IDENTIFYING POTENTIAL IMPACTS OF SEA-LEVEL RISE TO SUPPORT DEVELOPMENT OF CLIMATE ADAPTION STRATEGIES

Sea-level change has long been recognized as a key driver in the evolution of barrier islands and associated nearshore and back-barrier environments. Through paleogeographic reconstructions, process studies, and other research endeavors, the geoscience community has developed a wealth of information on the scope and scale of physical and biological changes than could be expected for a given sea-level rise (SLR) scenario. However, the scientific literature and national/international assessments show there is still much to be learned about the impacts of SLR and other climate phenomena on coastal environments and dependent ecosystems. Addressing these science gaps becomes more urgent in light of projections of accelerated SLR over the coming decades. These projections have garnered the attention of policy-makers, resource managers, and the public, resulting in increased demand for accurate, scientifically defensible information on impacts at temporal and spatial scales relevant to decision-making. One of our critical challenges then becomes how to translate scientific understanding of SLR into actionable information that can be accessed, understood, and used by resource managers and others responsible for responding or adapting to climate impacts.

This presentation will feature recent projects undertaken by the National Oceanic and Atmospheric Administration’s (NOAA’s) Coastal Services Center and its partners to analyze and visualize SLR impacts on coastal environments and communities. For example, NOAA partnered with the U.S. Geological Survey to develop web-based SLR visualizations for two pilot regions (Delaware and Mississippi/Alabama). This product has aided resource managers in identifying vulnerable regions and in communicating these vulnerabilities to elected officials and other decision-makers. NOAA also partnered with the North Inlet/Winyah Bay (SC) National Estuarine Research Reserve to explore the limitations, assumptions, underlying algorithms, and data requirements of the Sea Level Affecting Marshes Model (SLAMM), which predicts coastal habitat changes for user-specified SLR estimates. These projects will show some of the assumptions, challenges, and opportunities associated with translating scientific research for use in the policy and management arenas.