| Description |
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 transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, 20, and 25 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three month sampling periods, measuring sediment deposition from July 2018 to January 2020, with one set of NST being deployed for six months. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, grain size, and organic content (loss-on-ignition [LOI]). For select sampling periods, ancillary data (water level, elevation, turbidity, and wave data) are also provided in this data release. Data were collected during Field Activities Numbers (FAN) 2018-332-FA (18CCT01), 2018-358-FA (18CCT10), 2019-303-FA (19CCT01, 19CCT02, 19CCT03, and 19CCT04, respectively), and 2020-301-FA (20CCT01). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Data from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found at https://coastal.er.usgs.gov/data-release/doi-P9BFR2US/. Please read the full metadata for details on data collection, dataset variables, and data quality. [More]
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