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Woods Hole Coastal and Marine Science Center > Coastal Landscape Response to Sea-Level Rise Assessment for the Northeastern United States > Landscape Change Predictions

Coastal Landscape Response to Sea-Level Rise Assessment for the Northeastern United States

Landscape Change Predictions

Diagram of Bayesian network used in model predictions.

Figure 3. Conceptual diagram showing the structure of the Bayesian network used to assess potential sea-level rise impacts on the coastal landscape. Here sea-level projections (SL) and vertical land movement (VLM) are driving forces (green boxes), elevation (E) and land cover (LC) are boundary conditions (red boxes), and adjusted elevation (AE) and coastal response type (CR) (blue boxes) the sea-level rise response variables. The equation shown relates SL, VLM, and E to product AE predictions, where x indicates dependence on the geospatial location and t indicates dependence on time. AE predictions and LC are related through expert knowledge to produce CR predictions. [Larger Figure]

Sea-level rise (SLR) impacts on the coastal landscape are presented here as: 1) level of landscape submergence (adjusted land elevation with respect to projected mean high water levels); and 2) coastal response type characterized as either static (for example, inundation) or dynamic (for example, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s) for the Northeastern U.S. Predicted outcomes can be linked with habitat vulnerability information and used to develop adaptive management and resource allocation strategies at regional and local levels that can aid interagency and interdisciplinary management planning efforts.

The datasets presented here are intended to integrate with habitat models produced by others to meet decision making needs. For example, SLR scenarios are produced for time periods that either match those being used by collaborators or are generally common planning horizons for decision makers. SLR scenarios used as model inputs are generated using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 (CMIP5) models following Representative Concentration Pathways (RCPs) 4.5 and 8.5 in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5)Vertical land movement rates which vary throughout the region due to glacial isostasy and other factors are incorporated into relative sea-level projections. The use of high-quality elevation data allows the projection of water level increases across the landscape to define a general level of potential submergence. The land cover base map from a corresponding habitat impacts model is used to ensure results are produced at a resolution and coverage readily usable by collaborators; land cover information from this map is coupled with landscape submergence information to identify environment types likely to dynamically respond vs. inundate. All datasets are used to develop a predictive model (a Bayesian network) that integrates the sea-level, elevation and land cover data with coastal response probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. A full description of the methods and datasets used in this assessment can be found in:

Lentz, E. E., Stippa, S.R., Thieler, E.R., Plant, N.G., Gesch, D., and Horton, R.M., 2015, Evaluating coastal landscape response to sea-level rise in the northeastern United States—Approach and methods: U.S. Geological Survey Open-File Report 2014-1252, http://dx.doi.org/10.3133/ofr20141252.

Figure 6b.  Inset showing predictions of coastal response type for four prediction time steps at location in Blackwater National Wildlife Refuge, VA. Figure 6a. Inset showing predictions of adjusted elevation, adjusted elevation probabilities, and coastal response type at Plum Island, MA.

Figure 4. Map showing extent and coverage of coastal response type predictions; insets display different prediction types and geospatial variability through time.

  1. 2050s predictions of adjusted elevation, adjusted elevation probabilities, and coastal response type at Plum Island, MA.
  2. Predictions of coastal response likelihood for the four prediction time steps at a Maryland salt marsh.

[Larger Figure]


 

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Figure 4A.
Figure 4B.

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