Landscape position-based habitat modeling for the Alabama Barrier Island feasibility assessment at Dauphin Island

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, "medium" storminess and "high" storminess based on storm climatology data) and 4 sea-level scenarios (that is, a "low" increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).