Sea Level Rise

This dataset represents the sea level rise metric based on a model developed by Rob Theiler and associates at USGS Woods Hole, which is a measure of the probability of a focal cell being unable to adapt to predicted inundation by sea level rise. Specifically, whether a site gets inundated by salt water permanently due to sea level rise or intermittently via storm surges associated with sea level rise clearly determines whether an ecosystem can persist at a site and thus its ability to support a characteristic plant and animal community. USGS examined future sea-level rise impacts on the coastal landscape from Maine to Virginia by producing spatially-explicit, probabilistic predictions using sea-level projections (based on an average of two climate change scenarios: RCP 4.5 and 8.5), vertical land movement (due to glacial isostacy) rates, elevation, and land cover data. The data span the coastal zone from an elevation of 5 m inland to -10 m offshore, and are provided for the forecast year 2080.In the layer provided here, the raw coastal response metric produced by USGS is scaled and inverted so that a cell with high probability of exhibiting a dynamic (or adaptive) response to sea level rise gets a zero (low stress) and a cell with low probability of exhibiting a dynamic response gets a value approaching 1 (high stress). In addition, we set all cells classified as sub-tidal to nodata for consistency with other products.