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An effort to better understand the effects that sea-level rise (SLR) is likely to have on the coastal zone has brought together a network of Department of Interior collaborators and academic partners through the DOI North Atlantic Landscape Conservation Cooperative (NALCC) and Northeast Climate Science Center. The USGS Sea-Level Rise Hazards and Decision-Support project is developing decision-support models and tools through collaboration with researchers, resource managers and decision makers from federal and state agencies and non-governmental organizations using a structured decision making (SDM) process.
Structured Decision Making is a framework that helps to articulate and analyze problems by defining the decision problem and fundamental objectives at the outset. Explicitly defining complex problems can be particularly useful for multidisciplinary tasks, in which nuances in communication and terminology can easily cause misunderstanding or misinterpretation that can in turn affect results. The application of SDM at the outset of a research project helps to limit communication errors and help ensure the result being generated is one that will provide information necessary to meet the specified objectives.
To define decision-support needs for landscape conservation and adaptation in response to SLR, the NALCC brought together a group of eight coastal resource managers and researchers from the Northeastern United States to attend an SDM workshop in September 2012. Central to the decision problem, objectives, and alternative management actions defined in the SDM workshop, is an understanding of the effects of SLR on the landscape. Specifically, understanding where land that may provide important habitat or ecosystem services is likely to adapt to SLR vs. become inundated is essential to: 1) inform corresponding habitat models; and 2) map out alternative management strategies to optimize conservation efforts and allocate resources. The study area was defined as the entire NALCC region which extends from Maine to Virginia. SLR predictions are also needed at resolutions (30 m) and time intervals (e.g., 2030s and 2080s) commensurate with corresponding habitat models to ensure SLR outcomes can be readily integrated with these results.