Doran, K.S.; Long, J.W.; Birchler, J.J.; Brenner, O.T.; Hardy, M.W.; Morgan, K.L.M.; Stockdon, H.F.; Torres, M.L., 2020, Lidar-derived beach morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. sandy coastlines (ver 3.0, February 2020): , https://doi.org/10.5066/F7GF0S0Z.
Long, J.W.; Henderson, R.E.; Thompson, D.M., 2020, Forecasting future beach width-A case study along the Florida Atlantic coast: , 20, https://doi.org/10.3133/ofr20191150.
Stockdon, H.F.; Long, J.W.; Palmsten, M.L.; Van der Westhuysen, A.; Doran, K.S.; Snell, R.J., 2023, Operational forecasts of wave-driven water levels and coastal hazards for US Gulf and Atlantic coasts: Communications Earth & Environment 4:1, 169, https://doi.org/10.1038/s43247-023-00817-2.
(Abstract)
Predictions of total water levels, the elevation of combined tides, surge, and wave runup at the shoreline, are necessary to provide guidance on potential coastal erosion and flooding. Despite the importance of early warning systems for these hazards, existing real-time meteorological and oceanographic forecast systems at regional and national scales, until now, have lacked estimates of runup necessary to predict wave-driven overwash and erosion. To address this need, we present an approach that includes wave runup in an operational, national-scale modeling system. Using this system, we quantify the contribution of waves to potential dune erosion events along 4,700?km of U.S. Atlantic and Gulf of Mexico sandy coastlines for a one-year period. Dune erosion events were predicted to occur at over 80% of coastal locations, where waves dominated shoreline total water levels, representing 73% of the signal. This shows that models that neglect the wave component underestimate the hazard. This new, national-scale operational modeling system provides communities with timely, local-scale (0.5?km resolution) coastal hazard warnings for all wave conditions, allowing for rapid decision-making related to safety and emergency management. The modeling system also enables continued research into wave-driven processes at a broad range of coastal areas.
Stockdon, H.F.; Sallenger, A.H., Jr.; List, J.H., 2002, Estimation of shoreline position and change using airborne topographic lidar data: 18, 502.
(Abstract)
A method has been developed for estimating shoreline position from airborne scanning laser data. This technique allows rapid estimation of objective, GPS-based shoreline positions over hundreds of kilometers of coast, essential for the assessment of large-scale coastal behavior. Shoreline position, defined as the cross-shore position of a vertical shoreline datum, is found by fitting a function to cross-shore profiles of laser altimetry data located in a vertical range around the datum and then evaluating the function at the specified datum. Error bars on horizontal position are directly calculated as the 95[percent] confidence interval on the mean value based on the Student's t distribution of the errors of the regression. The technique was tested using lidar data collected with NASA's Airborne Topographic Mapper (ATM) in September 1997 on the Outer Banks of North Carolina. Estimated lidar-based shoreline position was compared to shoreline position as measured by a ground-based GPS vehicle survey system. The two methods agreed closely with a root mean square difference of 2.9 m. The mean 95[percent] confidence interval for shoreline position was [plus or minus] 1.4 m. The technique has been applied to a study of shoreline change on Assateague Island, Maryland/Virginia, where three ATM data sets were used to assess the statistics of large-scale shoreline change caused by a major 'northeaster' winter storm. The accuracy of both the lidar system and the technique described provides measures of shoreline position and change that are ideal for studying storm-scale variability over large spatial scales. Reprinted by permission of the publisher.
