Assessment of the Coastal Landscape Response to Sea-Level Rise Using a Decision-Support Framework

Tuesday, 16 December 2014
Erika E Lentz1, E Robert Thieler1, Nathaniel G Plant2, Sawyer Stippa1, Radley M Horton3 and Dean B Gesch4, (1)U.S. Geological Survey, Woods Hole, MA, United States, (2)U.S Geological Survey, Coastal and Marine Science Center, Saint Petersburg, FL, United States, (3)Columbia University/NASA GISS, New York, NY, United States, (4)US Geological Survey, Sioux Falls, SD, United States
Identifying the form and nature of coastal landscape changes that may occur in response to future sea-level rise (SLR) is essential to support decision making for resource allocation that improves climate change resilience. Both natural ecosystems and the built environment are subject to these changes and require associated resilience assessments. Existing assessments of coastal change driven by SLR typically focus on two categories of coastal response: 1) inundation by flooding as the water level rises; and 2) dynamic change resulting from movement of landforms and/or ecosystems. Results from these assessments are not always straightforward to apply in a decision support context, as it can be unclear what the dominant response type may be in a given coastal setting (e.g., barrier island, headland, wetland, forest). Furthermore, an important decision support element is to capture and clearly convey the associated uncertainty of both the underlying datasets (e.g., elevation) and climate drivers (e.g., relative SLR).

We developed a Bayesian network model of SLR assessment that uses publicly available geospatial datasets—land cover, elevation, and vertical land movement—and their associated uncertainties to generate probabilistic predictions of those areas likely to inundate versus dynamically respond to various SLR scenarios. SLR projections were generated using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Model outputs include predictions of potential future land-surface elevation and coastal response type at landscape (>100 km) to local (5-10 km) scales for the Northeastern U.S., commensurate with decision-making needs. The probabilistic approach allows us to objectively and transparently describe prediction certainty to decision makers. From this approach, we are also able to highlight areas in which more data or knowledge may be needed to provide a stronger basis for decision making.