This dataset contains simulated storm surge results for the northern Gulf of Mexico (Mississippi, Alabama, and the Florida panhandle) using a high-resolution SWAN+ADCIRC model (Bilskie, 2016b). The modeling approach incorporates dynamic processes including salt marsh evolution, shoreline and dune height change, land use land cover, as well as sea level rise, for the year 2100. This modeling effort permits more robust and realistic results than using a static, or ‘bathtub,’ approach (Passeri et al., 2015). The outcome is a better understanding of the storm surge generating mechanisms and interactions among hurricane characteristics and the Northern Gulf of Mexico’s geophysical configuration. There are two broad categories of storm surge model results from the Ecological Effects of Sea Level Rise Northern Gulf of Mexico (EESLR-NGOM) project: 1) Storm Surge by Storm [29 GB total file size, 500 files (unzipped)] and 2) Storm Surge Maximum of Maximums (MOMs) [13 GB total file size, 50 files (unzipped)]. The datasets contain both water surface elevation and inundation depth above ground as model outputs. Each storm surge model output, described below, is provided for the following 5 sea level rise scenarios (Parris et al. 2012): Initial Condition (c. 2000) (no change from c. 2000 mean sea level (MSL)), Low (+0.2m from MSL), Intermediate-Low (+0.5m from MSL), Intermediate-High (+1.2m from MSL), and High (+2.0m from MSL).

This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals and sponges offshore of the continental U.S. West Coast to 1200 m depth. It includes raster datasets at 200 x 200 m spatial resolution depicting the mean of the predicted relative habitat suitability, the coefficient of variation of the predicted relative habitat suitability, the classified mean relative habitat suitability, and the ‘robust high’ habitat suitability prediction for each of 31 taxa of deep-sea corals and 15 taxa of sponges and raster datasets at 200 x 200 m spatial resolution depicting the number of taxa of deep-sea corals associated with hard substrate that have ‘high’ habitat suitability or ‘robust high’ habitat suitability at each grid cell. The data collection also includes raster datasets at 200 x 200 m spatial resolution depicting each of the 66 spatial environmental predictor variables considered for fitting the models.

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 data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

This dataset comprises stillwater storm surge projections for 2100 for the northern Gulf of Mexico (Mississippi, Alabama, and the Florida panhandle) using a high-resolution coupled SWAN+ADCIRC model (Bilskie et al., 2016a). These results are from a predictive model in a scenario-based modeling framework that makes projections under sea level difference scenarios. There are two categories of stillwater storm surge model outputs for the 1% and 0.2% annual chance occurrence probability -- meaning 1% or 0.2% chance of being met or exceeded in any given year -- resulting from the Ecological Effect of Sea Level Rise Northern Gulf of Mexico (EESLR-NGOM) project: 1) Water surface elevation of stillwater storm surge [ 1 GB total file size, 150 files] and 2) Inundation depth above ground of stillwater storm surge [ 1 GB total file size, 150 files]. The boundaries for the three study regions are also included. For a complete description of the methods used to generate these results, please see the Bilskie et al. (2017) publication, referenced in the ‘Cited Publications’ section below. Each stillwater storm surge model output, described below, is provided for the following 5 sea level rise scenarios (Parris et al. 2012): Initial Condition (no change from c. 2000 mean sea level (MSL)), Low (+0.2m from MSL), Intermediate-Low (+0.5m from MSL), Intermediate-High (+1.2m from MSL), and High (+2.0m from MSL).

This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals (DSCs) and hardbottom habitats offshore of the southeastern U.S. It includes a database (.csv text file) containing records of occurrence (presence-absence) for DSCs with associated measures of sampling effort and bottom type from 20 datasets comprised of data from visual field surveys conducted with underwater vehicles. It also includes raster datasets at 100 x 100 m spatial resolution depicting the median and coefficient variation of the predicted occurrence (occupancy probability) for 24 taxa of DSCs (23 genera, 1 family) and hardbottom habitats. Additional raster datasets depict the median and coefficient of variation of the predicted genus richness for the 23 genera of DSCs. The data collection also includes raster datasets at 100 x 100 m spatial resolution depicting each of the 62 spatial environmental predictors considered for fitting the models. For more information, see Poti et al. (2022). The project to compile this model took place between 2016 and 2021, however the model input data range from 2001-2018 and the model output covers the same timeframe.

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 data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

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 data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

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 data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

This dataset contains salt marsh productivity projections under different sea level rise scenarios for the northern Gulf of Mexico (Florida panhandle, Alabama, and Mississippi) using a coupled hydrodynamic-marsh model called Hydro-MEM (Alizad et al. 2016a and 2016b). The modeled outputs were derived through integrated modeling of tidal hydrodynamics (ADCIRC) and marsh productivity (Marsh Equilibrium Model, or MEM) that incorporates dynamic feedbacks among physical and biological processes. The Hydro-MEM model incorporates biological feedback by including the MEM accretion formulation, while also implementing a friction coefficient effect that varies between subtidal and intertidal states. The Hydro-MEM model is capable of capturing the biophysical feedback that modifies relative salt marsh elevation and the biological feedback on hydrodynamics (Alizad et al. 2016a). There are two types of Hydro-MEM model outputs resulting from the Ecological Effects of Sea Level Rise Northern Gulf of Mexico (EESLR-NGOM) project: 1) Salt Marsh Productivity (Low/Medium/High) [202MB total file size, 919 files (unzipped)] and 2) Mean High Water [431 MB total file size, 137 files (unzipped)]. These outputs were generated for areas surrounding the following National Estuarine Research Reserves: Apalachicola (FL), Weeks Bay (AL), and Grand Bay (MS). Each Hydro-MEM model output, described above, is provided for incremental time steps (5 or 20Y) for the following 5 sea level rise scenarios (Parris et al. 2012): Initial Condition (no change from c. 2000 mean sea level (MSL)), Low (+0.2m from MSL), Intermediate-Low (+0.5m from MSL), Intermediate-High (+1.2m from MSL), and High (+2.0m from MSL). Mean high water data are provided for each SLR scenario for two timesteps (2050 and 2100).