBodegaHead/data_catalog_OffshoreBodegaHead.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., ... |
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Folds--Offshore of Bolinas Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Bolinas map area, California. The vector data file is included in "Folds_OffshoreBolinas.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreBolinas/data_catalog_OffshoreBolinas.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Manson, M.W., Sliter, R.W., Endris, C.A., Watt, J.T., Ross, ... |
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Folds--Offshore of Carpinteria, California
This part of DS 781 presents fold data for the Offshore of Carpinteria map area, California. The vector data file is included in "Folds_OffshoreCarpinteria.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreCarpinteria/data_catalog_OffshoreCarpinteria.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., ... |
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Folds--Offshore of Coal Oil Point, California
This part of DS 781 presents fold data for the Offshore of Coal Oil Point map area, California. The vector data file is included in "Folds_OffshoreCoalOilPoint.zip," which is accessible from https ://pubs.usgs.gov/ds/781/OffshoreCoalOilPoint/data_catalog_OffshoreCoalOilPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Dieter, B.E., Conrad, J.E., Lorenson, T.D., Krigsman, L.M., Greene, H.G., ... |
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Folds--Offshore of Fort Ross Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Folds_OffshoreFortRoss.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E ... |
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Folds--Offshore of Gaviota Map Area, California
This part of DS 781 presents fold data for the geologic and geomorphic map of the Offshore of Gaviota map area, California. The vector data file is included in "Folds_OffshoreGaviota.zip," which is accessible from https://doi.org/10.5066/F7TH8JWJ. In the offshore part of the map area, closely-spaced seismic-reflection profiles image many shallow, west-northwest striking folds that have variable geometry, length, amplitude, continuity, and wavelength. The two longest folds, the 17-km-long Molino anticline ... |
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Folds--Offshore of Half Moon Bay Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Half Moon Bay map area, California. The vector data file is included in "Folds_OffshoreHalfMoonBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreHalfMoonBay/data_catalog_OffshoreHalfMoonBay.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Greene, H.G., Johnson, S.Y., Golden, N.E., Hartwell, S.R., Dieter, B.E., Manson, M.W., Sliter, R.W., Ross, S.L., ... |
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Folds--Offshore of Monterey, California
This part of DS 781 presents fold data for the geologic and geomorphic map of the Offshore of Monterey map area, California. The vector data file is included in "Folds_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, ... |
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Folds--Offshore of Pacifica map area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Pacifica map area, California. The vector data file is included in "Folds_OffshorePacifica.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePacifica/data_catalog_OffshorePacifica.html. These data accompany the pamphlet and map sheets of Edwards, B.D., Phillips, E.L., Dartnell, P., Greene, H.G., Bretz, C.K., Kvitek, R.G., Hartwell, S.R., Johnson, S.Y., Cochrane, G.R., Dieter, B.E., Sliter, R ... |
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Folds--Offshore of Point Conception Map Area, California
This part of DS 781 presents fold data for the geologic and geomorphic map of the Offshore of Point Conception Map Area, California. The vector data file is included in "Folds_OffshorePointConception.zip," which is accessible from https://doi.org/10.5066/F7QN64XQ. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Hartwell, S.R., Golden, N.E., Kvitek, R.G., and Davenport, C.W. (S.Y. Johnson and S.A. Cochran, eds.), 2018, California State Waters Map Series� ... |
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Folds--Offshore of Point Reyes Map Area, California
This part of DS 781 presents fold data for the geologic and geomorphic map of the Offshore of Point Reyes map area, California. The vector data file is included in "Folds_OffshorePointReyes.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePointReyes/data_catalog_OffshorePointReyes.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A ... |
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Folds--Offshore of Salt Point Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Salt Point map area, California. The vector data file is included in "Folds_OffshoreSaltPoint.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSaltPoint/data_catalog_OffshoreSaltPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Erdey, M.D., Greene, H.G., Cochrane, G.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, ... |
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Folds--Offshore of San Francisco Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of San Francisco map area, California. The vector data file is included in "Folds_OffshoreSanFrancisco.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Endris, C.A., Manson, M.W., Sliter, R ... |
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Folds--Offshore of San Gregorio Map Area, California
This part of SIM 3306 presents data for the folds for the geologic and geomorphic map of the Offshore of San Gregorio map area, California. The vector data file is included in "Folds_OffshoreSanGregorio.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSanGregorio/data_catalog_OffshoreSanGregorio.html. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Greene, H.G., Watt, J.T., Golden, N.E., Endris, C.A., Phillips, E.L., Hartwell, S.R., Johnson, S.Y., Kvitek, ... |
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Folds--Offshore of Santa Barbara, California
This part of DS 781 presents fold data for the Offshore of Santa Barbara map area, California. The vector data file is included in "Folds_OffshoreSantaBarbara.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSantaBarbara/data_catalog_OffshoreSantaBarbara.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Greene, H.G., Krigsman, L.M., Kvitek, R.G., Dieter, B.E., Endris, C.A., Seitz, G.G., ... |
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Folds--Offshore of Scott Creek map area, California
This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore of Scott Creek map area, California. The vector data file is included in "Folds_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie ... |
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Folds--Offshore of Tomales Point Map Area, California
This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Tomales Point map area, California. The vector data file is included in "Folds_OffshoreTomalesPoint.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreTomalesPoint/data_catalog_OffshoreTomalesPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Greene, H.G., Erdey, M.D., Cochrane, G.R., Watt, J.T., Kvitek, R.G., Manson, M.W. ... |
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Folds--Offshore of Ventura, California
This part of DS 781 presents fold data for the Offshore of Ventura map area, California. The vector data file is included in "Folds_OffshoreVentura.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreVentura/data_catalog_OffshoreVentura.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Seitz, G.G., Gutierrez, C.I., Sliter, R.W., Erdey, M.D ... |
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Folds--Offshore Pigeon Point, California
This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore Pigeon Point map area, California. The vector data file is included in "Folds_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, ... |
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Folds--Offshore Refugio Beach, California
This part of DS 781 presents fold data for the geologic and geomorphic map of the Offshore of Refugio Beach map area, California. The vector data file is included in "Folds_OffshoreRefugioBeach.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreRefugioBeach/data_catalog_OffshoreRefugioBeach.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Krigsman, L.M., Dieter, B.E., Conrad, J.E., Greene, H.G ... |
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Folds--Offshore Santa Cruz, California
This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore of Santa Cruz map area, California. The vector data file is included in "Folds_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, ... |
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Folds—Point Sur to Point Arguello, California
This part of DS 781 presents data for the folds of the Point Sur to Point Arguello, California, region. The vector data file is included in the “Folds_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. Folds in the Point Sur to Point Arguello region are identified on seismic-reflection data based on warping and tilting of reflections. Folds were primarily mapped by interpretation of seismic reflection profile data collected by the U.S. Geological Survey between 2008 ... |
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FtHase_2023_MBES: High-resolution Geophysical and Imagery Data Collected in May 2023 Near Fort Hase, Marine Corps Base Hawaii
An Ellipsoidally Referenced Survey (ERS) using a Norbit Winghead multibeam echosounder, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Fort Hase Marine Corps Base Hawaii (MCBH), on the island of Oahu, May 4-12, 2023. This dataset, FtHase_2023_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid and the dataset FtHase_2023_MBES_Backscatter.zip includes the acoustic backscatter ... |
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Geochemical analysis of authigenic carbonates and chemosynthetic mussels at Atlantic Margin seeps (ver. 2.0, March 2019)
Isotopic analyses of authigenic carbonates and methanotrophic deep-sea mussels, Bathymodiolus sp., was performed on samples collected from seep fields in the Baltimore and Norfolk Canyons on the north Atlantic margin. Samples were collected using remotely operated underwater vehicles (ROVs) during three different research cruises in 2012, 2013, and 2015. Analyses were performed by several different laboratories, and the results are presented in spreadsheet format. |
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Geochemical analysis of seeps along the Queen Charlotte Fault
Geochemical analyses of authigenic carbonates, bivalves, and pore fluids were performed on samples collected from seep fields along the Queen Charlotte Fault, a right lateral transform boundary that separates the Pacific and North American tectonic plates. Samples were collected using grab samplers and piston cores, and were collected during three different research cruises in 2011, 2015, and 2017. |
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Geochemical data supporting investigation of solute and particle cycling and fluxes from two tidal wetlands on the south shore of Cape Cod, Massachusetts, 2012-19 (ver. 2.0, October 2022)
Assessment of geochemical cycling within tidal wetlands and measurement of fluxes of dissolved and particulate constituents between wetlands and coastal water bodies are critical to evaluating ecosystem function, service, and status. The U.S. Geological Survey and collaborators collected surface water and porewater geochemical data from a tidal wetland located on the eastern shore of Sage Lot Pond in Mashpee, Massachusetts, within the Waquoit Bay National Estuarine Research Reserve, between 2012 and 2019. ... |
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Geochemical data to characterize physical and chemical properties of the Cenote Bang, a component of the Ox Bel Ha cave network within the subterranean estuary coastal aquifer of the Yucatan Peninsula, from December 2013 to January 2016
Subterranean estuaries extend inland into density-stratified coastal carbonate aquifers that contain a surprising diversity of endemic animals (mostly crustaceans) within a highly oligotrophic environment. How complex ecosystems thrive in this globally-distributed, cryptic habitat (termed anchialine) is poorly understood. The northeastern margin of the Yucatan Peninsula contains over 250 km of mapped, diver-accessible caves passages where previous studies have suggested chemoautotrophic processes are the ... |
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Geochemistry of sediment and organic matter in drainages burned by the Altas and Nuns wildfires in October 2017 and of nearshore seabed sediment in north San Francisco Bay from March to April 2018
Fine-grained sediment was collected from the banks of Napa River, Sonoma Creek, and tributaries in March 2018 and from shallow nearshore areas of the northern reach of San Francisco Bay in April 2018. Bulk sediment was dated using activities of short-lived cosmogenic radionuclides (beryllium-7, cesium-137, and lead-210). Contents of potentially toxic metals and source-rock-indicative elements, including rare earth elements, were quantified in the fine fraction of sediment (particles less than 0.063 mm ... |
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Geometrically corrected image mosaic of 1936 aerial photographs of Rincon, Puerto Rico (mosaic_1936.tif)
The 8 km of shoreline from Punta Higuero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. |
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Geometrically corrected image mosaic of 1983 aerial photographs of Rincon, Puerto Rico (mosaic_1983.tif)
The 8 km of shoreline from Punta Higuero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. |
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Geometrically corrected image mosaic of 1987 aerial photograps of Rincon, Puerto Rico (mosiac_1987.tif)
The 8 km of shoreline from Punta Higuero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. |
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Geomorphic habitat units derived from 2009 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 11 September 2009 1 meter resolution NAIP aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (17 September 2009) for areas < MHHW and aerial lidar surveys (4-6 April 2009) for elevations > MHHW. |
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Geomorphic habitat units derived from 2011 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 03 September 2011* 0.3 meter resolution Microsoft/Digital Globe aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (25 August 2011) for areas < MHHW and aerial lidar surveys (13-15 April 2012) for elevations > MHHW. *Image date of 3-Sep-11 corrected in metadata. During product generation the imagery date was believed to be 8-25-2011, as reported by ... |
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Geomorphic habitat units derived from 2012 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 30 August 2012 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (28 August 2012) for areas < MHHW and aerial lidar surveys (17 October 2012) for elevations > MHHW. |
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Geomorphic habitat units derived from 2013 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 26 August 2013 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (16 September 2013) for areas < MHHW and aerial lidar surveys (17 October 2012) supplemented with NPS Elwha PlaneCam SfM photogrammetry data (19 September 2013) for elevations > MHHW. |
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Geomorphic habitat units derived from 2014 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 28 August 2014 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (5-8 September 2014) for areas < MHHW and aerial lidar surveys (7 November 2014) supplemented with NPS Elwha PlaneCam SfM photogrammetry data (30 September 2014) for elevations > MHHW. |
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Georeferenced National Ocean Service (NOS) Hydrographic Sheets for Grand Bay, Mississippi, and Surrounding Areas
Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal ... |
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Georeferenced scans of National Oceanic and Atmospheric Administration (NOAA) topographic sheets (T-Sheets) Collected Along the Fire Island and Great South Bay, New York, Coastline from 1834-1875
Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline web site (https://shoreline.noaa.gov/data/datasheets/t ... |
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Georeferenced Scans of National Oceanic and Atmospheric Administration (NOAA) T-Sheets Collected Along the New Jersey Coastline from 1839-1875
Historical shoreline surveys were conducted by the National Ocean Service (NOS), dating back to the early 1800s. The maps resulting from these surveys, often called t-sheets, provide a reference of historical shoreline position that can be compared to modern data to identify shoreline change. The t-sheets are stored at the National Archives and many have been scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline Web site (http://www.shoreline.noaa.gov ... |
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Geospatial Navigational Data Associated with Chirp Sub-Bottom Profiles Collected During USGS Field Activity Number 2023-325-FA in June and August 2023 from the Chandeleur Islands, Louisiana
As part of the 2022 Disaster Supplemental project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterize stratigraphy near the Chandeleur Islands, Louisiana (LA) in June and August 2023. The purpose of this study was to conduct a morphologic and geologic assessment of the impacts of the 2020 and 2021 hurricane seasons within part of the Breton National ... |
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Grain-size analysis data from sediment samples in support of oceanographic and water-quality measurements at Thompsons Beach and Stone Harbor, New Jersey, collected in September 2018 and March 2022
In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science ... |
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Grain-Size Analysis Data from Sediment Samples in Support of Oceanographic and Water-Quality Measurements in the Nearshore Zone of Matanzas Inlet, Florida, 2018
The interactions of waves and currents near an inlet influence sediment and alter sea-floor bedforms, especially during winter storms. As part of the Cross-Shore and Inlets Processes project to improve our understanding of cross-shore processes that control sediment budgets, the U.S. Geological Survey deployed instrumented platforms at two sites near Matanzas Inlet between January 24 and April 13, 2018. Matanzas Inlet is a natural, unmaintained inlet on the Florida Atlantic coast that is well suited for ... |
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Grain-size analysis data of sediment samples from the beach and nearshore environments at the Pea Island National Wildlife Refuge DUNEX site, North Carolina in 2021
These data provide grain-size measurements from sediment samples collected as part of the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. DUNEX is a multi-agency, academic, and non-governmental organization collaborative community experiment designed to study nearshore coastal processes during storm events. USGS participation in DUNEX will contribute new measurements and models that will increase our understanding of storm impacts to coastal environments, ... |
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Grain-size analysis of sediment cores collected in 2009 offshore from Palos Verdes, California
This part of the data release includes grain-size analysis of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), ... |
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Grain size and bulk density from Little Holland Tract and Liberty Island, Sacramento-San Joaquin Delta, California, 2015 to 2019 (ver. 3.0, April 2023)
Grain size distribution and bulk density are reported for sediment samples from two flooded agricultural tracts, Little Holland Tract and Liberty Island, in the Sacramento-San Joaquin Delta, California. Samples were repeatedly collected at 8 sites using a Ponar grab or push core samplers during 19 visits to the study area from 2015 to 2019. The long-term time series data collection stations (sites LWA, HVB, HWC, and LVB) were sampled on almost every field survey, and the remaining sites were sampled 6 or ... |
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Grain size and bulk density of sediment cores from Little Holland Tract and Liberty Island, Sacramento-San Joaquin Delta, California, 2014
Grain size distribution and bulk density are reported for sediment push cores from two flooded agricultural tracts, Little Holland Tract and Liberty Island, in the Sacramento-San Joaquin Delta, California. Push core samples were collected from 14 sites by the U.S Geological Survey in August, 2014. Each core was analyzed at multiple depths to investigate variations in particle sizes with depth below the sediment surface. The same sites were sampled again in 2016 (https://www.sciencebase.gov/catalog/item ... |
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Grain size and bulk density of sediment cores from Little Holland Tract and Liberty Island, Sacramento-San Joaquin Delta, California, 2016
Grain size distribution and bulk density are reported for sediment push cores from two flooded agricultural tracts, Little Holland Tract and Liberty Island, in the Sacramento-San Joaquin Delta, California. Push core samples were collected from 17 sites by the U.S. Geological Survey in June 2016. Each core was analyzed at multiple depths to investigate variations in particle sizes with depth below the sediment surface. The same sites were sampled previously in 2014 (https://www.sciencebase.gov/catalog/item ... |
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Grain size, bulk density, and carbon content of sediment collected from Whale's Tail South marsh and adjacent bay floor, South San Francisco Bay, California, 2021-2022
Sediment samples were collected on and adjacent to the Whale's Tail South marsh. Short push-cores of bed sediment were collected in South San Francisco Bay adjacent to Whale's Tail South marsh on five days from June through August 2021 and 3 days from November 2021 to January 2022. Additional samples were taken from sediment deposited on ceramic tiles attached to the marsh surface and from rip-up clasts deposited on the marsh edge. Samples were analyzed for sediment properties including bulk density, ... |
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Grain size, bulk density, and organic carbon of sediment cores from San Pablo Bay and Grizzly Bay, California, 2019
Bed sediment samples were collected in San Pablo Bay and Grizzly Bays on eight days from June through November 2019, to analyze for sediment properties including bulk density, particle size distribution, and percent organic carbon. Sediment samples were collected from a small vessel near pre-established USGS instrument moorings using a Gomex box corer that was subsampled with three push cores (37 mm in diameter) per Gomex core. Six subsamples were collected from the top 5 centimeters (cm) of each push ... |
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Grain size, bulk density, and organic carbon of sediment cores from San Pablo Bay and Grizzly Bay, California, 2020
Bed sediment samples were collected in San Pablo Bay and Grizzly Bays on eight days from January through September 2020, to analyze for sediment properties including bulk density, particle size distribution, and percent organic carbon. Sediment samples were collected from a small vessel near pre-established USGS instrument moorings using a Gomex box corer that was subsampled with three push cores (37 mm in diameter) per Gomex core. Six subsamples were collected from the top 5 centimeters (cm) of each push ... |
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Grain size, bulk density, and organic carbon of sediment cores from three locations in the Sacramento-San Joaquin Delta, California, 2017 to 2018
Bed sediment samples were collected in Lindsey Slough in April 2017, and Middle River and the Mokelumne River in March 2018, to analyze for sediment properties, including bulk density, particle size distribution, and percent organic carbon. Sediment samples were collected within the vegetation with push corers deployed from a small vessel, and in the unvegetated channel with a Gomex box corer, which was subsampled with three push cores per Gomex core. Data are provided in a comma-delimited values ... |
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Grain-size data from vibracores collected in 2014 from Barnegat Bay, New Jersey
In response to the 2010 Governor’s Action Plan to clean up the Barnegat Bay–Little Egg Harbor (BBLEH) estuary in New Jersey, the U.S. Geological Survey (USGS) partnered with the New Jersey Department of Environmental Protection in 2011 to begin a multidisciplinary research project to understand the physical controls on water quality in the bay. Between 2011 and 2013, USGS scientists mapped the geological and morphological characteristics of the seafloor of the BBLEH estuary using a suite of geophysical ... |
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Grain-size data of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes grain-size data of sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Grain-size distributions from San Pablo Bay, California, 2011 to 2012
Sediment cores were collected from San Pablo Bay, in the Sacramento-San Joaquin Delta in California by the U.S. Geological Survey Pacific Coastal and Marine Science Center (PCMSC) during multiple surveys from 2011 to 2012. The cores were analyzed for grain-size distributions at the PCMSC sediment lab. |
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Grain size of bed sediment surface samples from south San Francisco Bay, California, summer 2020
Bed sediment samples were collected in south San Francisco Bay on two days in July 2020 to analyze for sediment grain size distributions. Sediment samples were collected from the R/V Snavely near pre-established U.S. Geological Survey instrument moorings using a Gomex or Ponar box corer that was subsampled by scraping the top 0.5 cm of the core. Data are provided in a comma-delimited values spreadsheet. |
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GrandBay_2010_Shoreline.shp - Grand Bay, Mississippi/Alabama, Shoreline Data Derived from 2010 Aerial Imagery
GrandBay_2010_Shoreline.zip features a digitized historical shoreline for the Grand Bay, Mississippi (MS) coastline (Pascagoula, MS to Point aux Pins, Alabama [AL]) derived from 2010 aerial imagery. Imagery of the Mississippi and Alabama coastlines was acquired from the National Agriculture Imagery Program (NAIP) and the city of Mobile, AL. Using ArcMap 10.3.1, the imagery was used to delineate and digitize the historical shoreline as either the Wet Dry Line (WDL) along sandy beaches or the vegetation edge ... |
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GrandBay_2012_Shoreline.shp - Grand Bay, Mississippi/Alabama, Shoreline Data Derived from 2012 Aerial Imagery
GrandBay_2012_Shoreline.zip features a digitized historical shoreline for the Grand Bay, Mississippi (MS) coastline (Pascagoula, MS to Bayou La Fourche Bay, Alabama [AL]) derived from 2012 aerial imagery. Imagery of the Mississippi and Alabama coastlines was acquired from the National Agriculture Imagery Program (NAIP). Using ArcMap 10.3.1, the imagery was used to delineate and digitize a coarse historical shoreline as either proximal Wet Dry Line along sandy beaches or proximal vegetation edge along the ... |
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GrandBayModel_InputBathymetry: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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GrandBay_ValidationPeriod_Wave_WaterLevel: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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Graphical representations of data from sediment cores collected in 2009 offshore from Palos Verdes, California
This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse ... |
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Graphical representations of data from sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California
This part of the data release includes graphical representation (figures) of data of sediment cores collected in 2014 in Monterey Canyon. It is one of five files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in ... |
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Grid File of Historical Bathymetric Soundings for Mississippi and Alabama Derived from National Ocean Service (NOS) Hydrographic Sheets
Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal ... |
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Ground Penetrating Radar and Global Positioning System Data Collected from Central Florida Gulf Coast Barrier Islands, Florida, February-March 2021
A morphologically diverse and dynamic group of barrier islands along the Central Florida (FL) Gulf Coast (CFGC) form a 75-kilometer-long chain stretching from Anclote Key in the north to Egmont Key in the south. In 2021, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted ground penetrating radar (GPR) surveys on barrier islands located along the CFGC, in Pinellas County, FL. This study investigated the past evolution of the CFGC from field ... |
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Ground Penetrating Radar and Global Positioning System Data Collected from Fire Island, New York, March-April 2021
Fire Island, New York (NY) is a 50-kilometer (km) long barrier island system fronting the southern coast of Long Island, NY with relatively complex geology. In 2016, the U.S. Geological Survey (USGS) conducted ground penetrating radar (GPR) surveys and sediment sampling at Fire Island to characterize and quantify spatial variability in the subaerial geology (Forde and others, 2018; Buster and others, 2018). These surveys, in combination with historical data, allowed for a preliminary reconstruction of the ... |
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Ground-Penetrating Radar Data and Differential Global Positioning System Data Collected from Long Beach Island, New Jersey, April 2015
Scientists from the United States Geological Survey, St. Petersburg Coastal and Marine Science Center, U.S. Geological Survey Pacific Coastal and Marine Science Center, and students from the University of Hawaii at Manoa collected sediment cores, sediment surface grab samples, ground-penetrating radar (GPR) and Differential Global Positioning System (DGPS) data from within the Edwin B. Forsythe National Wildlife Refuge-Holgate Unit located on the southern end of Long Beach Island, New Jersey, in April 2015 ... |
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Ground Penetrating Radar (GPR) Profile Trace Data Collected from Dauphin Island, Alabama in April 2013
From April 13-20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama, as part of field activity number 13BIM01. This dataset, Ground Penetrating Radar (GPR) Profile Trace Data Collected from Dauphin Island, Alabama in April 2013, contains the unprocessed, raw profile trace data obtained during this survey. |
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Ground temperature time-series data collected in 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, ... |
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GULF - Coastal Vulnerability to Sea-Level Rise: U.S. Gulf Coast
The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where ... |
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Half interpercentile range (half of the difference between the 16th and 84th percentiles) of wave-current bottom shear stress in the Middle Atlantic Bight for May, 2010 - May, 2011 (MAB_hIPR.SHP)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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HATTERAS_INDEX - Hatteras Island, North Carolina (geographic, WGS84)
The shoreline of Cape Hatteras, North Carolina, is experiencing long-term coastal erosion. In order to better understand and monitor the changing coastline, historical aerial imagery is used to map shoreline change. For the area of Hatteras Island from Cape Point to Oregon Inlet, fourteen aerial datasets from 1978-2002 were scanned and georeferenced for use in a Geographic Information System (GIS). Shoreline positions (high water line) were digitized from georeferenced imagery. The shoreline vectors were ... |
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hawaii_ero - Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_oha - Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii
Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii |
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hawaii_sea - Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_stm - Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_tsu - Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_wav - High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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Healy_Continuous
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months, when ice melt is at its greatest extent. However, few comprehensive datasets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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Healy_Continuous
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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Healy_Discrete
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months, when ice melt is at its greatest extent. However, few comprehensive datasets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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Healy_Discrete
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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High-resolution shoreline change measurements (1997-2005) from Corolla to Cape Hatteras, NC (swash_shorelines.shp, geographic, WGS 84)
The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and ... |
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Hindcast (1981-2010) and projected (2011-2100) coastal storm events, including duration, wave conditions, and storm surges in the vicinity of Arey Lagoon and Barter Island, Alaska
Numerically modeled ocean storm conditions of hindcast (1981-2010) and projected (2011-2100) storm events in the nearshore region of Arey Lagoon, Alaska. Storms were identified from time-series of dynamically downscaled deep-water wave conditions using WaveWatch3 (WW3) and nearshore storm surges using the Deltares Delft3D model. A storm was defined as having offshore water wave heights >= 2 meters (m) and storm surges >=0 m. The data in this file provide a listing of individual storm dates, storm duration, ... |
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Historical bathymetry soundings between 1916 and 1920 around the Mississippi and Alabama barrier islands
In order to characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi and Alabama (MSAL) barrier islands. One goal of this work was to create a time-series of bathymetric change maps around the islands between 1916 and 2016. |
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Historical coastal bluff edge positions at Barter Island, Alaska for the years spanning 1950 to 2020
This dataset includes one vector shapefile delineating the position of the top edge of the coastal permafrost bluffs at Barter Island, Alaska spanning seven decades, between the years of 1950 and 2020. Bluff-edge positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the bluff edge through time. These data were used to calculate rates of change every 10 meters alongshore using the ... |
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Historical Shoreline for New Jersey (1971 to 1978): Vector Digital Data
New_Jersey_1971_78_Digitized_Shoreline.zip features a digitized historic shoreline for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1971 to 1978. Imagery of the New Jersey coastline was acquired from the New Jersey Geographic Information Network (NJGIN) as two images: “1970 NJDEP Wetlands Basemap” (1971-78) and the “1977 Tidelands Basemaps” (1977-78). These images are available as a web mapping service (WMS) through the NJGIN website (https://njgin.state.nj.us/NJ_NJGINExplorer ... |
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Historical shoreline positions at Barter Island, Alaska for the years spanning 1947 to 2020
This dataset includes one vector shapefile delineating the position of the shorelines at Barter Island, Alaska spanning seven decades, between the years 1947 and 2020. Shoreline positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the shoreline through time. These data were used to calculate rates of change every 10 meters alongshore using the Digital Shoreline Analysis System ... |
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Historical shoreline positions for the coast of MA, from 1844 - 2014.
The Massachusetts Office of Coastal Zone Management (MA CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (1800's-1989) shoreline positions and shoreline change maps. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- ... |
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Historical Shorelines for Puerto Rico from 1901 to 1987
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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Historical shoreline vectors for barrier islands and spits along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019
A suite of morphological metrics were derived from existing shoreline and elevation datasets for barrier islands and spits located along the north-slope coast of Alaska between Cape Beaufort and the U.S.-Canadian border. This dataset includes shoreline vectors, including data source and acquisition date, from five time periods: 1950s, 1980s, 2000s, 2010s, and 2020s. The shoreline vectors were combined to produce polygons upon which the metrics were calculated. |
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Historic shoreline positions for Rincon, Puerto Rico 1936-2006 (shorelines.shp)
The 8 km of shoreline from Punta Higüero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. Shoreline vectors represent the high water line at the time of the survey. |
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HLY1001_Averaged
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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HLY1002_Averaged
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months, when ice melt is at its greatest extent. However, few comprehensive datasets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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Holocene fluvial and estuarine (Qfe) and nearshore marine (Qmn) sediment thickness offshore of western and southern Martha's Vineyard and north of Nantucket, Massachusetts
Geologic, sediment texture, and physiographic zone maps characterize the sea floor south and west of Martha's Vineyard and north of Nantucket, Massachusetts. These maps were derived from interpretations of seismic-reflection profiles, high-resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and surficial sediment samples. The interpretation of the seismic stratigraphy and mapping of glacial and Holocene marine units provided a foundation on which the surficial maps were created. This ... |
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Hurricane Delta Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Delta, which made landfall in the U.S. on October 9, 2020. |
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Hurricane Florence Assessment of Potential Coastal Change Impacts: NHC Advisory 57, 1100 AM EDT THU SEP 13 2018
This dataset defines storm-induced coastal erosion hazards for the Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Florence in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune ... |
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Hurricane Florence Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from North Carolina to Virginia and attributed to coastal processes during [Atlantic Basin] Hurricane Florence, which made landfall in the U.S. on September 14, 2018. |
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Hurricane Harvey Assessment of Potential Coastal Change Impacts: NHC Advisory 020, 700 AM CDT FRI AUG 25 2017
This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Harvey in August 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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Hurricane Irma Assessment of Potential Coastal Change Impacts: NHC Advisory 41, 800 AM EDT SAT SEPT 9 2017
This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia and South Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Irma in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of ... |
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Hurricane Irma Overwash Extents (version 2.0, 20210916)
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Irma, which made landfall in the U.S. on September 9, 2017. |
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Hurricane Joaquin Assessment of Potential Coastal Change Impacts: NHC Advisory 27, 0800 AM EDT SUN OCT 04 2015
This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island and Massachusetts coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Joaquin in October 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune ... |
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Hurricane Laura Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Laura, which made landfall in the U.S. on August 27, 2020. |
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Hurricane Maria Assessment of Potential Coastal Change Impacts: NHC Advisory 41, 0800 AM EDT TUE SEPT 26 2017
This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland and Delaware coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Maria in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ... |
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Hurricane Matthew Assessment of Potential Coastal Change Impacts: NHC Advisory 037, 800 AM EDT FRI OCT 07 2016
This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia, South Carolina and North Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Matthew in October 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of ... |
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Hurricane Matthew Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from Florida to North Carolina and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2016. |
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Hurricane Matthew Overwash Extents (version 2.0, 20210916)
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida, Georgia, North Carolina,and South Carolina coasts and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2018. |
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Hurricane Michael Assessment of Potential Coastal Change Impacts: NHC Advisory 15, 0400 AM CDT WED OCT 10 2018
This dataset defines storm-induced coastal erosion hazards for the Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Michael in October 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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Hurricane Michael Overwash Extents (version 2.0, 20210916)
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Michael, which made landfall in the U.S. on October 10, 2018. |
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Hurricane Nate Assessment of Potential Coastal Change Impacts: NHC Advisory 12, 0800 AM EDT SAT OCT 07 2017
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Nate in October 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three ... |
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Hurricane Sally Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida and Alabama coast and attributed to coastal processes during [Atlantic Basin] Hurricane Sally, which made landfall in the U.S. on September 16, 2020. |
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Hurricane Sandy Assessment of Potential Coastal Change Impacts: NHC Advisory 29, 1100 AM EDT MON OCT 29 2012
This dataset defines hurricane-induced coastal erosion hazards for the Delaware, Maryland, New Jersey, New York, and Virginia coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Sandy in October 2012. Hurricane-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the ... |
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Hurricane Zeta Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Zeta, which made landfall in the U.S. on October 28, 2020. |
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HyCReWW database: A hybrid coral reef wave and water level metamodel
We developed the HyCReWW metamodel to predict wave run-up under a wide range of coral reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset and Radial Basis Functions implemented in Matlab were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain ... |
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Hydrodynamic and sediment transport data from San Pablo Bay (northern San Francisco Bay), 2011-2012
The U.S. Geological Survey Pacific Coastal and Marine Science Center collected data to investigate sediment dynamics in the shallows of San Pablo Bay in two deployments: February to March 2011 (ITX11) and May to June 2012 (ITX12). This data release includes time-series data and grain-size distributions from sediment grabs collected during the deployments. During each deployment, time series of current velocity, water depth, and turbidity were collected at several stations in the shallows, and one station in ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Locations of convergences in the maximum alongshore current
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Locations of decelerations in the direction of flow in the maximum alongshore current
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: peak wave period
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Ratio of the wave- and current-induced shear stress to the critical value for oil-tar balls and sediment mobilization over a tidal cycle
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Ratio of the wave- and current-induced shear stress to the critical value for oil-tar balls and sediment mobilization weighted by probability of wave scenario occurrence
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Ratio of wave- and current-induced shear stress to critical values for oil-sand ball and sediment mobilization
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Significant wave height
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Surf-zone integrated alongshore potential flux for oil-sand balls
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Surf-zone integrated alongshore potential flux for oil-sand balls of varying sizes weighted by probability of wave scenario occurrence
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: wave direction
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and sediment transport model of San Francisco Bay, California, Nov-Dec 2014
A three-dimensional hydrodynamic and sediment transport model of San Pablo and Suisun Bays was constructed using the Delft3D4 (D3D) modeling suite (Deltares, 2021a) to simulate water levels, flow, waves, and suspended sediment for time period of Nov 1 to Dec 31, 2014. This data release describes the construction and validation of the model application and provides input files suitable to run the model on D3D software version 4.04.01. |
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Hydrodynamic and sediment transport model of the mouth of the Columbia River, Washington and Oregon, 2020-2021
A three-dimensional hydrodynamic and sediment transport model application of the mouth of the Columbia River (MCR) was constructed using the Delft3D4 (D3D) modeling suite (Deltares, 2021) to simulate water levels, flow, waves, and sediment transport for time period of September 22, 2020, to March 10, 2021. The model was used to predict the dispersal of sediment from a submerged, nearshore berm composed of sediment that was dredged from the entrance to the MCR navigation channel and placed on the northern ... |
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Hydrodynamic and sediment transport tsunami models at the Salmon River estuary, Oregon
This portion of the USGS data release describes the Delft3D-FLOW model application for propagating simulated tsunamis from 15 hypothetical earthquake sources of the Cascadia Subduction Zone through a series of nested grids to modeling tsunami sediment transport in the Salmon River estuary, OR. Input files necessary to run the Delft3D-FLOW model are provided. The model application was constructed using Delft3D-FLOW. Zip files containing model setup data are provided for each of the nested hydrodynamic grids ... |
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Hydrodynamic model of the lower Columbia River, Oregon and Washington, 2017-2020
A three-dimensional hydrodynamic model of the lower Columbia River (LCR) was constructed using the Delft3D Flexible Mesh (DFM) modeling suite to simulate water levels, flow, and seabed stresses for time period of January 1, 2017 to April 20, 2020. This data release describes the construction and validation of the model application and provides input files suitable to run the model on Delft3D Flexible Mesh software version 2021.01. |
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Hydrodynamic model of the San Francisco Bay and Delta, California
A one- and two-dimensional hydrodynamic model of the San Francisco Bay and Delta was constructed using the Delft3D Flexible Mesh Suite (Delft3D FM; Kernkamp and others, 2011; https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/) to simulate still water levels. Required model input files are provided to run the model for the time period from October 1, 2018, to April 30, 2019. This data release describes the construction and validation of the model application and provides input files suitable to ... |
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Hydrological and chemical records from the flooded Ox Bel Ha cave system in the Yucatan Peninsula, Quintana Roo, from August 2014 to January 2015
Natural cave passages penetrating coastal aquifers in the Yucatan Peninsula (Quintana Roo, Mexico) were accessed to investigate how regional meteorology and hydrology control dissolved organic carbon and methane dynamics in karst subterranean estuaries, the region of aquifers where fresh and saline waters mix. Three field trips were carried out in December 2013, August 2014, and January 2015 to obtain 1) physicochemical and 2) geochemical data from the water column and 3) temporal records of water chemistry ... |
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iCoast - Did the Coast Change? Crowd-sourced Coastal Classifications
On October 29, 2012, Hurricane Sandy made landfall as a post-tropical storm near Brigantine, New Jersey, with sustained winds of 70 knots (80 miles per hour) and tropical-storm-force winds extending 870 nautical miles in diameter (Blake and others, 2013). The effects of Hurricane Sandy’s winds and storm surge included erosion of the beaches and dunes as well as breaching of barrier islands in both natural and heavily developed areas of the coast (Spokin et. al., 2014). On November 4-6, 2012, the U.S. ... |
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Idealized Antecedent Topography Sensitivity Study: Initial Baseline and Modified Profiles Modeled with XBeach
Antecedent topography is an important aspect of coastal morphology when studying and forecasting coastal change hazards. The uncertainty in morphologic response of storm-impact models and their use in short-term hazard forecasting and decadal forecasting is important to account for when considering a coupled model framework. Mickey and others (2020) provided a methodology to investigate uncertainty of profile response within the storm impact model, XBeach, related to varying antecedent topographies. A ... |
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Imagery from USGS CoastCam deployed at Madeira Beach, Florida
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. The images included in this data release were collected from January 21, 2017, to December 31, 2017. The camera is part of a U.S. Geological Survey (USGS) research project to study the beach and nearshore environment. USGS researchers analyzed the imagery collected ... |
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Inferred hydrodynamic residence time in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Information on sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset presents core information such as core IDs, section numbers, lengths, depth intervals, and locations from sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. An inventory of core section CT, MSCL, and photograph scan files available in this data release are listed here. |
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Initial and Future Marsh Productivity Conditions Under Three Sea-Level Rise Scenarios (Intermediate-Low, Intermediate, and Intermediate-High) from 2020 to 2100 in the Apalachicola-Big-Bend Region
Using the Hydro-MEM (Hydrodynamic-Marsh Equilibrium Model) (Alizad and others, 2016a; 2016b), the wetlands system within the Apalachicola-Big-Bend (ABB) region of Florida (FL) was assessed using initial and three sea-level rise (SLR) scenarios from the National Oceanic and Atmospheric Administration (NOAA) (Sweet and others, 2017). The initial (init) scenario represents the present conditions in the year 2020. The intermediate-low (int-low) scenario projects 50 centimeters (cm) of SLR by 2100, the ... |
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Initial and Future Marsh Vegetation Conditions Under Three Sea-Level Rise Scenarios (Intermediate-Low, Intermediate, and Intermediate-High) from 2020 to 2100 in the Apalachicola-Big-Bend Region
Using the Hydro-MEM (Hydrodynamic-Marsh Equilibrium Model) (Alizad and others, 2016a; 2016b), the wetlands system within the Apalachicola-Big-Bend (ABB) region of Florida (FL) was assessed using initial and three sea-level rise (SLR) scenarios from the National Oceanic and Atmospheric Administration (NOAA) (Sweet and others, 2017). The initial (init) scenario represents the present conditions in the year 2020. The intermediate-low (int-low) scenario projects 50 centimeters (cm) of SLR by 2100, the ... |
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In situ seafloor images from the lower Columbia River, Washington and Oregon, 2021
In situ seafloor images were acquired at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, with an underwater camera system between June 5 and June 8, 2021. Between 248 and 427 digital images of the sediment surface were collected at each site with an underwater camera system that was repeatedly lowered to the seabed along a series of 1 km-long transects oriented along the main navigation channel and spaced about 60 m apart. The camera consisted of a FLIR Blackfly BFS-PGE ... |
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Interpolated digital elevation model (DEM) of the nearshore around Ship, Horn, and Petit Bois Islands, Mississippi: 1916 to 1920
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and ... |
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Interpolated digital elevation model (DEM) of the nearshore around Ship, Horn, and Petit Bois Islands, Mississippi: 2008 to 2009
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and ... |
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Interpolated digital elevation model (DEM) of the nearshore around Ship, Horn, and Petit Bois Islands, Mississippi: 2016
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and ... |
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Interpretation of sea floor geologic units for offshore of western and southern Martha's Vineyard and north of Nantucket, Massachusetts
Geologic, sediment texture, and physiographic zone maps characterize the sea floor south and west of Martha's Vineyard and north of Nantucket, Massachusetts. These maps were derived from interpretations of seismic-reflection profiles, high-resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and surficial sediment samples. The interpretation of the seismic stratigraphy and mapping of glacial and Holocene marine units provided a foundation on which the surficial maps were created. This ... |
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Interpretation of Sedimentary Environments from National Oceanic and Atmospheric Administration (NOAA) Survey H11922 West of Gay Head, Massachusetts, in Eastern Rhode Island Sound (H11922_SEDENV.SHP, Geographic, WGS84)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA), is producing detailed geologic maps of the coastal sea floor. Bathymetry, originally collected by NOAA for charting purposes, provides a fundamental framework for research and management activities off southern New England, shows the composition and terrain of the seabed, and provides information on sediment transport and benthic habitat. During July-August 2008 NOAA completed hydrographic ... |
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Interpretation of Sedimentary Environments from National Oceanic and Atmospheric Administration (NOAA) Survey H12007 in the Vicinity of Cross Rip Channel in Nantucket Sound, Offshore Southeastern Massachusetts (H12007_SEDENV.SHP, Geographic, WGS84)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA), is producing detailed geologic maps of the coastal sea floor. Bathymetry, originally collected by NOAA for charting purposes, provides a fundamental framework for research and management activities off southern New England, shows the character and terrain of the seabed, and provides information on sediment transport and benthic habitat. During April-May 2009 NOAA completed hydrographic survey ... |
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Interpretation of Sedimentary Environments from National Oceanic and Atmospheric Administration (NOAA) Survey H12298 in Block Island Sound (Geographic, WGS 84, H12298SEDENV.SHP)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA), is producing detailed geologic maps of the coastal sea floor. Bathymetric and sidescan-sonar data, originally collected by NOAA for charting purposes, provide a framework for research and management activities along western Block Island Sound, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. During June 2013, bottom photographs ... |
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Interpretation of Sedimentary Environments from National Oceanic and Atmospheric Administration (NOAA) Survey H12299 in Block Island Sound (Geographic, WGS 84, H12299SEDENV.SHP)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA), is producing detailed geologic maps of the coastal sea floor. Bathymetric data, originally collected by NOAA for charting purposes, provide a fundamental framework for research and management activities along this part of Block Island Sound, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. During June 2013, bottom photographs and ... |
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Interpretation of Sedimentary Environments from National Oceanic and Atmospheric Administration (NOAA) Survey H12324 in Narragansett Bay (Geographic, WGS 84)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA), is producing detailed geologic maps of the coastal sea floor. Bathymetric data, originally collected by NOAA for charting purposes, provide a framework for research and management activities along southern Narragansett Bay, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. During September 2014, bottom photographs and surficial ... |
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Interpretation of Sedimentary Environments Within the Area of National Oceanic and Atmospheric Administration (NOAA) Survey H12013 Offshore in Northeastern Long Island Sound (Geographic, WGS84, H12012_SEDENV.SHP)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Connecticut Department of Energy and Environmental Protection (CT DEEP), has produced detailed geologic maps of the coastal sea floor. Bathymetry, originally collected by NOAA for charting purposes, provides a fundamental framework for research and management activities in Long Island Sound, shows the terrain of the seabed, and provides information on sediment transport and benthic ... |
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Interpretation of the New York Bight Fault Zone on the inner-continental shelf within the New York Bight, derived from seismic data collected by the U.S. Geological Survey, 1995 - 1999 (Esri polyline shapefile, Geographic, WGS84)
The New York Bight fault (Hutchinson, 1984) was clearly evident within the high-resolution seismic records acquired with a CHIRP, boomer, and 15 cubic inch water gun systems. This fault was mapped from these data. Thus, yeilding a more complete picture of the inner-shelf geologic framework of the area. |
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Interpretation of the Sedimentary Environments of National Oceanic and Atmospheric Administration (NOAA) H11320 Sidescan Sonar Mosaic in Rhode Island Sound (H11320ENVIRONS)
The U.S. Geological Survey (USGS) is working cooperatively with the National Oceanic and Atmospheric Administration (NOAA) to interpret the surficial geology in estuaries along the coast of the northeastern United States. The purpose of our present study is to define the sea floor morphology and sedimentary environments in an area of Rhode Island Sound using sidescan sonar imagery, multibeam bathymetry, and seismic records. The mosaic, bathymetry, and their interpretations serve many purposes, including: (1 ... |
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Interpretation Showing the Distribution of Sea-Floor Sedimentary Environments in Quicks Hole, MA (H11076_SEDENV.SHP, Geographic)
The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), is producing detailed geologic maps of the coastal sea floor. Imagery, originally collected by NOAA for charting purposes, provide a fundamental framework for research and management activities along this part of the Massachusetts coastline, show the composition and terrain of the seabed, and provide information on sediment ... |
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Intersects for coastal region around Boston, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for coastal region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for coastal region of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for coastal region of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for coastal region of South Carolina generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Intersects for coastal region of Virginia generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Intersects for coastal region south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for the Cape Cod Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for the coastal region of South Carolina generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Intersects for the coastal region of Virginia generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Intersects for the coast south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for the Florida east coast (FLec) coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida east coast (FLec) coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida panhandle (FLph) coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida panhandle (FLph) coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida west coast (FLwc) coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida west coast (FLwc) coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Georgia coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Georgia coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for the region of the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for the southern coastal region of Cape Cod Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for the southern coast of Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intrinsic and Extrinsic Calibration Data From USGS CoastCam deployed at Madeira Beach, Florida
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research project to study the beach and ... |
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Inventory of Managed Coastal Wetlands in Delaware Bay and Delaware's Inland Bays
This data release contains areas within Delaware Bay and Delaware Inland Bays that are within tidal elevations, as determined by the Highest Astronomical Tide (HAT), but that are classified as non-tidal or managed wetlands by the National Wetlands Inventory (NWI) or as non-estuarine by the 2016 Coastal Change Analysis Program (C-CAP) land cover dataset. These areas have been assigned the classification codes of NWI, where available, and C-CAP. These data are based on a 5m resolution elevation raster from ... |
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July 2010 Dauphin Island USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Dauphin Island U.S. ... |
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June 2008 Alabama and Florida USGS EAARL Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the June 2008 Louisiana, ... |
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KauaiE_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along East Kauai, Hawaii (Papaa to Nawiliwili)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiE_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai east region from Papaa to Nawiliwili, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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kauai_ero - Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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KauaiE_shorelines - Shorelines of the eastern coastal region of Kauai, Hawaii, from Papaa to Nawiliwili, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiE_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Kauai east region from Papaa to Nawiliwili, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiN_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along North Kauai, Hawaii (Haena to Moloaa)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiN_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai north region from Haena to Moloaa, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiN_shorelines - Shorelines of the northern coastal region of Kauai, Hawaii, from Haena to Moloaa, used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiN_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with weighted linear regression short-term rate calculations for the Kauai north region from Haena to Moloaa, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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kauai_oha - Overall Hazard Assessment in the coastal zone of Kauai, Hawaii
Overall Hazard Assessment in the coastal zone of Kauai, Hawaii |
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KauaiS_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along South Kauai, Hawaii (Waimea to Kipu Kai)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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kauai_sea - Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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KauaiS_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai south region from Waimea to Kipu Kai, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiS_shorelines - Shorelines of the southern coastal region of Kauai, Hawaii, from Waimea to Kipu Kai, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiS_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Kauai south region from Waimea to Kipu Kai, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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kauai_stm - Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_tsu - Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_wav - High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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KauaiW_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along West Kauai, Hawaii (Oomano to Polihale)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiW_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai west region from Oomano to Polihale, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiW_shorelines - Shorelines of the western coastal region of Kauai, Hawaii, from Oomano to Polihale, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiW_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Kauai west region from Oomano to Polihale, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Lagrangian drifter data from the mouth of the Columbia River, Oregon and Washington, 2013
Lagrangian surface currents were measured using drifters equipped with global navigation satellite system (GNSS) receivers. A total of 8 drifter deployments were performed between May 25 and June 8, 2013. For each deployment, drifters were released within the MCR and their positions were recorded until the drifters were recovered. The average duration of the drifter deployments varied between 1.6 h and 17.2 h and the number of drifters released in a deployment ranged between 11 and 84. The initial positions ... |
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Lagrangian ocean surface drifter deployments off the National Park of American Samoa, Tutuila, American Samoa, 2015
Satellite-tracked, DGPS-equipped Lagrangian surface-current drifter deployments were conducted over 12 weeks between 14 April and 7 July 2015 at various locations within and offshore of the National Park of American Samoa study area to track surface currents. The drifters internally logged their location every 1 minute, and they transmitted their positions to satellites every 5 minutes. A drogue was attached to the drifters at 1 m below sea level in order to track the currents at that depth. |
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lanai_ero - Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_oha - Overall Hazard Assessment in the coastal zone of Lanai, Hawaii
Overall Hazard Assessment in the coastal zone of Lanai, Hawaii |
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lanai_sea - Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_stm - Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_tsu - Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_wav - High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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Land-Cover Data Derived from Landsat Satellite Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1985 and 2015
This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created to analyze wetland changes along the Virginia and Maryland Atlantic coasts between 1984 and 2015. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). ... |
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Landslide debris aprons offshore of southern California, 2023
Landslide debris aprons have been mapped offshore of Southern California. Polygons were mapped from visual interpretation of high-resolution multibeam echosounder data (MBES), single-beam echosounder data, and seismic reflection data. |
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Landslide evacuation zones offshore of Southern California, 2023
Landslide evacuation zones, which represent the areas from which material is removed by landslide processes, have been mapped offshore of Southern California. Polygons were mapped from visual interpretation of high-resolution multibeam echosounder data (MBES) and single-beam echosounder data. |
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Landslide mass-wasting zones offshore of Southern California, 2023
Landslide mass-wasting zones have been mapped offshore of Southern California. Polygons were mapped from visual interpretation of high-resolution multibeam echosounder data (MBES) and single-beam echosounder data. |
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Landslide scarps offshore of Southern California, 2023
Landslide scarp features have been mapped offshore of Southern California. Polygons were mapped from visual interpretation of high-resolution multibeam echosounder data (MBES) and single-beam echosounder data. |
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Landslides offshore of southern California, 2023
Landslides have been mapped offshore of Southern California. Polygons were mapped from visual interpretation of high-resolution multibeam echosounder data (MBES), single-beam echosounder data, and seismic reflection data. |
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Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.
This shapefile consists of Dauphin Island, AL shorelines extracted from lidar data collected from November 1998 to January 2014. This dataset contains 14 Mean High Water (MHW) shorelines separated into 37 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open ocean, back-barrier, marsh shoreline. Raw lidar point data was converted to a gridded surface, from which a contour of the operational MHW shoreline (0.24 ... |
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Lifespan of Chesapeake Bay salt marsh units
Lifespan distribution in the Chesapeake Bay (CB) salt marsh complex is presented in terms of lifespan of conceptual marsh units defined by Ackerman and others (2022). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are present day estimates at the prescribed rate of SLR, which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022) ... |
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Lifespan of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
The sediment-based lifespan of salt marsh units in Assateague Island National Seashore (ASIS) and Chincoteague Bay is shown for conceptual marsh units defined by Defne and Ganju (2018). The lifespan represents the timescale by which the current sediment mass within a marsh parcel can no longer compensate for sediment export and deficits induced by sea-level rise. The lifespan calculation is based on vegetated cover, marsh elevation, sediment supply, and sea-level rise (SLR) predictions after Ganju and ... |
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Lifespan of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Lifespan of marsh units in New York salt marshes
Lifespan of salt marshes in New York are calculated using conceptual marsh units defined by Defne and Ganju (2018) and Welk and others (2019, 2020a, 2020b, 2020c). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are local estimates which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). The U.S. Geological Survey has been ... |
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Lifespan of Massachusetts salt marsh units
Lifespan of salt marshes in Massachusetts (MA) are calculated using conceptual marsh units defined by Ackerman and others (2022). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are local estimates which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). The U.S. Geological Survey has been expanding national assessment of ... |
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Local radiocarbon reservoir age (Delta-R) variability from the nearshore and open-ocean environments of the Florida Keys reef tract during the Holocene and associated U-series and radiocarbon data (Marine13 Radiocarbon Calibration Curve)
Holocene-aged corals from reef cores collected throughout the Florida Keys reef tract (FKRT) were dated using a combination of U-series and radiocarbon techniques to quantify the millennial-scale variability in the local radiocarbon reservoir age (ΔR) of the shallow water environments of south Florida. ΔR provides a measure of the deviation of local radiocarbon concentrations of marine environments from the global average and can be used as a tracer of oceanic circulation and local hydrology. U.S. ... |
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Local radiocarbon reservoir age (ΔR) variability from the nearshore and open-ocean environments of the Florida Keys reef tract during the Holocene and associated U-series and radiocarbon data (Marine20 Radiocarbon Calibration Curve)
68 Holocene-aged corals from reef cores collected throughout the Florida Keys reef tract (FKRT) were dated using a combination of U-series and radiocarbon techniques to quantify the millennial-scale variability in the local radiocarbon reservoir age (ΔR) of the shallow water environments of south Florida. ΔR provides a measure of the deviation of local radiocarbon concentrations of marine environments from the global average and can be used as a tracer of oceanic circulation and local hydrology. U.S. ... |
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Location and depth data for piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes the location and depth information for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure ... |
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Location and depth data for vibracores collected during a Monterey Bay Aquarium Research Institute cruise in February 2019 offshore of south-central California (USGS FAN 2019-603-FA)
This dataset includes the location and depth information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the ... |
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Location data for vibracores collected during a Monterey Bay Aquarium Research Institute cruise in November 2019 offshore of south-central California (USGS FAN 2019-667-FA)
This dataset includes the location information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study ... |
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Locations of sediment cores collected from Montague Island, AK
This dataset includes locations of sediment cores collected from coastal environments on Montague Island, Alaska. The cores were collected with hand driven peat augers to assess environmental changes related to tectonic uplift caused by historic and prehistoric earthquakes. |
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Long and short-term shoreline change rate transects for the central North Carolina coastal region (NCcentral), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline change rate transects for the southern North Carolina coastal region (NCsouth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline change rate transects for the western North Carolina coastal region (NCwest), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the central coast of North Carolina (NCcentral), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the northern coast of North Carolina (NCnorth), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the southern coast of North Carolina (NCsouth), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the western coast of North Carolina (NCwest), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long-term and short-term shoreline change rates for coastal region around Boston, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for Martha's Vineyard, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for Nantucket, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for Outer Cape Cod, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the Buzzards Bay coastal region in Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the Cape Cod Bay coastal region in Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the coastal region around Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the coastal region north of Boston, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the coastal region south of Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the coast south of Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the Outer Cape Cod, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region north of Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Buzzards Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Cape Cod Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Martha's Vineyard, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Nantucket, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of the Elizabeth Islands, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the southern coastal region of Cape Cod, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the southern coast of Cape Cod, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term shoreline change rates for Rincon, Puerto Rico 1936-2006 (lt_transects.shp)
The 8 km of shoreline from Punta Higüero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2006. Thirteen historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. Shoreline vectors represent the high water line at the time of the survey. |
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Long-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Long-term shoreline change rates for the Florida panhandle (FLph) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Long-term shoreline change rates for the Florida west coast (FLwc) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Long-term shoreline change rates for the Georgia coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long-term shoreline change rate transects for the South Carolina coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Low-altitude aerial imagery collected from a Helikite at Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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MA Bias_Feature – Feature class containing Massachusetts proxy-datum bias information to be used in the Digital Shoreline Analysis System.
The Digital Shoreline Analysis System (DSAS) is a freely available software application that works within the Environmental Systems Research Institute (ESRI) Geographic Information System (ArcGIS) software. DSAS computes rate-of-change statistics for a time series of shoreline vector data. Additionally, the DSAS application is useful for computing rates of change for any boundary-change problem that incorporates a clearly-identified feature position at discrete times, such as glacier limits, river banks, or ... |
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March 2006 Mississippi and Alabama USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 USGS Mississippi ... |
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Marsh Shorelines of the Massachusetts Coast from 2013-14 Topographic Lidar Data
The Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the Massachusetts coast. Seventy-six maps were produced in 1997 depicting a statistical analysis of shoreline change on ocean-facing shorelines from the mid-1800s to 1978 using multiple data sources. In 2001, a 1994 shoreline was added. More recently, in cooperation with CZM, the U.S. Geological Survey (USGS) delineated a new shoreline for Massachusetts using color ... |
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maui_ero - Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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MauiK_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along the Kihei Coast of Maui, Hawaii (Maalaea to Makena)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiK_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Maui Kihei region from Maalaea to Makena, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiK_shorelines - Shorelines of the Kihei coastal region of Maui, Hawaii, from Maalaea to Makena, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiK_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Maui Kihei region from Maalaea to Makena, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiN_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along the North Coast of Maui, Hawaii (Waihee to Kuau)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiN_LT- Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Maui North region from Waihee to Kuau, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiN_shorelines - Shorelines of the northern coastal region of Maui, Hawaii, from Waihee to Kuau, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiN_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Maui North region from Waihee to Kuau, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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maui_oha - Overall Hazard Assessment in the coastal zone of Maui, Hawaii
Overall Hazard Assessment in the coastal zone of Maui, Hawaii |
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maui_sea - Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Maui, Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_stm - Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii
Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_tsu - Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Maui, Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_wav - High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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MauiW_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along the West Coast of Maui, Hawaii (Ukumehame to Honolua)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiW_LT- Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Maui West region from Ukumehame to Honolua, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiW_shorelines - Shorelines of the western coastal region of Maui, Hawaii, from Ukumehame to Honolua, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiW_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Maui West region from Ukumehame to Honolua, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Mean high water (MHW) shorelines along the coast of California used to calculated shoreline change from 1998 to 2016
This dataset contains mean high water (MHW) shorelines for sandy beaches along the coast of California for the years 1998/2002, 2015, and 2016. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. The operational MHW line was extracted from Light Detection and Ranging (LiDAR) digital elevation models (DEMs) using the ArcGIS smoothed contour method. The smoothed contour line was then quality ... |
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Mean High Water Shorelines for the Outer Cape of Massachusetts from Nauset Inlet to Race Point (1998-2005)
This data release contains mean high water (MHW) shorelines for the Outer Cape of Cape Cod, Massachusetts, from Nauset Inlet to Race Point. From 1998-2005, the U.S. Geological Survey surveyed 45 kilometers of coastline 111 times using a ground-based system called Surveying Wide-Area Shorelines (SWASH). The SWASH system used a six-wheeled amphibious all-terrain vehicle as a platform for an array of Global Positioning System sensors. High-accuracy measurements of horizontal position, vertical position, and ... |
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Mean tidal range in marsh units of Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Assateague Island National Seashore and Chincoteague Bay based on conceptual marsh units defined by Defne and Ganju ... |
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Mean tidal range in marsh units of Cape Cod National Seashore salt marsh complex, Massachusetts
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands based on conceptual marsh units defined by ... |
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Mean tidal range in marsh units of Fire Island National Seashore and central Great South Bay salt marsh complex, New York
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Fire Island National Seashore and central Great South Bay salt marsh complex, based on conceptual marsh units defined ... |
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Mean tidal range in marsh units of Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Mean tidal range in marsh units of Plum Island Estuary and Parker River salt marsh complex, Massachusetts
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex based on conceptual marsh units defined by Defne and ... |
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Mean tidal range in salt marsh units of Edwin B. Forsythe National Wildlife Refuge, New Jersey (polygon shapefile)
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New ... |
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Mean tidal range of marsh units in Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Mean tidal range of marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Mean tidal range of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Mean tidal range of marsh units in Eastern Shore of Virginia salt marshes
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Mean tidal range of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Mean tidal range of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Mean tidal range of marsh units in Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Mean tidal range of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Median of wave-current bottom shear stress in the Middle Atlantic Bight for May, 2010 - May, 2011 (MAB_median.SHP)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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Meteorological data from Grizzly Bay, California, 2020
Meteorological data, including wind speed, wind direction, air temperature, relative humidity, and air pressure, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at a site located in Grizzly Bay, California. A Vaisala WXT530 meteorological station was mounted atop of a dolphin-type mooring structure, from January to June 2020. The data were truncated based on deployment and recovery times of hydrodynamic time-series data, spurious data points from the wind sensor ... |
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Meteorological data from Pea Island National Wildlife Refuge, North Carolina, 9/13/2021 to 10/24/2021
Meteorological data were collected as part of the DUring Nearshore Event eXperiment (DUNEX) on Pea Island National Wildlife Refuge in North Carolina from 9/13/2021 to 10/24/2021. The DUNEX project is a collaborative, multi-agency experiment designed to provide comprehensive measurements of storm-induced processes on coastal habitats. The overarching goals of this study are to understand oceanographic processes and their contribution to coastal morphological changes. These data will be used to improve storm ... |
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Microbial Ecology of the Floridan Aquifer near Okeechobee, FL
This metadata record describes the microbial uptake of nutrients by native planktonic and biofilm-associated bacteria within the Upper Floridan Aquifer (approximately 1,000 feet below land surface) in the Okeechobee, Florida area. Groundwater samples were collected between 2018 and 2019 from a South Florida Water Management District (SFWMD) monitoring well installed at the Kissimmee River Aquifer Storage and Recovery (ASR) facility, which is located at the discharge point of the Kissimmee River into Lake ... |
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Microbial Ecology of the Floridan Aquifer near Okeechobee, FL: Uptake and Inactivation Data
This metadata record describes the microbial uptake of nutrients by native planktonic and biofilm-associated bacteria and the inactivation of E. coli, MS2 bacteriophage, polio virus and Cryptosporidium parvum in the Upper Floridan Aquifer (approximately 1,000 feet below land surface) in the Okeechobee, Florida area. Groundwater samples were collected or accessed via the use of a mesocosm between 2018 and 2020 from a South Florida Water Management District (SFWMD) monitoring well installed at the Kissimmee ... |
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Mineralogical point-count data from vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes mineralogical point-count data from sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - 2005/06/19 through 2005/11/20 Deterministic Scenario
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - 2015/08/27 through 2015/08/29 Deterministic Scenario
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - 2015/12/09 through 2015/12/11 Deterministic Scenario
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Existing Condition 10-Year Simulation with 0.5-meter of Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Existing Condition 10-Year Simulation Without Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Existing Condition 2010 Simulation With 0.5-meter of Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Existing Condition 2010 Simulation With 0.5-meter of Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Existing Condition 2010 Simulation Without Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Initial Existing Conditions Grid
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - Initial Project Conditions Grid
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - With-Project Condition 10-Year Simulation With 0.5-meter of Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - With-Project Condition 10-Year Simulation Without Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Mobile Harbor Navigation Channel Delft3D Model Inputs and Results - With-Project Condition 2010 Simulation Without Sea Level Rise
The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and ... |
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Modeled and Observed Weekly Mean Wave Height for Validation of a Wave Exposure Model of Grand Bay, Mississippi
Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change, they can also influence shore-proximal vertical accretion through sediment delivery. For these reason, estimates of wave energy can provide a ... |
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Modeled effects of depth and semidiurnal temperature fluctuations on predictions of year that coral reef locations reach annual severe bleaching for various global climate model projections
Using global climate model projections of sea-surface temperature at coral reef sites, we modeled the effects of depth and exposure to semidiurnal temperature fluctuations to examine how these effects may alter the projected year of annual severe bleaching for coral reef sites globally. Here we present the first global maps of the effects these processes have on bleaching projections for three IPCC-AR5 emissions scenarios. |
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Modeled extreme total water levels along the U.S. west coast
This dataset contains information on the probabilities of storm-induced erosion (collision, inundation and overwash) for each 100-meter (m) section of the United States Pacific coast for return period storm scenarios. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the hydrodynamic forcing. Storm-induced water levels, due to both surge and waves, are compared to coastal ... |
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Modeled surface waves from winds in South San Francisco Bay
A model application using the phase-averaged wave model SWAN was developed to simulate wind waves in South San Francisco Bay, California, between May 30, 2021, and May 19, 2022. This data release describes the development of the model application, provides input files suitable for running the model using Delft3D version 4.04.01, and includes output from the model simulations in netCDF format. |
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Model input and output files for modeling surface gravity waves on a schematized ancient lake on Mars
This portion of the data release presents a wave model application developed to simulate wind generated surface gravity waves on an ancient lake on Mars. The phase-averaged wave model, SWAN, was applied within the Delft3D modeling system (Deltares, 2018) with reduced gravity and a range of atmospheric densities and wind speeds to simulate potential conditions that could generate wind waves on Mars. The data release includes model input files for simulations with three different atmospheric densities, ... |
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Model input files for the lower Nooksack River and delta, western Washington State
This data set consists of physics-based Delft3D-Flexible Mesh hydrodynamic model input files that are used to simulate compound flood exposure of the lower Nooksack River and delta of western Washington State under existing and future conditions of anticipated climate and land-use change. The model enables assessment of the changing flood exposure associated with the cumulative impacts of expected sea-level rise, greater tidal inundation, more frequent storm surge effects, and higher winter stream floods ... |
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Model parameter input files to compare effects of stream discharge scenarios on sediment deposition and concentrations around coral reefs off west Maui, Hawaii
This dataset consists of physics-based Delft3D model and Delwaq model input files used in modeling sediment deposition and concentrations around the coral reefs of west Maui, Hawaii. The Delft3D models were used to simulate waves and currents under small (SC1) and large (‘SC2’) wave conditions for current stream discharge (‘Alt1’) and stream discharge with watershed restoration (‘Alt3’). Delft3D model results were subsequently used as forcing conditions for Delwaq models to simulate sediment ... |
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Model parameter input files to compare locations of coral reef restoration on different reef profiles to reduce coastal flooding
This dataset consists of physics-based XBeach Non-hydrostatic hydrodynamic models input files used to study how coral reef restoration affects waves and wave-driven water levels over coral reefs, and the resulting wave-driven runup on the adjacent shoreline. Coral reefs are effective natural coastal flood barriers that protect adjacent communities. Coral degradation compromises the coastal protection value of reefs while also reducing their other ecosystem services, making them a target for restoration. ... |
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Model parameter input files to compare the influence of channels in fringing coral reefs on alongshore variations in wave-driven runup along the shoreline
An extensive set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate the influence of shore-normal reef channels on flooding along fringing reef-lined coasts, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is greatest. These input files accompany the modeling conducted for the following publication: Storlazzi, C.D., Rey, A.E., and van Dongeren, A.R., 2022, ... |
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Model parameter input files to compare the influence of coral reef carbonate budgets on alongshore variations in wave-driven total water levels on Buck Island Reef National Monument
A set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate how varying carbonate budgets, and thus coral reef accretion and degradation, affect alongshore variations in wave-driven water levels along the adjacent shoreline of Buck Island Reef National Monument (BUIS) for a number of sea-level rise scenarios, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is ... |
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Model parameter input files to compare wave-averaged versus wave-resolving XBeach coastal flooding models for coral reef-lined coasts
This data release includes the XBeach input data files used to evaluate the importance of explicitly modeling sea-swell waves for runup. This was examined using a 2D XBeach short wave-averaged (surfbeat, XB-SB) and a wave-resolving (non-hydrostatic, XB-NH) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Results show that explicitly modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time ... |
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Model parameter input files to study three-dimensional flow over coral reef spur-and-groove morphology
This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning ... |
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molo_ero - Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_oha - Overall Hazard Assessment in the coastal zone of Molokai, Hawaii
Overall Hazard Assessment in the coastal zone of Molokai, Hawaii |
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molo_sea - Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_stm - Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_tsu - Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_wav - High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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Monthly bedload estimates, Elwha River, Washington, October 2015 to September 2016
Bedload sediment transport was calculated on the Elwha River, Washington to measure the amount of sediment transported along the riverbed during the 2016 water year. Bedload was measured using the Elwha bedload impact plate system (Hilldale and others, 2015). Physical bedload sampling by the U.S. Bureau of Reclamation for system calibration took place during November, 2012; March, May, and June 2013; and April 2014 at the Diversion Weir gauge (Magirl and others, 2015). Early in water year 2016 (year 5) the ... |
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Multibeam Bathymetry Data Collected in 2016 from Grand Bay Alabama/Mississippi: Adjusted processed elevation point data (x,y,z)
A reconnaissance multibeam bathymetry survey was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi on May 12, 2016 as an assessment of the shallow water capabilities of the Teledyne Reson SeaBat T50-P multibeam echosounder, and as an attempt to map the eroding marsh edges at locations of interest around the bay. This dataset, Grand_Bay_2016_MBB_Adjusted_xyz.zip, includes the resulting processed elevation point data (x,y,z ... |
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Multibeam Bathymetry Data Collected in 2016 from Grand Bay Alabama/Mississippi: Trackline navigation
A reconnaissance multibeam bathymetry survey was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi on May 12, 2016 as an assessment of the shallow water capabilities of the Teledyne Reson SeaBat T50-P multibeam echosounder, and as an attempt to map the eroding marsh edges at locations of interest around the bay. This dataset, Grand_Bay_2016_MBB_Tracklines.zip, includes the trackline vector file derived from the ... |
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Multibeam Bathymetry Data Collected in 2016 from Grand Bay Alabama/Mississippi: Unadjusted processed elevation point data (x,y,z)
A reconnaissance multibeam bathymetry survey was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) on May 12, 2016 as an assessment of the shallow water capabilities of the Teledyne Reson SeaBat T50-P multibeam echosounder, and as an attempt to map the eroding marsh edges at locations of interest around the bay. This dataset, Grand_Bay_2016_MBB_Unadjusted_xyz.zip, includes the resulting [unadjusted] processed ... |
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Multibeam Bathymetry Data Collected in 2018 from Grand Bay and Point Aux Chenes Bay Alabama/Mississippi: Processed elevation point data (x,y,z)
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) October 22-23, 2018. This dataset, Grand_Bay_2018_MBB_xyz.zip, includes the processed point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Multibeam Bathymetry Data Collected in 2018 Offshore of Cedar Key, Florida
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of Cedar Key, Florida (FL) during two legs, November 27-30, and December 10-13, 2018. This dataset, Cedar_Key_MBB_2018_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Multibeam Bathymetry Data Collected in 2019 from Grand Bay and Point Aux Chenes Bay Alabama/Mississippi: Processed Elevation Point Data (x,y,z)
An Ellipsoidally Referenced Survey (ERS) using a Teledyne Reson SeaBat T50-P multibeam echosounder was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) May 7-10, 2019. This dataset, Grand_Bay_2019_MBES_xyz.zip, includes the processed point data (x,y,z), as derived from a 1-meter (m) bathymetric grid from two separate sensor configurations, which were acquired independently. One configuration utilized a tilted ... |
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Multibeam Bathymetry Data Collected in December 2017, February and March 2018 at Looe Key, the Florida Keys
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) at Looe Key, the Florida Keys, during three separate survey legs: December 14-16, 2017, February 2-9, 2018 and March 9-11, 2018. This dataset, Looe_Key_2017_2018_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric ... |
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Multibeam Bathymetry Data Collected in March 2018 at Crocker Reef, the Florida Keys
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) at Crocker Reef, the Florida Keys March 8-15, 2018. This dataset, Crocker_2018_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Multibeam Bathymetry Data Collected in October and December 2017 at Crocker Reef, the Florida Keys
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) at Crocker Reef, the Florida Keys October 10-28, and December 5-8, 2017. This dataset, Crocker_2017_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Multichannel seismic-reflection data acquired off the coast of southern California - Part A 1997, 1998, 1999, and 2000
Multichannel seismic-reflection (MCS) data were collected in the California Continental Borderland as part of southern California Earthquake Hazards Task. Five data acquisition cruises conducted over a six-year span collected MCS data from offshore Santa Barbara, California south to the Exclusive Economic Zone boundary with Mexico. The primary mission was to map late Quaternary deformation as well as identify and characterize fault zones that have potential to impact high population areas of southern ... |
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Multi-Sensor Core Logger (MSCL) data of piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes multi-sensor core logger (MSCL) data for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure ... |
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Multi-sensor core logger (MSCL) data of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes multi-sensor core logger (MSCL) data from sediment cores collected offshore central California in the vicinity of Morro Bay. The sediment cores were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Multi-Sensor Core Logger (MSCL) P-wave velocity and gamma-ray density whole-core logs of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California
This part of the data release includes Multi-Sensor Core Logger (MSCL) P-wave velocity and gamma-ray density whole-core logs of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium ... |
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Multi-Sensor Core Logger (MSCL) P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs of sediment cores collected in 2009 offshore from Palos Verdes, California
This part of the data release includes Multi-Sensor Core Logger (MSCL) P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the ... |
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Multi-sensor core logger (MSCL) scans of sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset includes multi-sensor core logger (MSCL) data of sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. |
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Name, location, and length of sediment cores collected in 2009 offshore from Palos Verdes, California
This part of the data release is a spreadsheet including the name, location, and length of sediment cores collected in 2009 offshore from Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs ... |
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Name, location, and length of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California
This part of the data release is a spreadsheet including the name, location, and length of sediment cores collected in 2014 in Monterey Canyon. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 ... |
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National Assessment of Hurricane-Induced Coastal Erosion Hazards: 2021 Update
This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 1-kilometer (km) section of the United States [Gulf of Mexico and Atlantic] coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due ... |
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National Assessment of Hurricane-Induced Coastal Erosion Hazards: Puerto Rico
This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 100-meter (m) section of the Puerto Rico coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due to both surge and waves, are ... |
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Navigation and environmental data from R/V Weatherbird II for the West Florida Shelf: USGS Cruise 11BHM01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, ... |
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Navigation and environmental data from R/V Weatherbird II for the West Florida Shelf: USGS Cruise 11BHM02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25-30, ... |
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Navigation and environmental data from R/V Weatherbird II for the West Florida Shelf: USGS Cruise 11BHM03
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Navigation and environmental data from R/V Weatherbird II for the West Florida Shelf: USGS Cruise 11BHM04
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Navigation and environmental data from R/V Weatherbird II for the West Florida Shelf: USGS Cruise 11CEV01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred January 3-7, ... |
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Navigation and environmental data from R/V Weatherbird II for the West Florida Shelf: USGS Cruise 11CEV02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred February 17 ... |
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Navigation data from Research Vessels Weatherbird II and Bellows collected within West Florida Shelf during July and August 2013
The United States Geological Survey (USGS) is studying the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises on the West Florida Shelf and northern Gulf of Mexico regions aboard the research vessel (R/V) Weatherbird II or Bellows, ships of opportunity led by Dr. Kendra Daly, of the University of South Florida (USF) in July and August, ... |
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Near-bed velocity measurements in Monterey Bay during arrival of the 2010 Chile Tsunami
On February 27, 2010, a tsunami originating near Chile arrived in Monterey Bay, California. This data release comprises two hours of pressure and near-bed velocity data spanning the largest tsunami waves. At the time, the U.S. Geological Survey Pacific Coastal and Marine Science Center had a remotely-controlled instrumented platform deployed adjacent to the Santa Cruz Municipal Wharf (mean depth 9 m) for collecting hydrodynamic and sediment transport data. In anticipation of the arrival of the tsunami, ... |
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Nearshore groundwater seepage and geochemical data measured in 2015 at Guinea Creek, Rehoboth Bay, Delaware
Assessment of biogeochemical processes and transformations at the aquifer-estuary interface and measurement of the chemical flux from submarine groundwater discharge (SGD) zones to coastal water bodies are critical for evaluating ecosystem service, geochemical budgets, and eutrophication status. The U.S. Geological Survey and the University of Delaware measured rates of SGD and concentrations of dissolved constituents, including nitrogen species, from recirculating ultrasonic and manual seepage meters, and ... |
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Nearshore Multibeam Bathymetry Data: Madeira Beach, Florida, February 2017
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Madeira Beach, Florida February 13-17, 2017. This dataset, Madeira_Beach_2017_MBES_1m_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid. |
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Nearshore parametric wave setup future projections (2020-2050) for the North and South Carolina coasts
This dataset presents alongshore wave setup timeseries for the North and South Carolina coastlines. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to cross-shore transects spaced at ... |
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Nearshore parametric wave setup future projections (2020-2050) for the U.S. Atlantic coast
This dataset presents alongshore wave setup timeseries for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to ... |
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Nearshore parametric wave setup hindcast data (1979-2019) for the North and South Carolina coasts
This dataset presents alongshore wave setup timeseries for the North and South Carolina coastlines. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to cross-shore transects spaced at ... |
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Nearshore parametric wave setup hindcast data (1979-2019) for the U.S. Atlantic coast
This dataset presents alongshore wave setup timeseries for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to ... |
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Nearshore Single-Beam Bathymetry Data: Madeira Beach, Florida, February 2017
In February 2017, the United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted multibeam and single-beam bathymetric surveys of the nearshore waters off Madeira Beach, Florida. These data were collected as part of a regional study designed to better understand coastal processes on barrier islands and sandy beaches. Results from this study will be incorporated with observations from other regional studies in order to validate operational water level and ... |
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Nearshore total water level (TWL) proxies (2018-2100) for Northern California
Nearshore proxies for total water level (TWL) developed for Coastal Storm Model (CoSMoS) work in Northern California 3.2 are presented. Deterministic dynamical modeling of future climate conditions and associated hazards, such as flooding, can be computationally-expensive if century-long time-series of waves, sea level variations, and overland flow patterns are simulated. To focus such modeling on storm events of interest, local impacts over long time periods and large geographical areas are estimated. ... |
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Nearshore water level, tide, and non-tidal residual future projections (2016-2050) for the North and South Carolina coasts
A dataset of modeled nearshore water levels (WLs) was developed for the North and South Carolina coastlines. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields. Water level outputs were extracted from the global grid at approximately 20 km resolution along the southeast Atlantic coastline. These data were then statistically downscaled using a ... |
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Nearshore water level, tide, and non-tidal residual future projections (2016-2050) for the U.S. Atlantic coast
A dataset of modeled nearshore water levels (WLs) was developed for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields. Water level outputs were extracted from the global grid at approximately 20 km resolution along the Atlantic coastline. These data were then ... |
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Nearshore water level, tide, and non-tidal residual hindcasts (1979-2016) for the North and South Carolina coasts
A dataset of modeled nearshore water levels (WLs) was developed for the North and South Carolina coastlines. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields -. Water level outputs were extracted from the global grid at approximately 20 km resolution along the coastlines. These data were then statistically downscaled using a signal-specific ... |
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Nearshore water level, tide, and non-tidal residual hindcasts (1979-2016) for the U.S. Atlantic coast
A dataset of modeled nearshore water levels (WLs) was developed for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields -. Water level outputs were extracted from the global grid at approximately 20 km resolution along the coastlines. These data were then statistically ... |
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Nearshore waves in southern California: hindcast, and modeled historical and 21st-century projected time series
Abstract: This data release presents modeled time series of nearshore waves along the southern California coast, from Point Conception to the Mexican border, hindcasted for 1980-2010 and projected using global climate model forcing for 1975-2005 and 2012-2100. Details: As part of the Coastal Storm Modeling System (CoSMoS), time series of hindcast, historical, and 21st-century nearshore wave parameters (wave height, period, and direction) were simulated for the southern California coast from Point Conception ... |
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Near-surface wind fields for San Francisco Bay--historical and 21st century projected time series
To support Coastal Storm Modeling System (CoSMoS) in the San Francisco Bay (v2.1), time series of historical and 21st-century near-surface wind fields (eastward and northward wind arrays) were simulated throughout the Bay. While global climate models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling projects, such as CoSMoS. Short-duration high wind speeds, on the order of hours, are of key ... |
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NORCAL1854_1880 - Vectorized Shoreline of Northern California from 1854-1880 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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NORCAL1928_1936 - Vectorized Shoreline of Northern California Derived from 1928-1936 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or ... |
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NORCAL1952_1971 - Vectorized Shoreline of Northern California Derived from 1952-1971 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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NORCAL2002 - Vectorized Shoreline of Northern California Derived from 2002 Lidar Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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NORCAL_BASELINES - Offshore Baseline for Northern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_BIASVALUES - Northern California Shoreline Bias Values
The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS ... |
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NORCAL_INTERSECTS_LT - Long-Term Transect-Shoreline Intersection Points for Northern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_INTERSECTS_ST - Short-Term Transect-Shoreline Intersection Points for Northern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_TRANSECTS_LT - Long-Term Shoreline Change Rates for Northern California Generated at a 50 m Transect Spacing, 1854-2002
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_TRANSECTS_ST - Short-Term Shoreline Change Rates for Northern California Generated at a 50m Transect Spacing, 1952-2002
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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Northern California 3.2 projections of coastal cliff retreat due to 21st century sea-level
This dataset contains projections of coastal cliff retreat and associated uncertainty across Northern California for future scenarios of sea-level rise (SLR) to include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300, and 500 centimeters (cm) of SLR by the year 2100 and cover coastline from the Golden Gate Bridge to the California-Oregon state border. Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical models and field observations ... |
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Northern California cross-shore transects for CoSMoS 3.2
Cross-shore transects (CSTs) developed for Coastal Storm Model (CoSMoS) work in Northern California 3.2 are presented. 3,528 CSTs are numbered consecutively from 8067 at Golden Gate Bridge to 11,594 at the California/Oregon state border. Each of the profiles extend from the approximate -15 m isobath to at least 10 m above NAVD88 (truncated in cases where a lagoon or other waterway exists on the landward end of the profile), and are spaced approximately 100-250 m apart. |
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OahuE_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along East Oahu, Hawaii (Kahuku to Makapuu)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuE_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu East region from Kahuku to Makapuu, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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oahu_ero - Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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OahuE_shorelines - Shorelines of the eastern coastal region of Oahu, Hawaii, from Kahuku to Makapuu, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuE_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu East region from Kahuku to Makapuu, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuN_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along North Oahu, Hawaii (Camp Erdman to Kahuku)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuN_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu north region from Camp Erdman to Kahuku, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuN_shorelines - Shorelines of the northern coastal region of Oahu, Hawaii, from Camp Erdman to Kahuku, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuN_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu North region from Camp Erdman to Kahuku, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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oahu_oha - Overall Hazard Assessment in the coastal zone of Oahu, Hawaii
Overall Hazard Assessment in the coastal zone of Oahu, Hawaii |
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OahuS_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along South Oahu, Hawaii (Barbers Point to Sandy Beach)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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oahu_sea - Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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OahuS_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu south region from Barbers Point to Sandy Beach, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuS_shorelines - Shorelines of the southern coastal region of Oahu, Hawaii, from Barbers Point to Sandy Beach, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuS_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu south region from Barbers Point to Sandy Beach, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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oahu_stm - Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_tsu - Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_wav - High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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OahuW_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along West Oahu, Hawaii (Yokohama to Tracks Beach)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuW_LT- Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu west region from Yokohama to Tracks Beach, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuW_shorelines - Shorelines of the western coastal region of Oahu, Hawaii, from Yokohama to Tracks Beach, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuW_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu west region from Yokohama to Tracks Beach, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Observations of tsunami and runup heights in Santa Cruz Harbor and surrounding beaches from the 2022 Hunga Tonga-Hunga Ha'apai tsunami
The 14 January 2022 eruption of Tonga Hunga-Tonga Ha'apai volcano generated tsunamis that impacted the west coast of the United States on the morning of 15 January 2022. This data release presents runup heights and tsunami heights collected by the U.S. Geological Survey (USGS) and the California Geological Survey (CGS) during surveys at the Santa Cruz Harbor and beaches in Santa Cruz County, California, on January 19th and 20th, 2022 (USGS Field Activity 2022-607-FA). Evidence of tsunami inundation included ... |
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Oceanographic measurements obtained offshore of the Elwha River delta in coordination with the Elwha River Restoration Project, Washington, USA, 2010-2014
Time-series data of velocity, pressure, turbidity, conductivity, and temperature were collected near the mouth of the Elwha River, Washington, USA, from December 2010 through October 2014, for the Department of Interior’s Elwha River Restoration project. As part of this project, the U.S. Geological Survey studied the effects of renewed sediment supplies on the coastal ecosystems before, during, and following the removal of two dams, Elwha and Glines Canyon, from the Elwha River. Removal of the dams ... |
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Oceanographic Time Series Data: Northeast Atlantic Outer Continental Shelf, Gulf of Maine and Georges Bank Marine Sanctuary
Time-series oceanographic data for the Northeast Atlantic outer continental shelf, Gulf of Maine and Georges Bank collected by the U.S. Geological Survey (USGS) or used in conjunction with USGS projects. These data are stored as NetCDF files using conventions developed by National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) lab to be compatible with their EPIC system. Variables present in the files include: ocean current, temperature, pressure, conductivity, ... |
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Oceanographic time-series measurements collected in Bellingham Bay, Washington, USA, 2019 to 2021
Bottom-landing and floating platforms with instrumentation to measure currents, waves, water level, optical turbidity, water temperature, and conductivity were deployed at four locations in Bellingham Bay, Washington, USA. Platforms were deployed in three separate periods: July 30, 2019–November 14, 2019, November 19, 2019–February 5, 2020, and January 22, 2021–April 13, 2021. These data were collected to support studies of sediment delivery, transport, deposition, and resuspension in this Pacific ... |
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Oceanographic time-series measurements collected in the Stillaguamish River Delta, Port Susan, Washington, USA from March 2014 to July 2015
Water level, flow velocity, temperature, salinity, and turbidity were measured in a breach constructed in a flood-protection levee surrounding a restored former agricultural area in Port Susan, Washington, USA, near the mouth of the Stillaguamish River. Data were collected in a breach known as PSB1 at 15-minute intervals from March 21, 2014 to July 1, 2015 using a SonTek Argonaut-SW current meter, an In-Situ Aqua TROLL 200 pressure, conductivity, and temperature sensor, and an FTS DTS-12 turbidity sensor. |
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Ocean wave time-series data along the Alaska coast simulated with a global-scale numerical wave model under the influence of CMIP6 wind and sea ice fields
This dataset presents projected hourly time-series of wave heights, wave periods, incident wave directions and directional spreading at distinct points along the open coast of Alaska for the years 2020 through 2050. The projections were developed by running the National Oceanic and Atmospheric Administration’s (NOAA’s) WAVEWATCHIII model. Wind and sea ice fields from seven different Global Climate or General Circulation Models from the CMIP6 High-Resolution Model Intercomparison Project were used to ... |
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Ocean wave time-series data along the U.S. Atlantic, Gulf of Mexico, and Puerto Rico coasts simulated with a global-scale numerical wave model under the influence of CMIP6 wind and sea ice fields
This dataset presents projected hourly time-series of wave heights, wave periods, incident wave directions, and directional spreading at distinct points along the U.S. Atlantic, Gulf of Mexico, and Puerto Rico coasts for the years 2020 through 2050. The projections were developed by running the National Oceanic and Atmospheric Administration’s (NOAA’s) WAVEWATCHIII model. Wind and sea ice fields from seven different Global Climate or General Circulation Models from the CMIP6 High-Resolution Model ... |
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Ocean wave time-series data along the U.S. West Coast and surrounding Hawai’i simulated with a global-scale numerical wave model under the influence of CMIP6 wind and sea ice fields
This dataset presents projected hourly time-series of wave heights, wave periods, incident wave directions, and directional spreading at distinct points along the U.S. West Coast and surrounding Hawai’i for the years 2020 through 2050. The projections were developed by running the National Oceanic and Atmospheric Administration’s (NOAA’s) WAVEWATCHIII model. Wind and sea-ice fields from seven different Global Climate or General Circulation Models from the CMIP6 High-Resolution Model Intercomparison ... |
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Ocean wave time-series data surrounding Hawai’i and U.S. territories in the Pacific Ocean simulated with a global-scale numerical wave model under the influence of CMIP6 wind and sea ice fields
This dataset presents projected hourly time-series of wave heights, wave periods, incident wave directions, and directional spreading at distinct points surrounding Hawai’i and U.S. territories in the Pacific Ocean, for the years 2020 through 2050. The projections were developed by running the National Oceanic and Atmospheric Administration’s (NOAA’s) WAVEWATCHIII model. Wind and sea ice fields from seven different Global Climate or General Circulation Models from the CMIP6 High-Resolution Model ... |
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Offshore baseline for Boston coastal region generated to calculate shoreline change rates from Carson Beach in South Boston to Weymouth River on the Massachusetts mainland, and including the Boston Harbor Islands 9Boston_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for Cape Cod coastal region generated to calculate shoreline change rates from Provincetown to the southern end of Monomoy Island, Massachusetts (CapeCod_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for Delmarva North coastal region generated to calculate shoreline change rates from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia (DelmarvaN_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for Greater Boston coastal region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts, generated to calculate shoreline change rates (GreaterBoston_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for Long Island coastal region generated to calculate shoreline change rates for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for Martha's Vineyard coastal region generated to calculate shoreline change rates within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for Massachusetts Islands coastal region generated to calculate shoreline change rates for Martha's Vineyard and Nantucket (MA_Islands_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for New England North coastal region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts, generated to calculate shoreline change rates (NE_North_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for New England South coastal region from Dartmouth, Massachusetts to Napatree Point, Rhode Island, generated to calculate shoreline change rates (NE_South_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for New Jersey North coastal region generated to calculate shoreline change rates from Sandy Hook to Little Egg Inlet, New Jersey (NJN_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for New Jersey South coastal region generated to calculate shoreline change rates from Little Egg Inlet to Cape May, New Jersey (NJS_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Alabama coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the central North Carolina (NCcentral) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Delmarva South/Southern Virginia region generated to calculate shoreline change rates from Wallops Island, Virginia to the Virginia/North Carolina border (DelmarvaS_SVA_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the exposed Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the exposed East Beaufort Sea, Alaska coastal region (U.S. Canadian Border to the Hulahula River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the exposed Eastern Chukchi Sea, Alaska coastal region (Point Barrow to Icy Cape) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between Point Barrow and Icy Cape for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the exposed West Beaufort Sea, Alaska coastal region (Colville River to Point Barrow) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the Florida north (FLnorth) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Florida west (FLwest) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Georgia (GA) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Louisiana coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Mississippi coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the northeastern Florida (FLne) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between the Okpilak-Hulahula River Delta and the Colville River Deltas for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between the U.S.-Canadian border and the Okpilak-Hulahula River Delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between the Okpilak-Hulahula River Delta and the Colville River Delta for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between the U.S.-Canadian border and the Okpilak-Hulahula River Delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern North Carolina (NCnorth) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Oregon coastal region generated to calculate shoreline change rates (OR_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the sheltered Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the sheltered East Beaufort Sea, Alaska coastal region (U.S. Canadian Border to the Hulahula River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the sheltered Eastern Chukchi Sea, Alaska coastal region (Point Barrow to Icy Cape) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal between Point Barrow and Icy Cape for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the sheltered West Beaufort Sea, Alaska coastal region (Colville River to Point Barrow) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the South Carolina (SC) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the southeastern Florida (FLse) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the southern North Carolina (NCsouth) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Texas east (TXeast) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Texas west (TXwest) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the Washington coastal region generated to calculate shoreline change rates (WA_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for the western North Carolina (NCwest) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline generated to calculate bluff change rates for the north coast of Barter Island, Alaska
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate bluff-change rates. |
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Offshore baseline generated to calculate shoreline change rates for the north coast of Alaska, Icy Cape to Cape Prince of Wales
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed coast of Alaska from Icy Cape and Cape Prince Wales for the time period 1948 to 2016. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline generated to calculate shoreline change rates near Barter Island, Alaska
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the shorelines near Barter Island, Alaska for the time period 1947 to 2020. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baselines for Assateague Island, Maryland and Virginia (projected, UTM Zone 18 (NAD83))
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future ... |
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Offshore Baselines for the Undeveloped Areas of New Jersey's Barrier Islands (projected, UTM Zone 18N (NAD83))
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these ... |
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Ofu, American Samoa, wave and water level data, 2020
Time series data of wave height and water surface elevation were acquired for 399 days at four locations on the southern reef of Ofu, American Samoa, in support of a study on submarine groundwater dynamics on this reef within the National Park of American Samoa’s Ofu Unit. The relative placement of sensors on the reef were as follows: OFU20E03 – mid reef at East site; OFU20E04 – inner reef at East site; OFU20W03 – mid reef at West site; OFU20W04 – inner reef at West site. |
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Onshore/offshore baseline for Cape Cod Bay coastal region generated to calculate shoreline change rates from Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Onshore/offshore baseline for Nantucket coastal region generated to calculate shoreline change rates for the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Onshore/offshore baseline for the North Shore coastal region generated to calculate shoreline change rates for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Onshore/offshore baseline for the Outer Cape Cod coastal region generated to calculate shoreline change rates from Long Point in Provincetown to Monomoy Island (OuterCapeCod_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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OpenFOAM models of low- and high-relief sites from the coral reef flat off Waiakane, Molokai, Hawaii
OpenFOAM Computational Fluid Dynamics (CFD) models were developed to simulate wave energy dissipation across natural rough reef surfaces on the reef flat off Waiakane, Molokai, Hawaii, to understand this process in the context of reef restoration design. A total of 140 models were developed (70 per low- and 70 per high-bed-relief domains). Models were calibrated and validated with oceanographic datasets collected in 2018. This data release presents the 140 model scenarios that can be readily input into ... |
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Optically stimulated luminescence (OSL) age data from vibracores collected offshore central California, during field activity 2019-651-FA (ver 2.0, August 2023)
This dataset includes optically stimulated luminescence (OSL) age data from sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Orthoimagery of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents orthoimagery spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the orthoimages were created, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected with precise ... |
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Orthomosaic imagery for Whiskeytown Lake and surrounding area, 2019-06-03
This portion of the data release presents an RGB orthomosaic image of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-06-03. The orthomosaic is available in a high-resolution 6-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud-optimized GeoTIFF format, with 8-bit ... |
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Orthomosaic imagery for Whiskeytown Lake and surrounding area, expanded AOI, 2019-06-03
This portion of the data release presents an RGB orthomosaic image of an expanded area surrounding Whiskeytown Lake derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-06-03. The orthomosaic is available in a high-resolution 14-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud-optimized GeoTIFF format, with ... |
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Orthomosaic imagery for Whiskeytown Lake and surrounding area, northern California, 2018-12-02
This portion of the data release presents an RGB orthomosaic image of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2018-12-02. The orthomosaic is available in a high-resolution 6-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud optimized GeoTIFF format, with 8-bit ... |
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Orthomosaic imagery for Whiskeytown Lake and surrounding area, northern California, 2019-11-12
This portion of the data release presents an RGB orthomosaic image of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-11-12. The orthomosaic is available in a high-resolution 6-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud optimized GeoTIFF format, with 8-bit ... |
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Orthomosaic imagery for Whiskeytown Lake and surrounding area, northern California, 2020-11-10
This portion of the data release presents an RGB orthomosaic image of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2020-11-10. The orthomosaic is available in a high-resolution 5-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud optimized GeoTIFF format, with 8-bit ... |
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Orthomosaic representing Head of the Meadow Beach, Truro from images collected during field activity 2021-014-FA on February 4, 2021
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In February 2021, U.S. Geological Survey and ... |
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Orthomosaic representing Head of the Meadow Beach, Truro, MA on March 10, 2023
The data in this release re-map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA and provide updated environmental context for the 2020 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-01, which are two video cameras aimed ... |
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Orthomosaic representing Head of the Meadow Beach, Truro on March 10, 2022
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In March 2022, U.S. Geological Survey and Woods ... |
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Orthomosaic representing Marconi Beach, Wellfleet from images acquired during field activity 2021-022-FA on March 17, 2021
The data in this publication map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide regional context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-022-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Orthomosaic representing Marconi Beach, Wellfleet, MA March 11, 2022
The data in this release map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Orthomosaic representing Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Orthomosaics of Pea Island National Wildlife Refuge DUNEX Site, North Carolina in September and October 2021
The data in this part of the release are orthomosaics that characterize the beach at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. During September and October 2021, USGS and Woods Hole Oceanographic Institute (WHOI) scientists conducted multiple field surveys to collect a topobathy elevation time series. Images of the beach for use in structure from motion were taken with a camera attached to a helium filled balloon-kite (Helikite). Agisoft Metashape (v ... |
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P07_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P08_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set if cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P09_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P10_Nov2012_Oct2014: Fire Island, NY pre- and post- storm cross-shore profiles from November 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from November 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 14 dates from November 04 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P11_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 15 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P22_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P23_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P24_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P25_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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P26_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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PACIFIC - Coastal Vulnerability to Sea-Level Rise: U.S. Pacific Coast
The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where ... |
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Paleoshorelines--Monterey Canyon and Vicinity Map Area, California
This part of DS 781 presents data for the paleoshorelines for the geologic and geomorphic map of Monterey Canyon and Vicinity, California. The vector data file is included in "Paleoshorelines_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ofr20161072. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H ... |
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Parent and alkylated polycyclic aromatic hydrocarbons (PAHs) and per- and polyfluoroalkyl substances (PFAS) in north San Francisco Bay, Napa River, and Sonoma Creek in 2018 and 2019
Sediment grain-size distributions, stable carbon isotope ratios (d13C), total carbon to total nitrogen ratios (C:N), short-lived radionuclides (Beryllium-7, Cesium-137, and Lead-210), concentrations of 76 parent and alkylated polycyclic aromatic hydrocarbons (PAHs) and concentrations of 33 per- and polyfluoroalkyl substances (PFAS) were measured in the northern reach of San Francisco Bay (San Pablo and Suisun Bays), and in stream beds of the lower reaches of Napa River and Sonoma Creek, 5 months and 20 ... |
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Parent and alkylated polycyclic aromatic hydrocarbons (PAHs) in watershed soil and reef sediment at Olowalu, Maui, 2022
Seventy six parent and alkylated polycyclic aromatic compounds, including polycyclic aromatic hydrocarbons (PAHs), were quantified in watershed and reef sediment from Olowalu, Maui, in February 2022 to explore urban and wildfire effects. Sample locations and total organic carbon contents (OC) are available in the accompanying file OlowaluWatershedReef2022_compositions.csv. |
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Percentage of time sediment is mobile for May, 2010 - May, 2011 at select points in the Gulf of Maine south into the Middle Atlantic Bight (GMAINE_mobile_perc.SHP, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) ... |
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Percentage of time sediment is mobile for May, 2010 - May, 2011 at select points in the Middle Atlantic Bight (MAB_mobile_perc.SHP)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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Percentage of time sediment is mobile for May 2010 to May 2011 at select points in the Gulf of Mexico (GMEX_mobile_perc, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) ... |
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Percent sand and fines in suspended sediment from water samples from South San Francisco Bay, California, 2021
Water samples were collected in South San Francisco Bay adjacent to Whale’s Tail South marsh on three days from June through December 2021 to analyze for suspended-sediment concentration and the percent of sand and fines in suspended sediment. |
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Photographs of piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes photographs of 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind ... |
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Photographs of sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset includes photographs (linescan images) of sediment cores collected in southern Cascadia (offshore northern California) aboard the MV Bold Horizon in September-October 2019. |
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Photographs of vibracores collected during a Monterey Bay Aquarium Research Institute cruise in February 2019 offshore of south-central California (USGS FAN 2019-603-FA)
This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the ... |
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Photographs of vibracores collected during a Monterey Bay Aquarium Research Institute cruise in November 2019 offshore of south-central California (USGS FAN 2019-667-FA)
This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the ... |
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Photographs of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes photographs of sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Physics-based numerical circulation model outputs of ocean surface circulation during the 2010-2013 summer coral-spawning seasons in Maui Nui, Hawaii, USA
Ocean surface current results from a physics-based, 3-dimensional coupled ocean-atmosphere numerical model were generated to understand coral larval dispersal patterns in Maui Nui, Hawaii, USA. The model was used to simulate coral larval dispersal patterns from a number of existing State-managed reefs and large tracks of reefs with high coral coverage that might be good candidates for marine-protected areas (MPAs) during 8 spawning events during 2010-2013. The goal of this effort is to provide geophysical ... |
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Physics-based numerical model simulations of wave propagation over and around theoretical atoll and island morphologies for sea-level rise scenarios
Schematic atoll models with varying theoretical morphologies were used to evaluate the relative control of individual morphological parameters on alongshore transport gradients. Here we present physics-based numerical SWAN model results of incident wave transformations for a range of atoll and island morphologies and sea-level rise scenarios. Model results are presented in NetCDF format, accompanied by a README text file that lists the parameters used in each model run. These data accompany the following ... |
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Point based shorelines derived from global positioning system data with nearest WorldView shoreline distance for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Point shapefile of probability of shoreline change along the U.S. Atlantic Coast (ProbSLC_AtlanticData.shp)
During the 21st century, sea-level rise will have a wide range of effects on coastal environments, human development and infrastructure in coastal areas. Consequently there is a need to develop modeling or other analytical approaches that can be used to evaluate potential impacts to inform coastal management. This shapefile provides the data that were used to develop and evaluate the performance of a Bayesian network (BN) that was developed to predict long-term shoreline change associated with sea-level ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2012–2013
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Coast Guard Beach, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2014–2015
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Monomoy Island, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Polycyclic aromatic hydrocarbons (PAHs) in the San Lorenzo River, Santa Cruz, California, USA, from 2015 to 2016
Polycyclic aromatic hydrocarbons (PAHs) are reported for water samples collected from the San Lorenzo River water during the rainy seasons from 2015 to 2016. Samples were collected during 2-, 5- and close to 10 year flood events. |
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Polygon shapefiles attributed with morphometric information for barrier islands and spits located along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019
A suite of morphological metrics were derived from existing shoreline and elevation datasets for barrier islands and spits located along the north-slope coast of Alaska between Cape Beaufort and the U.S.-Canadian border. This dataset includes barrier polygons attributed with morphological metrics from five time periods: 1950s, 1980s, 2000s, 2010s, and 2020s. |
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Porewater chloride and sulfate concentrations from piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes concentrations chloride and sulfate in porewater from piston and gravity cores collected in September 2019 offshore of south-central California aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy ... |
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Post-Expedition Report for USGS T-3 Ice Island Heat Flow Measurements in the High Arctic Ocean, 1963-1973
In February 1963, the U.S. Geological Survey (USGS) began a study of heat flow in the Arctic Ocean Basin and acquired data at 356 sites in Canada Basin and Nautilus Basin and on Alpha-Mendeleev Ridge by the end of the project in 1973. The USGS heat flow and associated piston coring operations were conducted from a scientific station on the freely drifting T-3 Ice island (also known as Fletcher's Ice Island). The Naval Arctic Research Laboratory (NARL) had established T-3 as a drifting research station in ... |
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Post-Hurricane Florence Aerial Imagery: Cape Fear to Duck, North Carolina, October 6-8, 2018
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic change, and for understanding coastal vulnerability and ... |
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Post-Hurricane Florence Digital Elevation Models of coastal North Carolina
This data release presents structure-from-motion (SFM) products derived from aerial imagery surveys with precise Global Navigation Satellite System (GNSS) navigation data flown in a piloted fixed wing aircraft taken along the North Carolina coast in response to Hurricane Florence (available here https://coastal.er.usgs.gov/data-release/doi-P91KB9SF/). USGS researchers use the elevation models and orthorectified imagery to assess future coastal vulnerability, nesting habitats for wildlife, and provide data ... |
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Post-Hurricane Florence RGB averaged orthoimagery of coastal North Carolina
This data release presents structure-from-motion (SFM) products derived from aerial imagery surveys with precise Global Navigation Satellite System (GNSS) navigation data flown in a piloted fixed wing aircraft taken along the North Carolina coast in response to Hurricane Florence (available here https://coastal.er.usgs.gov/data-release/doi-P91KB9SF/). USGS researchers use the elevation models and orthorectified imagery to assess future coastal vulnerability, nesting habitats for wildlife, and provide data ... |
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Post-Hurricane Gustav coastal oblique aerial photographs collected from the Chandeleur Islands, Louisiana, to Isles Dernieres Barrier Islands Refuge, Louisiana, September 4, 2008
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 4, 2008, the USGS conducted an oblique aerial photographic survey from the Chandeleur Islands, Louisiana, to Isles Dernieres Barrier Islands Refuge, Louisiana, aboard a Beechcraft Super King Air 200 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted ... |
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Post-Hurricane Matthew coastal oblique aerial photographs collected from Port St. Lucie, Florida, to Kitty Hawk, North Carolina, October 13–15, 2016
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On October 13–15, 2016, the USGS conducted an oblique aerial photographic survey from Port St. Lucie, Florida, to Kitty Hawk, North Carolina, aboard a Cessna 182 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes ... |
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Preliminary estimates of forecasted shoreline positions for Florida and Georgia
During Hurricane Irma, Florida and Georgia experienced substantial impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses from hurricanes result in increased vulnerability of coastal regions, including densely populated areas. Erosion may put critical infrastructure at risk of future flooding and may cause economic loss. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program is working to assess shoreline erosion along the southeast U.S. ... |
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PROBZONES - Generalized 100- and 500-year flood zones for Seaside, Oregon, determined by probabilistic tsunami hazard analysis
PROBZONES is a generalized polygon layer outlining areas in the Seaside-Gearhart, Oregon, area subject to the 100-year and 500-year flood as determined by probabilistic tsunami hazard analysis (PTHA). |
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Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the American Samoa’s most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated American Samoan Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to ... |
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Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian Islands
This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the most populated Hawaiian Islands of Oahu, Molokai, Kauai, Maui, and Big Island. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Hawaiian Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves ... |
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Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Mariana Islands
This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the most populated Mariana Islands of Guam and Saipan. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Mariana Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 ... |
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Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of American Samoa's most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution ... |
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Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian Islands
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of the most populated Hawaiian Islands of Oahu, Molokai, Kauai, Maui, and Big Island. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 ... |
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Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Mariana Islands
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of the most populated Mariana Islands of Guam and Saipan. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution along these islands' ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for American Samoa (the islands of Tutuila, Ofu-Olosega, and Tau)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for American Samoa (the islands of Tutuila, Ofu-Olosega, and Tau). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Commonwealth of the Northern Mariana Islands (the islands of Saipan and Tinian)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for Commonwealth of the Northern Mariana Islands (the islands of Saipan and Tinian). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the State Florida (the Florida Peninsula and the Florida Keys)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the State of Hawaii (the islands of Hawaii, Kahoolawe, Kauai, Lanai, Maui, Molokai, Niihau, and Oahu)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the State of Hawaii (the islands of Hawaii, Kahoolawe, Kauai, Lanai, Maui, Molokai, Niihau, and Oahu). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of Guam
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Guam. There are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of the U.S. Virgin Islands (the islands of Saint Croix, Saint John, and Saint Thomas)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of the U.S. Virgin Islands (the islands of Saint Croix, Saint John, and Saint Thomas). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of ... |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the Commonwealth of Puerto Rico before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for Commonwealth of Puerto Rico. There are eight associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the pre-storm scenario (base) and the post-storm scenarios. |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the Commonwealth of Puerto Rico for current and potentially restored coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the Commonwealth of Puerto Rico. There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25). |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs
This part of the data release presents projected flooding extent polygon shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are eight associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the pre-storm scenario (base) and the post-storm scenarios. |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida for current and potentially restored coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25). |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the Territory of the U.S. Virgin Islands for current and potentially restored coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the Territory of the U.S. Virgin Islands. There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25). |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands
This data release provides flooding extent polygons (flood masks) and depth values (flood points) based on wave-driven total water levels for 22 locations within the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands. For each of the 22 locations there are eight associated flood mask polygons and flood depth point files: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100- ... |
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Projected flood water depths on Roi-Namur, Kwajalein Atoll, Republic of the Marshall Islands
Projected future wave-driven flooding depths on Roi-Namur Island on Kwajalein Atoll in the Republic of the Marshall Islands for a range of climate-change scenarios. This study utilized field data to calibrate oceanographic and hydrogeologic models, which were then used with climate-change and sea-level rise projections to explore the effects of sea-level rise and wave-driven flooding on atoll islands and their freshwater resources. The overall objective of this effort, due to the large uncertainty in ... |
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Projected groundwater emergence and shoaling along the North and South Carolina coasts
Groundwater emergence and shoaling extents are derived from water table depth GeoTIFFs, which are calculated as steady-state groundwater model heads subtracted from high-resolution topographic digital elevation model (DEM) land surface elevations. Results are provided as shapefiles of water table depth in specific depth ranges. |
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Projected groundwater emergence and shoaling along the Virginia, Georgia, and Florida coasts
Groundwater emergence and shoaling extents are derived from water table depth GeoTIFFs, which are calculated as steady-state groundwater model heads subtracted from high-resolution topographic digital elevation model (DEM) land surface elevations. Results are provided as shapefiles of water table depth in specific depth ranges. Similar modeled data for North Carolina and South Carolina are available from Barnard and others, 2023 at https://doi.org/10.5066/P9W91314. |
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Projected groundwater emergence and shoaling for coastal California using present-day and future sea-level rise scenarios
Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were ... |
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Projected groundwater emergence and shoaling in coastal areas around Puget Sound, Washington
Groundwater emergence and shoaling extents are derived from water table depth GeoTIFFs, which are calculated as steady-state groundwater model heads subtracted from high-resolution topographic digital elevation model (DEM) land surface elevations. Results are provided as shapefiles of water table depth in specific depth ranges. |
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Projected groundwater head along the North and South Carolina coasts
Seamless unconfined groundwater heads for U.S. coastal North and South Carolina groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (land surface less than approximately 10 m above mean sea level) areas. Steady-state MODFLOW groundwater flow models were used to obtain detailed (50-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea ... |
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Projected groundwater head along the Virginia, Georgia, and Florida coasts
Seamless unconfined groundwater heads for U.S. coastal Virginia, Georgia, and Florida (Atlantic and Gulf coast south of Sarasota) groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (land surface less than approximately 10 m above mean sea level) areas. Steady-state MODFLOW groundwater flow models were used to obtain detailed (50-meter-scale) predictions over large geographic scales (100s of kilometers) of ... |
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Projected groundwater head for coastal California using present-day and future sea-level rise scenarios
Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were ... |
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Projected groundwater head in coastal areas around Puget Sound, Washington
Seamless unconfined groundwater heads for coastal groundwater systems around Puget Sound (Washington State) were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was defined primarily by watershed boundaries. Steady-state MODFLOW groundwater flow models were used to obtain detailed (50-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) scenarios (0, 0.25, 0.5, 1, 1.5, 2, 2 ... |
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Projected open water seasons using four global climate models for 2011 to 2100 fronting Arey Lagoon and Barter Island, Alaska
Estimated start date, end date, and duration of open water at a location fronting Barter Island, Alaska derived from projected sea ice extents in 4 global climate models: MIROC5, BCC-CSM1.1, INM-CM4, and GFDL-ESM2M. Starting and ending dates are when sea ice retreated or is projected to retreat offshore by more than 80 kilometers fronting Barter Island. Projected coastal storm events were derived by downscaling atmospheric conditions of the RCP 4.5 climate scenario with the MIROC5 global climate model (GCM) ... |
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Projected water table depths along the North and South Carolina coasts
To predict water table depths, seamless groundwater heads for unconfined coastal North and South Carolina groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (land surface less than approximately 10 m above mean sea level) areas. Steady-state MODFLOW groundwater flow models were used to obtain detailed (50-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for ... |
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Projected water table depths along the Virginia, Georgia, and Florida coasts
To predict water table depths, seamless groundwater heads for unconfined coastal Virginia, Georgia, and Florida (Atlantic and Gulf coast south of Sarasota) groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (land surface less than approximately 10 m above mean sea level) areas. Steady-state MODFLOW groundwater flow models were used to obtain detailed (50-meter-scale) predictions over large geographic ... |
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Projected water table depths for coastal California using present-day and future sea-level rise scenarios
Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were ... |
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Projected water table depths in coastal areas around Puget Sound, Washington
To predict water table depths, seamless unconfined groundwater heads for coastal groundwater systems around Puget Sound (Washington State) were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was defined primarily by watershed boundaries. Steady-state MODFLOW groundwater flow models were used to obtain detailed (50-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) ... |
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Projections of coastal flood depths for the U.S. Atlantic coast
Projected depths from compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for the U.S. Atlantic coast for three states (Florida, Georgia, and Virginia). Projections were made using a system of numerical models driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a tropical cyclone database from U.S. Army Corp of Engineers. The resulting data are depths of projected flood hazards along the U.S. Atlantic ... |
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Projections of coastal flood hazards and flood potential for North Carolina and South Carolina
Projected impacts by compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for North Carolina and South Carolina. Accompanying uncertainty for each SLR and storm scenario, indicating total uncertainty from model processes and contributing datasets, are illustrated in maximum and minimum flood potential. As described by Nederhoff and others (2023), projections were made using a system of numerical models driven by output from Global Climate Models (GCMs) from the ... |
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Projections of coastal flood hazards and flood potential for the U.S. Atlantic coast
Projected impacts by compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for the U.S. Atlantic coast for three states (Florida, Georgia, and southern Virginia). Accompanying uncertainty for each SLR and storm scenario, indicating total uncertainty from model processes and contributing datasets, are illustrated in maximum and minimum flood potential. As described by Nederhoff and others (2023), projections were made using a system of numerical models driven by output ... |
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Projections of coastal flood velocities for Whatcom County, Northwest Washington State coast (2015-2100)
Projected flood velocities associated with compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are provided for Whatcom County, Washington, in a series of raster geotiff files. Projections were made using a system of numerical models with atmospheric forcing, tides, sea level position and stream discharge driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The resulting computed coastal flood velocities along the ... |
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Projections of coastal water depths for North Carolina and South Carolina
Projected water depths from compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for North Carolina and South Carolina. As described by Nederhoff and others (2023), projections were made using a system of numerical models driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a tropical cyclone database from U.S. Army Corp of Engineers. The resulting data are depths of projected flood hazards along the ... |
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Projections of compound floodwater depths for the lower Nooksack River and delta, western Washington State
Computed flood depths associated with the combined influence of sea level position, tides, storm surge, and streamflow under existing conditions and projected future higher sea level and peak stream runoff are provided for the lower (Reach 1) of the Nooksack River and delta in Whatcom County, western Washington State. The flood-depth projection data are provided in a series of raster geotiff files. Flood-depth projections were computed using a system of numerical models that accounted for projected changes ... |
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Projections of shoreline change for California due to 21st century sea-level rise
This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 ... |
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Projections of shoreline change of current and future (2005-2100) sea-level rise scenarios for North Carolina and South Carolina
This dataset contains projections of shoreline change and uncertainty bands for future scenarios of sea-level rise (SLR). Scenarios include 25, 50, 75, 100, 150, 200, and 300 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2005, in accordance with recent SLR projections and guidance from the National Oceanic and Atmospheric Administration (NOAA; see process steps). Projections were made using the Coastal Storm Modeling System - Coastal One-line ... |
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Projections of shoreline change of current and future (2005-2100) sea-level rise scenarios for the U.S. Atlantic Coast
This dataset contains projections of shoreline change and uncertainty bands for future scenarios of sea-level rise (SLR). Scenarios include 25, 50, 75, 100, 150, 200, and 300 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2005, in accordance with recent SLR projections and guidance from the National Oceanic and Atmospheric Administration (NOAA; see process steps).Projections were made using the Coastal Storm Modeling System - Coastal One-line ... |
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Quaternary faults offshore of California
A comprehensive map of Quaternary faults has been generated for offshore of California. The Quaternary fault map includes mapped geometries and attribute information for offshore fault systems located in California State and Federal waters. The polyline shapefile has been compiled from previously published mapping where relatively dense, high-resolution marine geophysical data exist. The data are also available in kml format and are accompanied by a pdf containing citations for the compiled source data. In ... |
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Radiocarbon age data from sediment cores collected offshore southern Cascadia, during field activity 2019-643-FA
This dataset presents radiocarbon data from 87 samples from sediment cores collected in southern Cascadia (offshore northern California) aboard the M/V Bold Horizon in September-October 2019. Sample ages were determined by the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility and the W.M. Keck Carbon Cycle Accelerator Mass Spectrometry (KCCAMS) facility at the University of California, Irvine (UCI). |
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Radiocarbon age data from vibracores collected offshore central California, during field activity 2019-651-FA (ver 2.0, August 2023)
This dataset includes radiocarbon age data from sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Radiocarbon data from coastal wetlands on the Hawaiian islands of Kaua'i, O'ahu, and Hawai'i
This portion of the data release presents radiocarbon age data from 66 samples collected from Anahola Valley (Kaua'i), Kahana Valley (O'ahu), and Pololu Valley (Hawai'i). Sample ages were determined by the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility. The data are provided in a comma-delimited spreadsheet (.csv). |
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Radiocarbon sample data and calibrated ages of sediment core collected in 2009 offshore from Palos Verdes, California
This part of the data release is a spreadsheet including radiocarbon sample information and calibrated ages of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http:/ ... |
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Radiocarbon sample data and calibrated ages of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California
This part of the data release is a spreadsheet including radiocarbon sample information and calibrated ages of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s ... |
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Radiogenic heat content for selected cores recovered during T-3 Ice Island heat flow operations in the Arctic Ocean, 1963-74 (ver. 1.1, December 2022)
The T-3 (Fletcher's) Ice Island in the Arctic Ocean was the site of a scientific research station re-established by the Naval Arctic Research Laboratory starting in 1962. Starting in 1963, the USGS acquired marine heat flow data and coincident sediment cores at sites in Canada Basin, Nautilus Basin, Mendeleev Ridge, and Alpha Ridge as the ice island drifted in the Amerasian Basin. Radiogenic heat content in sediments can be an important contributor to measured heat flow. The USGS therefore measured ... |
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Radium and Radon Radioisotope Activity Data from Samples Collected Between May 2019 and September 2020 Along the West Florida Shelf (Amberjack and Green Banana Blue Holes)
Relict karstic features or sinkholes, often referred to as blue holes, are common features along continental shelves that are underlain by carbonate rich sediments and/or rocks. Several of these features occur along the west-Florida shelf within the Gulf of Mexico, including the two mentioned in Vargas and others (2022): Amberjack Hole and Green Banana Sink (hereafter referred to as Green Banana). Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) ... |
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Rain measurements in and near the CZU Lightning Complex Fire area, Santa Cruz Mountains, California, 2020 to 2021.
Rainfall measurements were collected in and near the CZU Lightning Complex Fire (hereafter, "CZU Fire") burn area, Santa Cruz Mountains, California. The CZU Fire ignited in the Santa Cruz Mountains, California, on August 16, 2020. By the time of full containment on September 22, 2020, the fire had burned 350 km2 (86,510 acres) in Santa Cruz and San Mateo Counties. The U.S. Geological Survey (USGS) installed four rain gages in and near the CZU Fire burn area to measure rainfall during the post-fire wet ... |
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Rain measurements in and near the CZU Lightning Complex Fire area, Santa Cruz Mountains, California, 2021 to 2022
Rainfall measurements were collected in and near the CZU Lightning Complex Fire (hereafter, "CZU Fire") burn area, Santa Cruz Mountains, California. The CZU Fire ignited in the Santa Cruz Mountains, California, on August 16, 2020. By the time of full containment on September 22, 2020, the fire had burned 350 km2 (86,510 acres) in Santa Cruz and San Mateo Counties. The U.S. Geological Survey (USGS) installed four rain gages in and near the CZU Fire burn area to measure rainfall during two post-fire wet ... |
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Rain measurements in and near the Dolan Fire Area, Los Padres National Forest, California, 2022 to 2023
Rainfall measurements were collected in and near the Dolan Fire burn area, Los Padres National Forest, California. The CZU Fire ignited in Los Padres National Forest, California, on August 18, 2020. By the time of full containment on December 31, 2020, the fire had burned 518 km2 (128,050 acres) in Monterey County. The U.S. Geological Survey (USGS) installed seven rain gages in and near the Dolan Fire burn area in October 2021 to measure rainfall during two post-fire wet seasons. This data release contains ... |
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Rain measurements in Santa Cruz County, California, January 2023
Rain gages were deployed temporarily at four sites in Santa Cruz County, California, during a series of atmospheric-river storms that delivered unusually large amounts of rain in January 2023. Data collection focused on the San Lorenzo River, and include three locations in the San Lorenzo Valley (in Boulder Creek along Hilton Drive, in Felton near Glengarry Road, and in Scotts Valley along Green Valley Road), as well as one site within the city of Santa Cruz, on Darwin Street. These data are provided to ... |
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Rain measurements in the Dolan Fire Area, Los Padres National Forest, California, 2021 to 2022
Rainfall measurements were collected in and near the Dolan Fire burn area, Los Padres National Forest, California. The Dolan Fire ignited on August 18, 2020. By the time of full containment on December 31, 2020, the fire had burned 518 km2 (128,050 acres) in Monterey County. Post-fire debris flows occurred in many watersheds burned by the Dolan Fire during the first post-fire wet season, in winter 2021. The U.S. Geological Survey (USGS) installed seven rain gages within the Dolan Fire burn area in October ... |
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Raster image of exposure potential to environmental health stressors in Edwin B. Forsythe National Wildlife Refuge (32-bit GeoTIFF)
Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays ... |
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Raster image of mean tidal range in the Edwin B. Forsythe National Wildlife Refuge, New Jersey (32-bit GeoTIFF)
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New ... |
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Rate of shoreline change of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific ... |
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Rate of shoreline change of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U ... |
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Rate of shoreline change of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. ... |
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Raw computed tomography (CT) images of sediment cores collected in 2009 offshore from Palos Verdes, California
This part of the data release includes raw computed tomography (CT) images of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info ... |
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RBR sensor pressure and tidal data for two sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from April 2019 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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RBR sensor wave data for two sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from April 2019 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Recurrence interval of sediment mobility at select points in the Gulf of Maine south into the Middle Atlantic Bight for May, 2010 - May, 2011 (GMAINE_mobile_freq, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) ... |
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Recurrence interval of sediment mobility at select points in the Gulf of Mexico for May 2010 to May 2011 (GMEX_mobile_freq, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) ... |
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Recurrence interval of sediment mobility at select points in the Middle Atlantic Bight for May, 2010 - May, 2011 (MAB_mobile_freq_v1_1.SHP, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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Reef-census data from Buck Island Reef
In July of 2016, Florida Institute of Technology researchers, in collaboration with the U.S. Geological Survey (USGS), conducted reef-census surveys at 54 sites around Buck Island Reef National Monument, St. Croix, U.S. Virgin Islands. The sites are divided across two reef sectors (North and South) and three reef habitats (fore reef, reef crest, and back reef). The surveys provided data on the percent coverage of corals and other benthic taxa, and abundance of bioeroding parrotfishes and urchins. |
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Reference baselines used to extract shorelines for the West Coast of the United States
This data release contains reference baselines for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each ... |
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Revised (v. 1.1) Interpretation of Sedimentary Environments Based on National Oceanic and Atmospheric Administration (NOAA) Surveys H12009, H12010, H12011, H12015, H12033, H12137, and H12139, the adjacent 2011 NOAA survey H12299, and Verification Data from U.S. Geological Survey (USGS) Cruise 2011-006-FA Offshore in Block Island Sound (BISOUND_SEDENV_v1.1.SHP, Geographic, WGS 84)
The USGS, in cooperation with NOAA, is producing detailed maps of the seafloor off southern New England. The current phase of this cooperative research program is directed toward analyzing how bathymetric relief relates to the distribution of sedimentary environments and benthic communities. As part of this program, digital terrain models (DTMs) from bathymetry collected as part of NOAA's hydrographic charting activities are converted into ESRI raster grids and imagery, verified with bottom sampling and ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2019-08-30 to 2019-09-02, Pre-Hurricane Dorian
Orthoimages were created from aerial imagery collected between August 30 and September 2, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions prior to Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface before Hurricane Dorian and were created to document ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
RGB-averaged ortho products were created from aerial imagery collected between September 8 and 13, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2020-02-08 to 2020-02-09
RGB-averaged orthoimages were created from aerial imagery collected February 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RGB-averaged orthoimages were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RGB-averaged orthoimages help researchers document inter-annual changes in shoreline position and coastal morphology in ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2020-05-08 to 2020-05-09
RGB-averaged orthoimages were created from aerial imagery collected May 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RGB-averaged orthoimages were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RGB-averaged orthoimages help researchers document inter-annual changes in shoreline position and coastal morphology in ... |
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RGB-averaged orthoimagery of coastal North Carolina, on 2019-10-11, one-month post-Hurricane Dorian
RGB-averaged orthoimages were created from aerial imagery collected on October 11, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions one-month after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to document ... |
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RGB-averaged orthoimagery of coastal North Carolina, on 2019-11-26, two-months Post-Hurricane Dorian
RGB-averaged orthoimages were created from aerial imagery collected on November 26, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions two-months after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to document ... |
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Salinity_all_frontal: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_frontal_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_tropical: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_tropical_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_GBI_frontal: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_GBI_frontal_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_GBI_tropical: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_GBI_tropical_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_na_frontal: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_na_frontal_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_na_tropical: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_na_tropical_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salish Sea water level hindcast simulations: 1985-2015
Simulatations of water levels in the Salish Sea for a continuous hindcast of the period October 1, 1985, to September 30, 2015 were conducted to evaluate the utility and skill of a sea-level anomaly predictor and to develop extreme water level estimates accounting for decadal climate variability. The model accounts for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Comparison of modeled and measured water levels showed the ... |
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Salish Sea water level simulation projections: 2016-2099
Simulations of the period 2016-2099 were conducted using the Salish Sea hydrodynamic model to evaluate extreme water levels associated with anticipated changes in sea level and climate forcing. The model projections accounting for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Dynamically downscaled Weather Research and Forecasting (WRF) CMIP5 GFDL wind and atmospheric pressure fields were prescribed over the model open ... |
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Salish Sea water level validation simulations: 2017-2020
Simulations of water levels in the Salish Sea over the period October 1, 2016 to September 30, 2020 were conducted to validate the Salish Sea hydrodynamic model. The model accounts for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Comparison of modeled and measured water levels showed the model predicts extreme water levels at NOAA and USGS tide gage stations within 0.15 m. Model inputs and outputs of time-series forcing and ... |
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sand_ero - Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_oha - Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii
Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_sea - Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_sfl - Stream Flooding Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Stream Flooding Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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SandSnap grain-size analysis and photos from North Core Banks, NC in October 2022
These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash, overwash, and pond formation were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote ... |
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sand_stm - Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_tsu - Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_vol - Volcanic and Seismic Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Volcanic and Seismic Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_wav - High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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Satellite-derived shorelines for North Carolina and South Carolina (1984-2021)
This dataset contains shoreline positions derived from available Landsat satellite imagery for North Carolina and South Carolina for the time period of 1984 to 2021. Positions were determined using CoastSat (Vos and others, 2019a and 2019b), an open-source mapping toolbox, was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. To understand shoreline evolution in complex environments and operate long-term simulations illustrating potential shoreline positions in the next ... |
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Satellite-derived shorelines for the U.S. Atlantic coast (1984-2021)
This dataset contains shoreline positions derived from available Landsat satellite imagery for five states (Delaware, Maryland, Viginia, Georgia, and Florida) along the U.S. Atlantic coast for the time period 1984 to 2021. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in KMZ format. Significant uncertainty is associated with the locations of shorelines in ... |
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SC Bias Feature – Feature class containing South Carolina proxy-datum bias information to be used in the Digital Shoreline Analysis System
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Scenarios_Grid
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2008
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites ... |
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Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites ... |
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Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites ... |
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Seafloor or Short Core Hydrate Locations in the Gulf of Mexico (HYDRATES.SHP)
This GIS overlay is a component of the U.S. Geological Survey, Woods Hole Field Center's, Gulf of Mexico ArcView GIS database. The Gulf of Mexico GIS database is intended to organize and display USGS held data and provide on-line (WWW) access to the data and/or metadata. Additional GIS overlays downloaded from the WWW, such as the one described here, are also included in the Gulf of Mexico ArcView GIS database. Attempts to properly attribute such GIS overlays with the WWW address and data compilers has been ... |
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Sea-surface water temperature time-series data collected in 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, ... |
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Seawater carbonate chemistry, Kahekili, west Maui
Time-series of seawater carbonate chemistry variables, including salinity, dissolved inorganic nutrients, pH, total alkalinity, and dissolved inorganic carbon from sites along Kahekili Beach Park, west Maui near submarine groundwater seeps and living coral reefs. Samples for seawater were collected by pumping bottom water from the seafloor using a peristaltic pump and collecting discrete water samples every 4-hrs over a 6-day period. |
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Sediment and Radiochemical Characteristics from Shore-Perpendicular Estuarine and Marsh Transects in the Grand Bay National Estuarine Research Reserve, Mississippi
To examine sediment transport and provenance between a marsh and estuary, surface sediments were collected along two transects in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each shore-perpendicular transect consisted of fifteen surface samples, collected every 2.5 meters (m) from 10-m out into the estuary to 25-m into the marsh from the shoreline. Sediment samples were analyzed for their physical and radiochemical properties or signatures. Sediment samples were collected ... |
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Sedimentary Environment Map of Long Island Sound
Long Island Sound is one of the largest estuaries along the Atlantic coast of the United States. It is a glacially produced, semi-enclosed, northeast-southwest-trending embayment, which is 150 km long and 30 km across at its widest point. Its mean water depth is approximately 24 m. The eastern end of the Sound opens to the Atlantic Ocean through several large passages between islands, whereas the western end is connected to New York Harbor through a narrow tidal strait. Long Island Sound abuts the New York ... |
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Sedimentary Environments of NOAA H11310 Sidescan Sonar Mosaic in Central Narragansett Bay (H11310SEDENVIRONS.SHP)
The United States Geological Survey (USGS) is working cooperatively with the National Oceanic and Atmospheric Association (NOAA) to interpret the surficial geology in estuaries along the coast of the northeastern United States. The purpose of our present study is to interpret the distributions of surficial sediments and sedimentary environments in an area of Narragansett Bay using sidescan sonar imagery, high-resolution bathymetry, and sediment data. The mosaic presented herein covers an area of the sea ... |
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Sedimentary Environments of the Sea Floor off Eastern Cape Cod, Massachusetts (CC_ENVIRON.SHP, Geographic, WGS84)
This data set includes the sedimentary environments for the sea floor offshore of northern and eastern Cape Cod, Massachusetts. This interpretation is based on data collected with a multibeam sea floor mapping system during USGS survey 98015, conducted November 9 - 25, 1998 and on data collected with a bottom sampling and photographic system during USGS survey 04011, conducted during May and June, 2004. |
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Sediment deposition in the Elwha River estuary, Washington, measured on rod surface elevation tables (RSETs) from 2011 to 2014
This portion of the data release presents sediment deposition in the estuary as measured using rod surface elevation tables (RSETs) at fifteen locations throughout the Elwha River estuary, Washington, from August 2011 to June 2014 (no associated USGS Field Activities numbers because data were collected predominantly by biologists from the Lower Elwha Klallam Tribe). The locations of the RSETs were determined with a hand-held global positioning system (GPS). We measured sediment deposition from 2011 to 2013 ... |
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Sediment grain size and digital image calibration parameters from the mouth of the Columbia River, Oregon and Washington, 2014
This dataset includes 63 still images extracted from digital video imagery of sediment grab samples, along with laboratory grain size analysis of the sediment grab samples, taken from the mouth of the Columbia River, OR and WA, USA. Digital video was collected in September 2014 in the mouth of the Columbia River, USA, as part of the U.S. Geological Survey Coastal and Marine Geology Program contribution to the Office of Naval Research funded River and Inlets Dynamics experiment (RIVET II). Still images were ... |
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Sediment grain-size data from sand augers collected in March/April and October 2014 from Assateague Island, Maryland (U.S. Geological Survey Field Activity Numbers [FAN] 2014-301-FA and 2014-322-FA)
The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. Although physical changes caused by tropical and extratropical storms to the sandy beaches and dunes fronting barrier islands are generally well documented, the interaction between sandy shoreline erosion and overwash with the back-barrier wetland and estuarine environments is poorly ... |
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Sediment grain-size distributions from cores collected in the Salmon River estuary, Oregon
This portion of the data release presents sediment grain-size data from cores and surface samples collected from the Salmon River estuary in 2017 and 2018. In total, 60 samples were collected from 18 sites containing sandy sediment from the circa 1700 CE tsunami deposit, two sites with post-1700 CE silt, and eight modern surface sample sites. The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The ... |
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Sediment grain-size distributions from vibracores collected in Searsville Lake, Jasper Ridge Biological Preserve, Stanford, California
This portion of the data release presents sediment grain-size data from vibracores collected from Searsville Lake, Jasper Ridge Biological Preserve, Stanford, California in October 2018 (USGS Field Activity 2018-682-FA). In total, 36 samples were subsampled from two vibracores: JRBP2018-VC01A and JRBP2018-VC01B. The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The grain-size data are provided in a ... |
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Sediment grain-size distributions of three carbonate sand layers in Anahola Valley, Kaua'i, Hawai'i (ver. 2.0, July 2023)
This portion of the data release presents sediment grain-size data from samples collected from Anahola Valley, Kaua`i, Hawai`i in November, 2015 (USGS Field Activity 2015-671-FA). 63 sand and mud samples were taken from sediment cores that were collected using a Russian corer (a hand-held, side-filling peat auger) from two site locations. Site locations were determined using a hand-held global navigation satellite system, GNSS. The grain-size distributions of samples were determined using standard ... |
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Sediment grain size from the Elwha River, Washington, 2006 to 2017
The grain size of sediment on the riverbed was measured during 20 surveys on the Elwha River, Washington, between 2006 and 2017. Most data were collected along the same transects where channel topography was measured (see related child item in this data release: https://www.sciencebase.gov/catalog/item/5a989288e4b06990606de04b). Measurements of sediment ranging from medium sand to boulders were made using the CobbleCam digital photographic technique (Warrick and others, 2009), which uses a calibrated ... |
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Sediment grain size in the Elwha River estuary, Washington, from 2013 and 2014.
This portion of the data release presents sediment grain-size data from samples collected in the Elwha River estuary, Washington, in July 2013 and June 2014 (USGS Field Activities L-15-13-PS and 2014-628-FA). Surface sediment was collected from one location in 2013 and five locations in 2014 using a using a push core. The locations of grab samples were determined with a hand-held global positioning system (GPS). The cores were split into one- to three-centimeter sections. The grain-size distributions of ... |
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Sediment sample analysis data from ponds to the beach on North Core Banks, NC in October 2022
These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash, overwash, and pond formation were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote ... |
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Sediment size distributions from San Pablo Bay and China Camp Marsh, California
As part of the hydrodynamic and sediment transport investigations in San Pablo Bay and China Camp Marsh, California, particle size distributions of bed sediments were measured at most instrumented stations and are presented in a comma-delimited values spreadsheet. This portion of the data release presents San Pablo Bay and China Camp Marsh sediment particle size distributions from samples collected during multiple instrument deployments. Users are advised to check the data carefully for sampling time, ... |
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September 2006 Mississippi and Alabama USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 USGS Mississippi ... |
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September 2006 Post-Hurricane Wilma Florida U.S. Army Corps of Engineers Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Post-Hurricane ... |
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September 2007 Northern Gulf of Mexico USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Northern Gulf of ... |
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September 2007 Southwest Florida Division of Emergency Management Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Southwest Florida ... |
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Shapefile of Historical Bathymetric Soundings for Mississippi and Alabama Derived from National Ocean Service (NOS) Hydrographic Sheets
Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal ... |
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Shapefile of Historical shorelines for Fire Island and Great South Bay, New York, derived from previously unpublished National Oceanic and Atmospheric Administration (NOAA) 1834-1875 topographic sheets
Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline website (https://shoreline.noaa.gov/data/datasheets/t ... |
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Shoreline change data along the coast of California from 2015 to 2016
This dataset contains shoreline change measurements for sandy beaches along the coast of California over the 2015/2016 El Nino winter season. Mean high water (MHW) shorelines were extracted from Light Detection and Ranging (LiDAR) digital elevation models from the fall of 2015 and the spring of 2016 using the ArcGIS smoothed contour method. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. ... |
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Shoreline change rates along the coast of California from 1998 to 2016
This dataset contains California shoreline change rates derived from mean high water (MHW) shorelines from 1998 (in Central and Southern California) and 2002 (in Northern California) to 2016. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. The operational MHW line was extracted from Light Detection and Ranging (LiDAR) digital elevation models (DEMs) using the ArcGIS smoothed contour method ... |
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Shoreline Change Rates for Barnegat and Great Bay, NJ: 1839 to 2012 (ver 1.1, December 2017)
This dataset represents shoreline change rates for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1839 to 2012. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Department of Environmental Protection (NJDEP). Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline change can be used for ... |
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Shoreline change rates for the coast of Puerto Rico's main island calculated using the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Shoreline change rates for the islands of Vieques and Culebra, Puerto Rico, calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Shoreline change rates in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey
Monitoring shoreline change is of interest in many coastal areas because it enables quantification of land loss over time. Evolution of shoreline position is determined by the balance between erosion and accretion along the coast. In the case of salt marshes, erosion along the water boundary causes a loss of ecosystem services, such as habitat provision, carbon storage, and wave attenuation. In terms of vulnerability, higher shoreline erosion rates indicate higher vulnerability. This dataset ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Coast Guard Beach, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Monomoy Island, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Shoreline intersects for the coast of Puerto Rico's main island generated by the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
Info |
Shoreline intersects for the islands of Vieques and Culebra, Puerto Rico, calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Shorelines Derived from Continuous Video-Imagery at the NASA-Kennedy Space Center, Florida From August 2011 to July 2012
In 2010, a video camera was installed near the northern boundary of the National Aeronautics and Space Administration-Kennedy Space Center (NASA-KSC) property along the Atlantic coast of Florida. A region extending 1 kilometer (km) to the south of the camera was established as the region of interest for the video image observations. During every daylight hour of camera operation from August 8, 2011 to July 24, 2012, a time exposure (timex) image product was created by averaging pixel color intensity for all ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (dates_meta.txt)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 268 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Individual Dates) is a dataset consisting of 223 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 254 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 271 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 280 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines for Barnegat and Great Bay, NJ: 1839 to 2012 (ver 1.1, December 2017)
This data set represents vector shorelines for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1839 to 2012. Data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Department of Environmental Protection (NJDEP). Shorelines were obtained from the original provider and merged into a single file in order to conduct shoreline change analysis for the open-ocean and estuarine shorelines ... |
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Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Shorelines for Vieques, Culebra, and the main island of Puerto Rico from the 1900s to 2018 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Shorelines from 1948 to 2016 for the north coast of Alaska, Icy Cape to Cape Prince Wales used in shoreline change analysis
This dataset includes shorelines that span 68 years, from 1948 to 2016, for the north coast of Alaska from Icy Cape to Cape Prince of Wales. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)) and aerial orthophotographs (U.S. Geological Survey (USGS) and Alaska High Altitude Photography (AHAP)). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These ... |
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Shorelines_Oct2012_Sep2016.shp: Fire Island, NY pre and post storm shoreline data from October 2012 to September 2016
Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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Shorelines_Oct2012_Sept2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This shapefile consists of Fire Island, NY pre- and post-storm shoreline data collected from October 2012 to September 2014. This dataset contains 13 Mean High Water (MHW) shorelines for Fire Island, NY (A total of 15 dates, where two shorelines were collected over multiple days). Prior to and following Hurricane Sandy in October, 2012, continuous alongshore DGPS data were collected to assess the positional changes of the shoreline (MHW - 0.46 m NAVD88) and the upper portion of the beach. Over the course of ... |
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Shorelines of the Alabama coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Cape Cod coastal region from Provincetown to the southern end of Monomoy Island, Massachusetts, used in shoreline change analysis (CapeCod_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) used in shoreline change analysis
This dataset includes shorelines from 63 years ranging from 1947 to 2010 for the north coast of Alaska between the Hulahula River and the Colville River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), Conoco-Philips (CP), British Petroleum Alaska (BPXA)), satellite imagery (State of Alaska), and lidar elevation data (USGS). ... |
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Shorelines of the central North Carolina (NCcentral) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Delmarva North coastal region from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia, used in shoreline change analysis (DelmarvaN_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Delmarva South and Southern Virginia coastal region from Wallops Island, Virginia to the Virginia/North Carolina border, used in shoreline change analysis (DelmarvaS_SVA_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Eastern Beaufort Sea, Alaska coastal region (U.S. Canadian Border to the Hulahula River) used in shoreline change analysis
This dataset includes shorelines from 63 years ranging from 1947 to 2010 for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), satellite imagery (U.S. Fish and Wildlife Service (USFWS), State of Alaska), and lidar elevation data (USGS). ... |
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Shorelines of the Eastern Chukchi Sea, Alaska coastal region (Point Barrow to Icy Cape) used in shoreline change analysis
This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between Point Barrow and Icy Cape. Shorelines were compiled from topographic survey sheets and Nautical Charts (T-sheet, Nautical Chart; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), satellite imagery (State of Alaska), and lidar elevation data (USGS). Historical shoreline positions ... |
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Shorelines of the Florida east coast (FLec) coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Shorelines of the Florida north (FLnorth) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Florida panhandle (FLph) coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Shorelines of the Florida west coast (FLwc) coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Shorelines of the Florida west (FLwest) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Georgia coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Shorelines of the Georgia (GA) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Greater Boston coastal region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts, used in shoreline change analysis (GreaterBoston_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Long Island coastal region used in shoreline change analysis for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Louisiana coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Massachusetts Islands coastal region including Martha's Vineyard and Nantucket, used in shoreline change analysis (MA_Islands_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Mississippi coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the New England North coastal region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts, used in shoreline change analysis (NewEnglandN_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the New England South coastal region used in shoreline change analysis from Dartmouth, Massachusetts to Napatree Point, Rhode Island (NewEnglandS_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the New Jersey North coastal region used in shoreline change analysis from Sandy Hook to Little Egg Inlet, New Jersey (NewJerseyN_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the New Jersey South coastal region used in shoreline change analysis from Little Egg Inlet to Cape May, New Jersey (NewJerseyS_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the northeastern Florida (FLne) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the northern North Carolina (NCnorth) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Oregon coastal region used in shoreline change analysis (OR_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the South Carolina (SC) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the southeastern Florida (FLse) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the southern North Carolina (NCsouth) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Texas east (TXeast) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Texas west (TXwest) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Washington coastal region used in shoreline change analysis (WA_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Western Beaufort Sea, Alaska coastal region (Colville River to Point Barrow) used in shoreline change analysis
This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between the Colville River and Point Barrow. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and lidar elevation data(USGS). Historical shoreline positions serve as easily understood features that can be used to describe ... |
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Shorelines of the western North Carolina (NCwest) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines used to calculate shoreline change statistics Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the Buzzards Bay coastal region of Massachusetts from Nobska Point in Woods Hole to Westport at the Rhode Island border (BuzzardsBay_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the Elizabeth Islands coastal region of Massachusetts from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the Martha's Vineyard coastal region including Vineyard Sound, Nantucket Sound, and the Atlantic Ocean-facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the North Shore A coastal region from North Salisbury at the New Hampshire border to the Annisquam River in Gloucester (NorthShoreA_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the North Shore B coastal region from the Annisquam River in Gloucester to the west side of Deer Island in Boston Harbor (NorthShoreB_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island, Massachusetts (OuterCapeCod_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the South Cape Cod coastal region of Massachusetts from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from within the Nantucket coastal region including the Nantucket Sound and Atlantic Ocean-facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shore Proximal Marsh Sediment Deposition and Ancillary Data From Grand Bay National Estuarine Research Reserve, Mississippi: grain size analysis
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Shore proximal sediment deposition in coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi: net sedimentation tile datasets from July 2018 to January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Shore proximal sediment deposition in coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi: net sedimentation tile datasets from October 2016 to October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Short-term shoreline change rates for Rincon, Puerto Rico 1994-2006 (st_transects.shp)
The 8 km of shoreline from Punta Higüero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2006. Thirteen historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. Shoreline vectors represent the high water line at the time of the survey. |
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Short-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Florida panhandle (FLph) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Florida west coast (FLwc) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Georgia coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Virginia coastal region using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Short-term shoreline change rate transects for the South Carolina coastal region using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami elevation model of Half Moon Bay, California
A high-resolution raster dataset of simulated maximum tsunami elevations in Half Moon Bay, California, based on the Science Application for Risk Reduction (SAFRR) tsunami scenario |
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Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami elevation model of Oakland/Alameda, California
A high-resolution raster dataset of simulated maximum tsunami elevations in the Oakland and Alameda area of California based on the Science Application for Risk Reduction (SAFRR) tsunami scenario |
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Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami velocity model of Half Moon Bay, California
A high-resolution raster dataset of simulated maximum tsunami velocities in Half Moon Bay, California, based on the Science Application for Risk Reduction (SAFRR) tsunami scenario. |
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Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami velocity model of Oakland/Alameda, California
A high-resolution raster dataset of simulated maximum tsunami velocities in the Oakland and Alameda area of California based on the Science Application for Risk Reduction (SAFRR) tsunami scenario. |
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Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami velocity model of Santa Cruz, California
A high-resolution raster dataset of simulated maximum tsunami velocities in Santa Cruz, California, based on the Science Application for Risk Reduction (SAFRR) tsunami scenario. |
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Single-Beam Bathymetry Data Collected in March 2021 from Grand Bay and Point Aux Chenes Bay, Mississippi/Alabama
Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) in St. Petersburg, Florida, conducted a bathymetric survey of Point Aux Chenes Bay and a small portion of Grand Bay, Mississippi/Alabama, from March 3-6, 2021. Efforts were supported by the Estuarine and MaRsh Geology project (EMRG), and the data described will provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, and recent and long-term ... |
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Site description and associated GPS data collected at eleven study sites within the Grand Bay National Estuarine Research Reserve in Mississippi
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Slope Values Across Marsh-Forest Boundary in Chesapeake Bay Region, USA
The marsh-forest boundary in the Chesapeake Bay was determined by geoprocessing high-resolution (1 square meter) land use and land cover data sets. Perpendicular transects were cast at standard intervals (30 meters) along the boundary within a GIS by repurposing the Digital Shoreline Analysis System (DSAS) Version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. Average and maximum slope values were assigned to each transect from surface elevation data. The same values were also provided as ... |
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Snap Raster used to create interpolated digital elevation models (DEMs) in the nearshore around Ship, Horn, and Petit Bois Islands, Mississippi: 1916 to 1920, 2008 to 2009 and 2016
To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. This USGS data release includes three digital elevation models (DEMs) for 1916 to 1920, 2008 ... |
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SOCAL1852_1889 - Vectorized Shoreline of Southern California Derived from 1852-1889 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL1920_1934 - Vectorized Shoreline of Southern California Derived from 1920-1934 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL_1971_1976 - Vectorized Shoreline of Southern California Derived from 1971-1976 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL_1998 - Vectorized Shoreline of Southern California Derived from 1998 Lidar Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL_BASELINE - Offshore Baseline for Southern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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SOCAL_BIASVALUES - Southern California Shoreline Bias Values
The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS ... |
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SOCAL_INTERSECTS_LT - Long-Term Transect-Shoreline Intersection Points for Southern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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SOCAL_INTERSECTS_ST - Short-Term Transect-Shoreline Intersection Points for Southern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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SOCAL_TRANSECTS_LT - Long-Term Shoreline Change Rates for Southern California Generated at a 50m Transect Spacing, 1852-1998
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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SOCAL_TRANSECTS_ST - Short-Term Shoreline Change Rates for Southern California Generated at a 50m Transect Spacing, 1971-1998
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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Sonde data of continuous surface water flow-through system for the West Florida Shelf: USGS Cruise 11BHM01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, ... |
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Sonde data of continuous surface water flow-through system for the West Florida Shelf: USGS Cruise 11BHM02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25-30, ... |
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Sonde data of continuous surface water flow-through system for the West Florida Shelf: USGS Cruise 11BHM03
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Sonde data of continuous surface water flow-through system for the West Florida Shelf: USGS Cruise 11BHM04
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Sonde data of continuous surface water flow-through system for the West Florida Shelf: USGS Cruise 11CEV02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred February 17 ... |
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Sonde data of continuous surface water flow-through system for the West Florida Shelf: USGS Cruise 11CEV02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred January 3-7, ... |
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Sonde data to characterize physical and chemical properties of the Cenote Bang, a component of the Ox Bel Ha cave network within the subterranean estuary coastal aquifer of the Yucatan Peninsula, from December 2013 to January 2016
Subterranean estuaries extend inland into density-stratified coastal carbonate aquifers that contain a surprising diversity of endemic animals (mostly crustaceans) within a highly oligotrophic environment. How complex ecosystems thrive in this globally-distributed, cryptic habitat (termed anchialine) is poorly understood. The northeastern margin of the Yucatan Peninsula contains over 250 km of mapped, diver-accessible caves passages where previous studies have suggested chemoautotrophic processes are the ... |
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Sonde data to characterize physical and chemical water column properties of flooded caves (Ox Bel Ha and Cenote Crustacea) within the coastal aquifer of the Yucatan Peninsula, Quintana Roo, from December 2013 to January 2015
Natural cave passages penetrating coastal aquifers in the Yucatan Peninsula (Quintana Roo, Mexico) were accessed to investigate how regional meteorology and hydrology control dissolved organic carbon and methane dynamics in karst subterranean estuaries, the region of aquifers where fresh and saline waters mix. Three field trips were carried out in December 2013, August 2014, and January 2015 to obtain 1) physicochemical and 2) geochemical data from the water column and 3) temporal records of water chemistry ... |
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South Carolina Coastal Erosion Study Data Report for Observations : October 2003 - April 2004
Oceanographic observations have been made at nine locations in Long Bay, South Carolina from October 2003 through April 2004. These sites are centered around a shore-oblique sand feature that is approximately 10 km long, 2 km wide, and in excess of 3 m thick. The observations were collected through a collaborative effort with the U.S. Geological Survey, the University of South Carolina, and Georgia Institute of Technology Savannah Campus as part of a larger study to understand the physical processes that ... |
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SouthShore_baseline.shp Offshore baseline for the South Shore coastal region generated to calculate shoreline change rates from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Sr/Ca and linear extension data for a modern Orbicella faveolata coral from Marquesas Keys, Florida, USA
This data release contains new subannual Strontium/Calcium (Sr/Ca) and annual linear extension records from a colony of the massive coral, Orbicella faveolata. The colony was collected live from the Marquesas Keys, Florida (FL) in August 1980 from core MK1. The coral Sr/Ca paleothermometer can provide a powerful proxy for centennial-scale sea-surface temperature (SST) variability in the Caribbean/Atlantic Ocean region. |
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Static chamber fluxes of carbon dioxide and methane from coastal wetlands on Upper Cape Cod, Massachusetts and supporting environmental data, 2021
Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted vast areas of coastal wetlands to tidal exchange. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls of carbon exchange in these understudied ... |
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Static chamber fluxes of carbon dioxide and methane from Phragmites wetlands and supporting data collected across a salinity gradient on Cape Cod, Massachusetts
Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted vast areas of coastal wetlands to tidal exchange. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls of carbon exchange in these understudied ... |
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Static chamber gas fluxes and carbon and nitrogen isotope content of age-dated sediment cores from a Phragmites wetland in Sage Lot Pond, Massachusetts, 2013-2015
Coastal wetlands are major global carbon sinks; however, quantification of carbon flux can be difficult in these heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, static chamber measurements of greenhouse gas (GHG) fluxes were compared among major plant-defined zones (high marsh dominated by Distichlis spicata and a zone of invasive Phragmites australis) during 2013 and 2014 growing seasons. Two sediment cores were collected in 2015 from the ... |
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Storm-Impact Scenario XBeach Model Inputs – Initial Bathymetry and Topography Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 11 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 12 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 1 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 20 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 2 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 3 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 6 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 7 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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Storm-Impact Scenario XBeach Model Results – Scenario 8 Digital Elevation Model (DEM) Grid
The numerical model XBeach (version 4937) was used to investigate how different storm scenarios impact the sediment berm constructed offshore of the Chandeleur Islands and adjacent areas. The XBeach model solves coupled 2-dimensional, horizontal wave propagation equations to predict flow, sediment transport, and bottom changes for varying spectral wave and flow boundary conditions (Roelvink and others, 2009 ). The XBeach model setup requires the input of a merged topographic and bathymetric DEM, and inputs ... |
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St. Petersburg Coastal and Marine Science Center Geoscience Data Viewer Metadata
This web mapping application is a compilation of geoscientific data collected and published by the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). This application does not serve as a complete archive of all the geoscientific data collected by the center, but highlights frequently published data types. Data within this web application include: seismic data extents, seismic survey tracklines (boomer, chirp, and minisparker), bathymetric footprints, bathymetric ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 1967-10-18
Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-03-08
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-19
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point Cloud Coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-13
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-26
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-10-12
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-07
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-21
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2018-01-29
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Subbottom and Sidescan Sonar Data Acquired in 2015 From Grand Bay, Mississippi and Alabama
From May 28 to June 3, 2015, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic evolution and estuarine sediment thickness in Grand Bay, Alabama and Mississippi. Specific objectives were to document the age and accumulation patterns of estuarine sediment to advance our understanding of sediment exchange with the adjacent marsh and sources of sediment to the coastal ocean. This investigation is part of the USGS Sea-level and Storm Impacts on Estuarine Environments ... |
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Submarine-landslide scarps--Hueneme Canyon and Vicinity, California
This part of DS 781 presents data for the submarine-landslide scarps for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "SubmarineLandslideScarps_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G. ... |
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Subtropical Storm Alberto Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0800 AM EDT SUN MAY 27 2018
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Subtropical Storm Alberto in May 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ... |
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Summary statistics for the central Beaufort Sea coast, Alaska
A nested spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) was deployed for the central Beaufort Sea coast of Alaska to simulate waves for the period from 1979 to 2019. Results in the form of spatial summary statistics, describing wave parameters, wind speed and sea-ice area cover for the intermediate grid (see Overview Image on main page of data release), are provided. Further information can be found in Nederhoff and others (2021). |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2014–2015
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Coast Guard Beach, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Monomoy Island, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Surface sediment grain diameter measurements from point counts of in situ seafloor images collected in the lower Columbia River, Washington and Oregon, 2021
This dataset contains surface sediment grain diameter measurements from in situ seafloor images collected in the lower Columbia River, Washington and Oregon, in 2021. Surface sediment grain diameters were derived from manual measurements (or "point counts") in a subset of images used to calibrate and validate an automated image processing algorithm to determine surface sediment grain size distributions. For each calibration and validation image that was selected, the long and short axis of 100 grains were ... |
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Surface-sediment grain-size data from the mouth of the Columbia River, Oregon and Washington, 2013
This portion of the USGS data release presents sediment grain-size data from samples collected from the mouth of the Columbia River, Oregon and Washington, in 2013. Surface sediment was sampled using a small ponar, or 'grab', sampler on May 9, 2013 from the F/V Cape Windy at 3 locations. A handheld global navigation satellite system (GNSS) receiver was used to determine the locations of sediment samples. The grain size distributions of samples were determined using standard techniques developed by the USGS ... |
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Surface sediment grain size distributions derived from automated image processing of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021
This dataset contains surface sediment grain size distributions derived from automated image processing of in situ seafloor images obtained with an underwater camera system at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, in 2021. The surface sediment grain size distribution data are provided in comma-separated text (.csv) format for each site and for data used in calibration and validation of the automated image processing technique. |
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Surface sediment grain size distributions derived from manual point counts of in situ seafloor images from the lower Columbia River, Washington and Oregon, 2021
This dataset contains surface sediment grain size distributions derived from manual point counts of in situ seafloor images obtained with an underwater camera system in the lower Columbia River, Washington and Oregon, in 2021. The distributions derived from manual point counts were compared with results from an automated image processing technique to calibrate and validate the automated method used to quantify surface sediment grain size distributions in objective images. The surface sediment grain size ... |
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Surface-sediment grain-size distributions from the Elwha River delta, Washington, August 2012
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in August 2012 (USGS Field Activity W-05-12-PS). Surface sediment was sampled using a small ponar, or 'grab', sampler between August 28 and August 30, 2012 from the R/V Frontier at a total of 57 locations in water depths between about 1 and 9 m around the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size ... |
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Surface-sediment grain-size distributions from the Elwha River delta, Washington, July 2015
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, between July and August 2015 (USGS Field Activities 2015-648-FA and 2015-652-FA). Surface sediment was collected from 70 locations using a small ponar, or 'grab', sampler from the R/V Frontier on July 28, 2015. An additional 17 sediment samples were collected between July 22 and August 23, 2015 by scuba divers. Forty-eight sediment samples were collected at low tide using a push ... |
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Surface-sediment grain-size distributions from the Elwha River delta, Washington, March 2013
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in March 2013 (USGS Field Activity W-01-13-PS). Surface sediment was sampled using a small ponar, or 'grab', sampler on March 4, 2013 from the R/V Frontier at a total of 48 locations in water depths between about 1 and 12 m around the delta. An additional 7 sediment samples were collected between March 6 and March 7, 2013 at low tide from intertidal locations on the delta. The ... |
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Surface-sediment grain-size distributions from the Elwha River delta, Washington, May 2014
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in May 2014 (USGS Field Activity 2014-620-FA). Surface sediment was collected from 43 locations using a small ponar, or 'grab', sampler from a small boat on May 12, 2014 in depths between about 1 and 12 m around the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples were determined ... |
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Surface-sediment grain-size distributions from the Elwha River delta, Washington, September 2013
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in September 2013 (USGS Field Activity W-07-13-PS). Surface sediment was collected from 62 locations using a small ponar, or 'grab', sampler from the R/V Frontier on September 19, 2013 in depths between about 1 and 12 m around the delta. An additional 21 sediment samples were collected between September 16 and September 19, 2013 at low tide from intertidal locations on the delta. ... |
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Surface-sediment grain-size distributions from the Elwha River delta, Washington, September 2014
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in September 2014 (USGS Field Activity 2014-649-FA). Surface sediment was collected from 63 locations using a small ponar, or 'grab', sampler from the R/V Frontier on September 5, 2014 in depths between about 1 and 17 m around the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples ... |
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Surface-sediment grain-size distributions of the Elwha River delta, Washington, August 2022
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in August 2022 (USGS Field Activity 2022-638-FA). Surface sediment was collected from 67 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 44 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of ... |
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Surface-sediment grain-size distributions of the Elwha River delta, Washington, February 2016
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in February 2016. Surface sediment was collected from 83 locations using a small ponar, or 'grab' sampler from the R/V Frontier in water depths between 17 and 1 m around the delta. An additional 18 samples were collected by hand at low tide. A handheld global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain size ... |
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Surface-sediment grain-size distributions of the Elwha River delta, Washington, January 2015
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in January 2015 (USGS Field Activity 2015-605-FA). Surface sediment was collected from 61 locations using a small ponar, or 'grab', sampler from the R/V Frontier in depths between about 1 and 17 m around the delta. A handheld global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain-size distributions of samples were ... |
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Surface-sediment grain-size distributions of the Elwha River delta, Washington, July 2016
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in July 2016 (USGS Field Activity 2016-653-FA). Surface sediment was collected from 67 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 38 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of ... |
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Surface-sediment grain-size distributions of the Elwha River delta, Washington, July 2017
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in July 2017 (USGS Field Activity 2017-638-FA). Surface sediment was collected from 80 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 31 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of ... |
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Surface-sediment grain-size distributions of the Elwha River delta, Washington, July 2018
This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in July 2018 (USGS Field Activity 2018-648-FA). Surface sediment was collected from 39 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 35 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of ... |
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Surface water data for samples collected approximately hourly along the West Florida Shelf: USGS Cruise 11BHM01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, ... |
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Surface water data for samples collected approximately hourly along the West Florida Shelf: USGS Cruise 11BHM02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25-30, ... |
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Surface water data for samples collected approximately hourly along the West Florida Shelf: USGS Cruise 11BHM03
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Surface water data for samples collected approximately hourly along the West Florida Shelf: USGS Cruise 11BHM04
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Surface water data for samples collected approximately hourly along the West Florida Shelf: USGS Cruise 11CEV01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred January 3-7, ... |
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Suspended-sediment concentration and loss-on-ignition from water samples at Thompsons Beach and Stone Harbor, New Jersey, collected between September 2018 and December 2022
In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science ... |
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Suspended-sediment concentration data from water samples collected in 2016-17 in Grand Bay, Alabama and Mississippi
Suspended-sediment transport is a critical element governing the geomorphology of tidal marshes and estuaries. Marsh elevation, relative to sea level, is maintained by both organic material and the deposition of inorganic sediment. Additionally, horizontal marsh extent is altered by lateral erosion and accretion. In wetlands within and near Grand Bay National Estuarine Research Reserve, parts of the salt marsh are eroding relatively rapidly. To understand the connection between sediment fluxes and these ... |
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Suspended sediment concentration data in the Elwha River, Washington, September 2011 to September 2016
This data release provides 15-minute data of suspended-sediment concentration and fine (less than 0.0625 mm) suspended-sediment concentration during the removal of 2 large dams on the Elwha River from September 2011 to September 2016. Data are derived from regression relations with turbidity at the USGS gaging station Elwha River at the Diversion (no.12046260). |
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Suspended-sediment concentrations and loss-on-ignition from water samples collected in the Herring River during 2018-19 in Wellfleet, MA (ver 1.1, March 2023)
The Herring River in Wellfleet, MA is a tidally-restricted estuary system. Management options including potential restoration of unrestricted tidal flows require an understanding of pre-restoration sediment conditions. Altering future tidal flows may cause changes in net sediment flux and direction, which could affect marsh restoration and aquaculture in Wellfleet Harbor. This research aims to measure sediment fluxes seaward of the Herring River restriction and sediment concentrations landward of the ... |
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Suspended sediment concentration (SSC) in the San Lorenzo River, Santa Cruz, California, USA, from 2008 to 2019
Suspended-sediment concentrations are reported in mg/L for water samples collected from the San Lorenzo River during the rainy seasons from 2008 to 2019. Samples were collected during 2-, 5- and 10-year flood events. |
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Tabulated wave parameter results from modeling surface gravity waves on a schematized ancient lake on Mars
This portion of the data release presents tabulated wave parameter results derived from simulations of wind generated surface gravity waves on an ancient lake on Mars. The phase-averaged wave model, SWAN, was applied within the Delft3D modeling system (Deltares, 2018) with reduced gravity and a range of atmospheric densities and wind speeds to simulate potential conditions that could generate wind waves on Mars. |
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Temporal hydrologic and chemical records from the Ox Bel Ha cave network within the coastal aquifer of the Yucatan Peninsula, from January 2015 to January 2016
Natural cave passages penetrating a coastal aquifer in the Yucatan Peninsula (Mexico) were accessed to investigate how regional meteorology and hydrology control methane dynamics in karst subterranean estuaries. Three field trips were carried out in January 2015, June 2015, and January 2016 to obtain year-long high-resolution temporal records of water chemistry and environmental parameters below and above the surface at a site (Cenote Bang) within the Ox Bel Ha cave network. These efforts resulted in ... |
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The 95th percentile of bottom shear stress for the Gulf of Maine south into the Middle Atlantic Bight, May 2010 to May 2011 (GMAINE_95th_perc.shp, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) ... |
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The 95th percentile of bottom shear stress for the Gulf of Mexico, May 2010 to May 2011 (GMEX_95th_perc, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) ... |
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The half interpercentile range of bottom shear stress for the Gulf of Maine south into the Middle Atlantic Bight, May 2010 to May 2011 (GMAINE_hIPR, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) ... |
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The half-interpercentile range of bottom shear stress for the Gulf of Mexico, May 2010 to May 2011 (GMEX_hIPR, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) ... |
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The Massachusetts Bay Internal Wave Experiment, August 1998: Data Report
This data report presents oceanographic observations made in Massachusetts Bay in August 1998 as part of the Massachusetts Bay Internal Wave Experiment (MBIWE98). MBIWE98 was carried out to characterize large-amplitude internal waves in Massachusetts Bay and to investigate the possible resuspension and transport of bottom sediments caused by these waves. This data report presents a description of the field program, an overview of the data through summary plots and statistics, and the time-series data in ... |
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The median of bottom shear stress for the Gulf of Maine south into the Middle Atlantic Bight, May 2010 to May 2011 (GMAINE_median.shp, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) ... |
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The median of bottom shear stress for the Gulf of Mexico, May 2010 to May 2011 (GMEX_median, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) ... |
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Thickness distribution of the most recent sandy tsunami deposit in the Salmon River estuary, Oregon
This portion of the data release provides the spatial thickness distribution of sandy deposits inferred to have been deposited at the Salmon River, OR by a circa 1700 CE tsunami. Data were collected by describing hand-operated gouge cores at 129 sites in 2017 and 2018, and supplemented by 114 core descriptions from 1987 (Nelson and others, 2004). |
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Tidal Datums, Tidal Range, and Nuisance Flooding Levels for Chesapeake Bay and Delaware Bay
This U.S. Geological Survey data release provides data on spatial variations in tidal datums, tidal range, and nuisance flooding in Chesapeake Bay and Delaware Bay. Tidal datums are standard elevations that are defined based on average tidal water levels. Datums are used as references to measure local water levels and to delineate regions in coastal environments. Nuisance flooding refers to the sporadic inundation of low-lying coastal areas by the maximum tidal water levels during spring tides, especially ... |
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Tidal_Grid
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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TIDESTATIONS - Pacific Northwest Water-Level Stations and Tidal Datum Distributions
This geospatial data set depicts the locations of National Ocean Service water-level stations to determine tidal datum distributions with the Seaside, Oregon, region. |
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Tile index for Alaska coastal orthoimagery and elevation data: Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents a shapefile that includes a spatial index of orthoimagery and elevation data describing the Alaskan coastline from Icy Cape to Cape Prince of Wales. The data products referenced in this index include orthoimagery, digital surface models, and elevation point clouds which were generated from aerial imagery using structure-from-motion methods. Fairbanks Fodar, a contracted mapping service, collected the aerial imagery in 2016 and created all of the data products ... |
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Time series data of oceanographic conditions from La Parguera, Puerto Rico, 2017-2018 Coral Reef Circulation and Sediment Dynamics Experiment
Time-series data of water surface elevation, waves, currents, temperature, and salinity collected between 17 May 2017 and 17 Jan 2018 off the southwest coast of Puerto Rico in support of a study on circulation and sediment transport dynamics over coral reefs. The data are available in NetCDF format, grouped together in zip files by instrument site location. A README.txt file details the files contained within each zip, including the file names, type of data collected, instrument that collected the data, ... |
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Time-series data of water surface elevation, waves, currents, temperature, and turbidity collected between November 2017 and March 2018 off the west coast of Maui, Hawaii, USA
Time-series data of water surface elevation, waves, currents, temperature, and turbidity collected between November 2017 and March 2018 off the west coast of Maui, Hawaii, USA. The data are available in NetCDF format, grouped together in zip files by instrument site location. These data support a modeling study on the effects of potential watershed restoration on decreasing sediment loads to adjacent reefs (Storlazzi and others, 2023). |
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Time-series data on currents, waves, and turbidity off Santa Cruz, CA, 2014-2015
Time series data of water surface elevation, wave height, currents, and turbidity were acquired during the winters of 2014-2015 and 2015-2016 in support of a study on the morphological change of rippled scour depressions off Santa Cruz, CA. One set of instruments (SCW) was mounted at the end of Santa Cruz Municipal Wharf during both winters. Another set of instruments (M1T) was deployed offshore in Monterey Bay each winter; the two offshore winter locations were different, but each were about 0.5 km ... |
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Time series for the central Beaufort Sea coast, Alaska
Time series output from a spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others 1999), implemented for the central Beaufort Sea coast of Alaska from 1979 to 2019, are provided. The variables include significant wave heights, mean wave periods, mean wave directions, wave steepness, and orbital velocities. Additionally, water depths, x (east-west) and y (north-south) components of the wind, and sea ice concentrations are provided. Further information can be found in Nederhoff and ... |
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Time-series measurements of oceanographic and water quality data collected at Thompsons Beach and Stone Harbor, New Jersey, USA, September 2018 to September 2019 and March 2022 to May 2023
In October 2012, Hurricane Sandy made landfall in the Northeastern U.S., affecting ecosystems and communities of 12 states. In response, the National Fish and Wildlife Federation (NFWF) and the U.S. Department of Interior (DOI) implemented the Hurricane Sandy Coastal Resiliency Program, which funded various projects designed to reduce future impacts of coastal hazards. These projects included marsh, beach, and dune restoration, aquatic connectivity, and living shoreline installation, among others. To ... |
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Time-series measurements of oceanographic and water quality data collected in the Herring River, Wellfleet, Massachusetts, USA, November 2018 to November 2019
Restoration in the tidally restricted Herring River Estuary in Wellfleet, MA benefits from understanding pre-restoration sediment transport conditions. Submerged sensors were deployed at four sites landward and seaward of the Herring River restriction to measure water velocity, water quality, water level, waves, and seabed elevation. These data will be used to evaluate sediment dynamics and geomorphic change and inform marsh modeling efforts over tidal and seasonal timescales. |
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Time-series oceanographic data from the National Park of American Samoa, Tutuila, American Samoa, 2015
Time-series data of water surface elevation, wave height, and water column currents, temperature, and salinity were acquired for 150 days between 13 April and 14 July 2015 off the north coast of the island of Tutuila, American Samoa in support of a study on the coastal circulation patterns within and in the vicinity of the National Park of American Samoa. |
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Time Series of Aerial Imagery from Small Unmanned Aircraft Systems and Associated Ground Control Points: Madeira Beach, Florida, July 2017 to June 2018 (Aerial Imagery)
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCPs) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data consisting of aerial ... |
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Time Series of Aerial Imagery from Small Unmanned Aircraft Systems and Associated Ground Control Points: Madeira Beach, Florida, July 2017 to June 2018 (Surveyed GCPs)
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCPs) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data consisting of aerial ... |
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Time Series of Autonomous Carbonate System Parameter Measurements from Crocker Reef, Florida, USA
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification at Crocker Reef located along the Florida Keys Reef Tract, in Southeast Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 1 (OCSv1) deployed on the seafloor at Crocker Reef. The OCSv1 consists of five sensors ... |
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Time Series of Autonomous Carbonate System Parameter Measurements in Eastern Gulf of Mexico near Tampa Bay, Florida, USA
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification in the Gulf of Mexico near the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 3 (OCSv3) deployed on the University of South Florida (USF), Coastal Ocean Monitoring and ... |
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Time Series of Autonomous Carbonate System Parameter Measurements in Eastern Gulf of Mexico near Tampa Bay, Florida, USA (Version 2.0)
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification in the Gulf of Mexico near the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 3 (OCSv3) deployed on the University of South Florida (USF), Coastal Ocean Monitoring and ... |
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Time Series of Autonomous Carbonate System Parameter Measurements in Middle Tampa Bay, Florida, USA
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird ... |
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Time Series of Autonomous Carbonate System Parameter Measurements in Middle Tampa Bay, Florida, USA (version 2.0, August 2019)
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird ... |
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Time Series of Autonomous Carbonate System Parameter Measurements in Middle Tampa Bay, Florida, USA (version 3.0, March 2021)
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird ... |
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Time Series of Autonomous Carbonate System Parameter Measurements in Middle Tampa Bay, Florida, USA (version 4.0, June 2022)
This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird ... |
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Time-series of biogeochemical and flow data from a tidal salt-marsh creek, Sage Lot Pond, Waquoit Bay, Massachusetts, 2012-2016 (ver. 2.0, July 2023)
Extended time-series sensor data were collected between 2012 and 2016 in surface water of a tidal salt-marsh creek on Cape Cod, Massachusetts. The objective of this field study was to measure water chemical characteristics and flows, as part of a study to quantify lateral fluxes of dissolved carbon species between the salt marsh and estuary. Data consist of in-situ measurements including salinity, temperature, pH, dissolved oxygen, redox potential, fluorescent dissolved organic matter, turbidity, ... |
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Time Series of Structure-from-Motion Products - Digital Elevation Models: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs ... |
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Time Series of Structure-from-Motion Products - Digital Elevation Models: Madeira Beach, Florida, July 2017 to June 2018
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs ... |
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Time Series of Structure-from-Motion Products - Multispectral Orthomosaics: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - Orthomosaics: Madeira Beach, Florida, July 2017 to June 2018
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - Point Clouds: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - Point Clouds: Madeira Beach, Florida, July 2017 to June 2018
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - RGB Orthomosaics: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of red ... |
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Topographic digital surface model (DSM) for Whiskeytown Lake and surrounding area, 2018-12-02
This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2018-12-02. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original ... |
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Topographic digital surface model (DSM) for Whiskeytown Lake and surrounding area, 2019-06-03
This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-06-03. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original ... |
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Topographic digital surface model (DSM) for Whiskeytown Lake and surrounding area, 2019-11-12
This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-11-12. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original ... |
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Topographic digital surface model (DSM) for Whiskeytown Lake and surrounding area, 2020-11-10
This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2020-11-10. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original ... |
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Town Neck Beach, Massachusetts, 10 cm 2016-2017 Digital Elevation Models
Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS ... |
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Town Neck Beach, Massachusetts, 5 cm 2016-2017 Orthomosaics
Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS ... |
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Transect Lines for Assateague Island, Maryland and Virginia
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future ... |
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Transect Lines for the Undeveloped Areas of New Jersey's Barrier Islands (projected, UTM Zone 18N (NAD83))
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these ... |
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Transects_BackBarrier.shp - Digital Shoreline Analysis System version 4.3 Transects with Linear Regression Rate Calculations for the Back-Barrier (North-Facing) coast of Dauphin Island, Alabama.
Rates of shoreline change for Dauphin Island, Alabama were generated for three analysis periods, using two different shoreline proxy datasets. Mean High Water line (MHW) shorelines were generated from 14 lidar datasets (1998-2014) and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images (1940-2015). Rates of change were generated for three groups of shorelines: MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. These data will aid in developing an ... |
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Transects_OpenOcean.shp - Digital Shoreline Analysis System version 4.3 Transects with Linear Regression Rate Calculations for the Open Ocean coast of Dauphin Island, Alabama.
Rates of shoreline change for Dauphin Island, Alabama were generated for three analysis periods, using two different shoreline proxy datasets. Mean High Water line (MHW) shorelines were generated from 14 lidar datasets (1998-2014) and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images (1940-2015). Rates of change were generated for three groups of shorelines: MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. These data will aid in developing an ... |
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Transects with linear regression rates of change for GPS, Worldview, and aerial image shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Transects with net change results for GPS and Worldview shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Transects with Shoreline Change Rates for the Grand Bay National Estuarine Research Reserve in Mississippi and Alabama from 1848 to 2017
This dataset contains shoreline change rates for the Grand Bay National Estuarine Research Reserve from 1848 to 2017. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve(GBNERR), and the Mississippi Office of Geology (MOG). Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline ... |
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Tropical Storm Bill Assessment of Potential Coastal-Change Impacts: NHC Advisory 2, 0900 AM UTC MON JUN 16 2015
This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Bill in June 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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Tropical Storm Colin Assessment of Potential Coastal Change Impacts: NHC Advisory 4, 0500 AM EDT MON JUN 06 2016
This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Colin in June 2016. Storm-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision ... |
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Tropical Storm Gordon Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0700 AM CDT TUE SEP 04 2018
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Gordon in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ... |
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Tropical Storm Hermine Assessment of Potential Coastal Change Impacts: NHC Advisory 20, 0500 AM EDT FRI SEP 02 2016
This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Hermine in September 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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TSUNAMI_DEPOSITS - Tsunami Deposits at Seaside, Oregon
This data set is a point shapefile representing tsunami deposits within the Seaside, Oregon region obtained by Brooke Fiedorowicz and Curt Peterson in 1997 and Bruce Jaffe, Curt Peterson, and Robert Peters in 2004. The geospatial dataset were derived from spreadsheets provided by Bruce Jaffe. |
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Turbidity data for two sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Turbidity data from the Carmel River, central California, 2014 to 2017
This data provides river turbidity measurements collected on the Carmel River, CA. Turbidity was measured to study any changes in the Carmel River’s sediment loads following the removal of the San Clemente Dam. The USGS-run DTS-12 turbidity sensor was deployed above the Sleepy Hollow Weir on the Carmel River, CA (instrument was located at 36.445250 degrees North, 121.710494 degrees West). Deployment began on December 9, 2014. After June 16, 2016, the instrument was removed for calibration. A new ... |
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Tyndall_2022_MBES: High-resolution Geophysical Data Collected in June 2022 Near Tyndall Air Force Base, Panama City, Florida
An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) nearshore Tyndall Air Force Base, Panama City, Florida, from June 20-30, 2022. This dataset, Tyndall_2022_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid, and the dataset Tyndall_2022_MBES_Backscatter.zip ... |
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Uncertainty of forecasted shoreline positions for Florida and Georgia
During Hurricane Irma, Florida and Georgia experienced substantial impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses from hurricanes result in increased vulnerability of coastal regions, including densely populated areas. Erosion may put critical infrastructure at risk of future flooding and may cause economic loss. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program is working to assess shoreline erosion along the southeast U.S. ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Cape Cod Bay, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Oregon (OR_shorelines_uncertainty.dbf)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Texas east (TXeast)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Texas west (TXwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the Cape Cod region from Provincetown to the southern end of Monomoy Island, Massachusetts (OuterCapeCod_shorelines_uncertainty.dbf)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the North Shore of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the north shore of Martha's Vineyard, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the north shore of Nantucket, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the Outer Cape of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Coast of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the southern shoreline of Cape Cod, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Shore of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the south shore of Martha's Vineyard, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the south shore of Nantucket, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Washington (WA_shorelines_uncertainty.dbf)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for western North Carolina (NCwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Unvegetated to vegetated marsh ratio in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
Unvegetated to vegetated marsh ratio (UVVR) in the Assateague Island National Seashore and Chincoteague Bay is computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to ... |
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Unvegetated to vegetated marsh ratio in Cape Cod National Seashore salt marsh complex, Massachusetts
Unvegetated to vegetated marsh ratio (UVVR) in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands is computed based on conceptual marsh units defined by Defne and Ganju (2019). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Unvegetated to vegetated marsh ratio in Fire Island National Seashore and central Great South Bay salt marsh complex, New York
Unvegetated to vegetated marsh ratio (UVVR) in the Fire Island National Seashore and central Great South Bay salt marsh complex, is computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Unvegetated to vegetated marsh ratio in Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Unvegetated to vegetated marsh ratio in Plum Island Estuary and Parker River salt marsh complex, Massachusetts
Unvegetated to vegetated marsh ratio (UVVR) in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex was computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast ... |
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Unvegetated to vegetated ratio at Thompsons Beach and Stone Harbor, New Jersey from 2014 to 2018
In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science ... |
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Unvegetated to vegetated ratio of marsh units in Blackwater salt marsh complex, Chesapeake Bay, Maryland
This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Unvegetated to vegetated ratio of marsh units in Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Unvegetated to vegetated ratio of marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Unvegetated to vegetated ratio of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Unvegetated to vegetated ratio of marsh units in Eastern Shore of Virginia salt marshes
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Unvegetated to vegetated ratio of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Unvegetated to vegetated ratio of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Unvegetated to vegetated ratio of marsh units in Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Unvegetated to vegetated ratio of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Upstream sediment contributions to Lake Mills on the Elwha River, Washington, 1926 to 2016
Sediment inputs to Lake Mills, on the Elwha River, Washington, were measured from 1927 to 2016. These measurements represent the annual total sediment load, in tonnes per year, that were input into Lake Mills and partially trapped by Glines Canyon dam. The sediment was allowed to erode and be transported down-river by the removal of the Glines Canyon and Elwha dams during 2011 to 2014. The measurements were taken as part of a study investigating the river channel's morphological responses to the removal of ... |
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U.S. Geological Survey calculated 95th percentile of wave-current bottom shear stress for the South Atlantic Bight for May 2010 to May 2011 (SAB_95th_perc, polygon shapefile, Geographic, WGS84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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U.S. Geological Survey calculated half interpercentile range (half of the difference between the 16th and 84th percentiles) of wave-current bottom shear stress in the South Atlantic Bight from May 2010 to May 2011 (SAB_hIPR.shp, polygon shapefile, Geographic, WGS84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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U.S. Geological Survey calculated median of wave-current bottom shear stress in the South Atlantic Bight from May 2010 to May 2011 (SAB_median, polygon shapefile, Geographic, WGS84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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U.S. Geological Survey calculated percentage of time sediment is mobile for May 2010 to May 2011 at select points in the South Atlantic Bight (SAB_mobile_perc, point shapefile, Geographic, WGS84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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U.S. Geological Survey calculated recurrence interval of sediment mobility at select points in the South Atlantic Bight for May 2010 to May 2011 (SAB_mobile_freq, Geographic, WGS 84)
The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are ... |
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USGS Arctic Ocean Carbon Cruise 2010: Discrete Lab data
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months, when ice melt is at its greatest extent. However, few comprehensive datasets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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USGS Arctic Ocean Carbon Cruise 2011: Discrete Lab data
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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USGS Arctic Ocean Carbon Cruise 2011: Discrete Underway data
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic ... |
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USGS Arctic Ocean Carbon Cruise 2012: Discrete Underway Laboratory data
Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent; however, few comprehensive datasets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic carbon ... |
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USGS CoastCam at Isla Verde, Puerto Rico: 2018-2019 Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Isla Verde, Puerto Rico (PR) and faced northeast along the beach. Every hour during daylight hours, daily from February 1, 2019, to July 15, 2019, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research ... |
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USGS CoastCam at Isla Verde, Puerto Rico: 2018-2019 Timestack Imagery and Coordinate Data
A digital video camera was installed at Isla Verde Beach in San Juan, Puerto Rico (PR) and faced northeast along the beach. Every hour during daylight hours, daily from February 1, 2019, to July 15, 2019, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the ... |
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USGS CoastCam at Madeira Beach, Florida: Timestack Imagery and Coordinate Data
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, and blue or monochrome pixel ... |
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USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 1)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements.. The cameras are part of a U.S. Geological Survey (USGS) research project to study the ... |
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USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 2)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The images included in this data release were collected by camera 2 (c2) from May 29, ... |
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USGS CoastCam at Sand Key, Florida: Timestack Imagery and Coordinate Data (Camera 2)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, daily from 2018 to 2022, the cameras collected raw video and produced snapshots and time-averaged image products. For camera 2, one such product that is created is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup ... |
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USGS CoastCam at Tres Palmas, Rincón, Puerto Rico: Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Tres Palmas, Rincón, Puerto Rico (PR) and faced west to view the beach and water offshore. Every hour during daylight hours, daily from August 27, 2019 to March 10, 2020, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological ... |
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USGS CoastCam at Tres Palmas, Rincón, Puerto Rico: Timestack Imagery and Coordinate Data
A digital video camera was installed at Tres Palmas, Rincón, Puerto Rico (PR) and faced west along the beach. Every hour during daylight hours, daily from August 27, 2019, to March 10, 2020, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, ... |
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USGS CoastCam at Waiakāne, Moloka'i, Hawai'i: 2018 Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Waiakāne, Moloka'i, Hawai'i (HI) and faced west along the beach. Every hour during daylight hours, daily from June 26, 2018, to September 20, 2018, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research ... |
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USGS CoastCam at Waiakāne, Moloka'i, Hawai'i: 2018 Timestack Imagery and Coordinate Data
A digital video camera was installed at Waiakāne, Moloka'i, Hawai'i (HI) and faced west along the beach. Every hour during daylight hours, daily from June 26, 2018, to September 20, 2018, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, and ... |
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USGS T-3 enhanced thermal data from T-3 Ice Island, 1963-73
The T-3 (Fletcher's) Ice Island in the Arctic Ocean was the site of a scientific research station re-established by the Naval Arctic Research Laboratory starting in 1962. Starting in 1963, the USGS acquired marine heat flow data and coincident sediment cores at sites in Canada Basin, Nautilus Basin, Mendeleev Ridge, and Alpha Ridge as the ice island drifted in the Amerasian Basin. At least 584 heat flow penetrations were attempted, and data were reported at 356 of these. This dataset is the enhanced version ... |
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USGS T-3 Original Thermal Gradient, Thermal Conductivity, and Heat Flow Data from T-3 Ice Island, 1963-73
The T-3 (Fletcher's) Ice Island in the Arctic Ocean was the site of a scientific research station re-established by the Naval Arctic Research Laboratory starting in 1962. Starting in 1963, the USGS acquired marine heat flow data and coincident sediment cores at sites in Canada Basin, Nautilus Basin, Mendeleev Ridge, and Alpha Ridge as the ice island drifted in the Amerasian Basin. At least 584 heat flow penetrations were attempted, and data were reported at 356 of these. This dataset is the digital version ... |
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VA Bias_Feature – Feature class containing Virginia proxy-datum bias information to be used in the Digital Shoreline Analysis System.
Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release ... |
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Vectorized marsh shorelines derived from global positioning system data for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Vectorized marsh shorelines derived from high resolution aerial imagery for the Grand Bay National Estuarine Research Reserve in Mississippi from 2014-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Vectorized Marsh Shorelines derived from WorldView imagery for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Vectorized Marsh Shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi and Alabama from 1848 to 2017
This dataset represents a compilation of vector shorelines in the Grand Bay National Estuarine Research Reserve (Mississippi and Alabama) from 1848 to 2017. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve (GBNERR), and the Mississippi Office of Geology (MOG). All shoreline data types have uncertainty associated with delineating the shoreline ... |
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Vegetation habitat units derived from 2009 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 11 September 2009 1-meter-resolution NAIP aerial imagery at a scale of 1:1500. |
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Vegetation habitat units derived from 2011 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 3 September 2011* 0.3-meter-resolution aerial imagery (Microsoft/Digital Globe) at a scale of 1:1500. *Image date of 3-Sep corrected in metadata. During product generation the imagery date was believed to be 8-25-2011, as reported by DigitalGlobe reseller. |
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Vegetation habitat units derived from 2012 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 30 August 2012 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500. |
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Vegetation habitat units derived from 2013 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 26 August 2013 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500. |
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Vegetation habitat units derived from 2014 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 28 August 2014 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500. |
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Vegetation survey in a coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Velocity_all: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_all_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_GBI: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_GBI_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_na: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_na_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Vertical chemical profiles collected across haloclines in the water column of the Ox Bel Ha cave network within the coastal aquifer of the Yucatan Peninsula in January 2015 and January 2016
Natural cave passages penetrating a coastal aquifer in the Yucatan Peninsula (Mexico) were accessed to test the hypothesis that chemoclines associated with salinity gradients (haloclines) within the flooded cave networks of the karst subterranean estuary are sites of methane oxidation. Two field trips were carried out to the fully-submerged cave system located 6.6 km inland from the coastline in January 2015 and January 2016. Vertical chemical profiles across the water column haloclines were obtained using ... |
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Vertical land motion rates for the years 2007 to 2020 for the North and South Carolina coasts
Rates of land subsidence and uplift for the North and South Carolina coasts are derived from Sentinel-1A/B (2015-2020) and ALOS (2007-2011) synthetic aperture radar (SAR) satellites, at approximately 50-75 m resolution and mm-level precision. The data consist of vertical land motion (VLM) rates and the 1-sigma error in land motion rates and are available as csv files. |
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Vertical land motion rates for the years 2007 to 2020 for the U.S. Atlantic coast
This dataset contains rates of land subsidence and uplift derived from Sentinel-1A/B (2015-2020) and ALOS (2007-2011) synthetic aperture radar (SAR) satellites, at approximately 50-75 m resolution and mm-level precision for the U.S. Atlantic coast except for the states of North and South Carolina. The data consist of vertical land motion (VLM) rates and the 1-sigma error in land motion rates and are available as csv files. Similar vertical land motion rates for North Carolina and South Carolina are ... |
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Vessel-mounted acoustic Doppler current profiler (ADCP) data from the lower Columbia River, Washington and Oregon, 2021
This dataset contains water velocity data derived from spatial surveys performed with a vessel-mounted acoustic Doppler current profiler at four sites (SKM, SLG, LDB, WLW) in the lower Columbia River, Washington and Oregon, in 2021. The data are provided in netCDF (.nc) format and compressed into .zip archives for each site. |
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Vibracore locations collected in 2014 from Barnegat Bay, New Jersey
In response to the 2010 Governor’s Action Plan to clean up the Barnegat Bay–Little Egg Harbor (BBLEH) estuary in New Jersey, the U.S. Geological Survey (USGS) partnered with the New Jersey Department of Environmental Protection in 2011 to begin a multidisciplinary research project to understand the physical controls on water quality in the bay. Between 2011 and 2013, USGS scientists mapped the geological and morphological characteristics of the seafloor of the BBLEH estuary using a suite of geophysical ... |
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Water column sample data from predefined locations of the West Florida Shelf: USGS Cruise 11BHM01
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, ... |
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Water column sample data from predefined locations of the West Florida Shelf: USGS Cruise 11BHM02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25-30, ... |
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Water column sample data from predefined locations of the West Florida Shelf: USGS Cruise 11BHM03
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Water column sample data from predefined locations of the West Florida Shelf: USGS Cruise 11BHM04
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September ... |
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Water column sample data from predefined locations of the West Florida Shelf: USGS Cruise 11CEV02
The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred February 17 ... |
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Water depth time-series data collected in 2009 offshore of Wainwright, Alaska
A time-series of varying water depths were collected offshore of Wainwright, Alaska, from August 23 to October 02, 2009 (UTC). Measurements were collected with a built-in pressure transducer from a 1 MHz NortekTM AWAC acoustic Doppler current profiler mounted on a frame in approximately 10 m of water. The instrument was mounted to the frame at 0.55 m off the bottom of the seafloor. Reported depth values include the 0.55 m offset, and thus are depths relative to the seabed. These data are available in a ... |
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Water_Level_all: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_all_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water level and salinity data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Water level and velocity measurements from the 2012 University of Western Australia Fringing Reef Experiment (UWAFRE)
This data release contains water level and velocity measurements from wave runup experiments performed in a laboratory flume setting. Wave-driven water level variability (and runup at the shoreline) is a significant cause of coastal flooding induced by storms. Wave runup is challenging to predict, particularly along tropical coral reef-fringed coastlines due to the steep bathymetric profiles and large bottom roughness generated by reef organisms. The 2012 University of Western Australia Fringing Reef ... |
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Water level data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2018 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Water_Level_GBI: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_GBI_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_na: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_na_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water-level, wind-wave, and suspended-sediment concentration (SSC) time-series data from Liberty Island (station LWA), Sacramento-San Joaquin Delta, California, 2015-2017
Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) from 2015 to 2017. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided ... |
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Water-level, wind-wave, and suspended-sediment concentration (SSC) time-series data from Little Holland Tract (station HWA), Sacramento-San Joaquin Delta, California, 2015
Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) in 2015. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the ... |
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Water-level, wind-wave, and suspended-sediment concentration (SSC) time-series data from Little Holland Tract (station HWC), Sacramento-San Joaquin Delta, California, 2015-2017
Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) from 2015 to 2017. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided ... |
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Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Liberty Island Conservation Bank (station WVA), Sacramento-San Joaquin Delta, California, 2017
Water depth, turbidity, and current velocity time-series data were collected in Liberty Island Conservation Bank (WVA) in 2017. The turbidity sensors were not calibrated to suspended-sediment concentration at this location. Typically, each zip folder for a deployment period contains two data files from a velocimeter and one data file from a CTD, each of which include data from an optical backscatter sensor. |
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Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Liberty Island (station LVB), Sacramento-San Joaquin Delta, California, 2015-2017
Water depth, turbidity, and current velocity time-series data were collected in Liberty Island from 2015 to 2017. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, and direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of ... |
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Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Little Holland Tract (station HVB), Sacramento-San Joaquin Delta, California, 2015-2017
Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract from 2015 to 2017. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all ... |
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Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Little Holland Tract (station HVD), Sacramento-San Joaquin Delta, California, 2016
Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract in 2016. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, and direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the ... |
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Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Little Holland Tract (station HVE), Sacramento-San Joaquin Delta, California, 2016
Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract in 2016. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the ... |
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Water pressure/depth and turbidity time-series data from CHC13 Marsh and mudflat stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth and turbidity time-series data from CHC14 Marsh and mudflat stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth and turbidity time-series data from CHC16 Marsh and mudflat stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC13 Bay channel station in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC13 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC13 Tidal creek stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC14 Bay channel station in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC14 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC14 Tidal creek stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC16 Bay channel stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC16 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from CHC16 Tidal creek stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from SPA14 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from SPB14 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from SPC14 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water pressure/depth, velocity, and turbidity time-series data from SPD15 Bay shallows stations in San Pablo Bay and China Camp Marsh, California
Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay ... |
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Water quality data from a multiparameter sonde collected in the Herring River during November 2018 to November 2019 in Wellfleet, MA
Management efforts of the tidally-restricted Herring River in Wellfleet, MA include research to understand pre-restoration sediment conditions. Submerged multiparameter sondes that measure optical turbidity were deployed at one site landward and three sites seaward of the Herring River restriction. Periodically, the sites were visited and additional turbidity measurements were collected with a handheld multiparameter sonde, and water samples were collected for determination of suspended-sediment ... |
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Water quality data from a multiparameter sonde from Thompsons Beach and Stone Harbor, New Jersey, collected between September 2018 and December 2022
In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science ... |
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Wave and wind projections along United States coasts
Coastal managers and ocean engineers rely heavily on projected average and extreme wave conditions for planning and design purposes, but when working on a local or regional scale, are faced with much uncertainty as changes in the global climate impart spatially varying trends. Future storm conditions are likely to evolve in a fashion that is unlike past conditions and is ultimately dependent on the complicated interaction between the Earth’s atmosphere and ocean systems. Despite a lack of available data ... |
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Wave model grids and bathymetry for the central Beaufort Sea coast, Alaska
The required grid and bathymetry files to run a nested spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) for the central Beaufort Sea coast of Alaska are provided. A three-level SWAN nesting grid with grid resolutions of 5000 meters, 1000 meters, and 200 meters for the overall, intermediate and detail grids, respectively (see included Browse Graphic) has been developed. For this purpose, available local bathymetry (Coastal Frontiers Corporation, 2014; Kasper and others, ... |
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Wave model input files
Provided here are the required input files to run a standalone wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) on eleven model domains from the Canada-U.S. border to Norton Sound, Alaska to create a downscaled wave database (DWDB). The DWDB, in turn, can be used to reconstruct hindcast (1979-2019) and projected (2020-2050) time series at each point in the model domains see Engelstad and others, 2023 for further information on reconstruction of time-series. The model forcing ... |
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Wave observations from bottom-mounted pressure sensors in Bellingham Bay, Washington from Dec 2017 to Jan 2018
RBRduo pressure and temperature sensors (early 2015 generation), mounted on aluminum frames, were moored in shallow (< 6 m) water depths in Bellingham Bay, Washington, to capture wave heights and periods. Continuous pressure fluctuations are transformed into surface-wave observations of wave heights, periods, and frequency spectra at 30-minute intervals. |
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Wave observations from bottom-mounted pressure sensors in Skagit Bay, Washington from Dec 2017 to Feb 2018
RBRduo pressure and temperature sensors (early 2015 generation), mounted on aluminum frames, were moored in shallow (< 6 m) water depths in Skagit Bay to capture wave heights and periods. Continuous pressure fluctuations are transformed into surface-wave observations of wave heights, periods, and frequency spectra at 30-minute intervals. |
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Wave power on marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Wave Scenario Grid with Proposed Sediment Borrow Pit 3 of Breton Island, Louisiana: Model Input Grid 4 with Pit 3 Configuration
The Simulating WAves Nearshore (SWAN) wave model input grid 4 bathymetry with pit 3 configuration (G4_P3_grid.shp) and output of significant wave height, dominant wave period, and mean wave direction resulting from simulation of wave scenarios at Breton Island, LA, as described in USGS Open-File Report 20151055 are provided here. |
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Wave thrust values at point locations along the shorelines of Chesapeake Bay, Maryland and Virginia
This product provides spatial variations in wave thrust along shorelines in the Chesapeake Bay. Natural features of relevance along the Bay coast are salt marshes. In recent times, marshes have been eroding primarily through lateral erosion. Wave thrust represents a metric of wave attack acting on marsh edges. The wave thrust is calculated as the vertical integral of the dynamic pressure of waves. This product uses a consistent methodology with sufficient spatial resolution to include the distinct features ... |
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Wave thrust values at point locations along the shorelines of Massachusetts and Rhode Island
This product provides spatial variations in wave thrust along shorelines in Massachusetts and Rhode Island. Natural features of relevance along the State coast are salt marshes. In recent times, marshes have been eroding primarily through lateral erosion. Wave thrust represents a metric of wave attack acting on marsh edges. The wave thrust is calculated as the vertical integral of the dynamic pressure of waves. This product uses a consistent methodology with sufficient spatial resolution to include the ... |
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Wave time-series data collected in 2009 offshore of Wainwright, Alaska
Time series wave data were collected offshore of Wainwright, Alaska, from August 24 to October 02, 2009 (UTC). Measurements were collected using a 1 MHz NortekTM AWAC acoustic Doppler current profiler mounted on a frame in approximately 10 m of water. The instrument was mounted to the frame at 0.55 m off the bottom of the seafloor, and collected data in 8.53-minute bursts at 2 Hz. Significant wave heights (Hs), maximum significant wave heights (Hmax), peak and mean wave periods (Tp and Tm, respectively), ... |
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Wave time-series data collected in 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, ... |
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Wave time-series: ERA5 hindcast period 1979-2019 - U.S. Canada border to Bering Strait
Modeled wave time series data are presented for the hindcast period of 1979 to 2019 from the U.S. Canada border to the Bering Strait close to the 5 and 10 m isobaths. Outputs include three-hourly nearshore significant wave heights (Hs), mean wave periods (Tm) and mean wave directions (Dm) for 6424 locations. Data are available as netCDF files and are packaged for the Beaufort Sea region from the U.S. Canada border to Nuvuk (Point Barrow), and for the Chukchi Sea region from Nuvuk to Kotzebue Sound and from ... |
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Weekly Wind Speed and Frequency for a Wave Exposure Model of Grand Bay, Mississippi
Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change, they can also influence shore-proximal vertical accretion through sediment delivery. For these reason, estimates of wave energy can provide a ... |
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WestBeaufort_sheltered_baselines.shp - Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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West Florida Shelf sonde (temperature, conductivity, salinity, pH) data collected from a continuous surface water flow-through system in August 2013
The United States Geological Survey (USGS) is studying the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises on the West Florida Shelf and northern Gulf of Mexico regions aboard the research vessel (R/V) Weatherbird II or Bellows, ships of opportunity led by Dr. Kendra Daly, of the University of South Florida (USF) in July and August, ... |
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Wetland-Change Data Derived from Landsat Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2015: Land-cover Change Analysis
This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). Land-cover ... |
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Wetland-Change Data Derived from Landsat Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2015: Wetland Persistence Analysis
This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). To assess ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Ivan Constant Land Friction Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Ivan Default Friction Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Ivan Spatially Varying Friction Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Katrina before Hurricane Ivan Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Katrina Constant Land Friction Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Katrina Default Friction Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XBeach Bottom Friction Scenarios: Model Inputs and Results for Hurricane Katrina Spatially Varying Friction Scenario
Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs ... |
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XYZ point data - Post Hurricane Sandy Beach Profile Survey Fire Island Inlet to Moriches Inlet 2013
The U.S. Army Corps of Engineers(USACE) contracted a beach survey of Fire Island, New York from September 17–October 6, 2013, for the purpose of planning a beach reconstruction project following Hurricane Sandy. This dataset contains elevation data of subaerial morphology and nearshore bathymetry collected using real time kinematic global positioning system (RTK-GPS) and hydrography techniques. The data were provided to the U.S. Geological Survey(USGS) to contribute to an existing monitoring dataset of ... |
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