2014, 30 meter Esri binary grids of coastal response type probabilities with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83): , http://dx.doi.org/10.5066/F70Z719C, http://woodshole.er.usgs.gov/project-pages/coastal_response/data/NE_region_CR.zip.
The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically [...]
The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.
30 meter Esri binary grids of coastal response type probabilities with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83)
These GIS layers provide the probability of observing a static vs. dynamic coastal response (CR) with respect to predicted sea-level rise for the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment, elevation data, and land cover data. The output displays a probability based on binary end members for the forecast year as defined by a probabilistic framework (a Bayesian network). Because the static vs dynamic coastal response is a binary relationship, the dynamic (i.e. landform or landscape change) coastal response can be derived by subtracting the static response from 1 (and vice versa). These data layers primarily show the distribution of likely coastal response types over a large spatial scale and should therefore be used qualitatively (see Horizontal Positional Accuracy Report).
