2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)

Paper No. 246-4
Presentation Time: 2:40 PM


LENTZ, Erika E., U.S. Geological Survey, Woods Hole Coastal and Marine Science Center, 384 Woods Hole Rd, Woods Hole, MA 02543, THIELER, E. Robert, U.S. Geological Survey, Woods Hole Coastal and Marine Science Center, 384 Woods Hole Road, Woods Hole, MA 02543, PLANT, Nathaniel, U.S. Geological Survey, 600 4th St. South, St. Petersburg, FL 33701, STIPPA, Saywer, Woods Hole, MA 02543, HORTON, Radley, Center for Climate Systems Research, Columbia University/ NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025 and GESCH, Dean, U.S. Geological Survey, Sioux Falls, SD 57198, erika.lentz@gmail.com

Impacts of sea-level rise (SLR) will vary across the coastal landscape. Inundation is a relatively straightforward impact to understand and model, and dominates many of the online tools and resources to provide SLR decision support; however this approach does not adequately account for the resilience of some areas. A more comprehensive assessment of SLR impacts requires accounting for dynamic change; many areas have the potential to adapt to either preserve their current morphologic or ecologic state or transition to a new one (e.g. a forest becomes a marsh) under various SLR scenarios instead of simply inundating. We present results from a high resolution (30 x 30 m) coastal response model using a probabilistic (Bayesian network) approach that produces the likelihood of observing inundation or dynamic response for the Northeastern U.S. from Maine to Virginia. Relative SLR scenarios derived from multiple sources of information, including Coupled Model Intercomparison Project Phase 5 (CMIP5) models, are presented probabilistically over timescales that complement management and planning horizons. Scenarios are used in combination with elevation and land cover information to predict the probability of dynamic response of a given land cover type. Because we predict probabilities of dynamic response, we can translate our results using standardized uncertainty terminology to demonstrate how results can be applied to inform decision-making as well as highlight research gaps. Results are assessed regionally and at smaller spatial scales (e.g. wildlife refuges, cities) to explore the relationship between the composition and distribution of land cover types in an area and its ability to adapt to SLR. In so doing, we demonstrate a decision support application that highlights locations that may provide buffering or mitigation to preserve natural resources, habitat, and infrastructure. Applying this over a broad scale provides comparison with inundation-model guidance, and allows decision makers to identify and prioritize areas that may provide near- and longer-term tradeoffs in a regional context.