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 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).

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 three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

The sand shoals (modeled) polygons represent the hypothesized distribution of sand shoals of the Gulf of Mexico and US Atlantic Coast based on seafloor characteristics and distance to shoreline variables. Defined by Rutecki et al. (2014), a sand shoal is "a natural, underwater ridge, bank, or bar consisting of, or covered by, sand or other unconsolidated material, resulting in shallower water depths than surrounding areas." In the dataset, attributes characterize shoals with a classification scheme developed with a basis in the Coastal and Marine Ecological Classification Standard (CMECS).

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 elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

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 three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

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 types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

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 Gustav (Louisiana to Florida) Experimental Advanced Airborne Research Lidar (EAARL) survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

These elevation data (in meters) in Apalachee Bay, Florida, have been systematically and variably lowered, mitigating the bias in the lidar DEM and improving its spot elevation accuracy by approximately 69% in Apalachee Bay, Florida. These data span the big bend region of Florida’s gulf coast consisting of Gulf, Franklin, Wakulla, Jefferson, and Taylor counties. The data are in GIS raster format. These adjusted data are now suitable for modeling salt marsh evolution and flood inundation under sea-level rise (SLR) scenarios. Lidar data used in this adjustment were collected on March 01, 2018.

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 (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.