Coastal Adaptation to Sea level rise Tool (COAST)
Developed at the University of Southern Maine with funding from the EPA, the COAST program predicts damages from varying amounts of sea level rise (SLR) and storms of various intensities, and evaluates relative benefits and costs of response strategies. Although it is a technical tool, COAST can connect the technical with the social, political, and economic realities of local adaptation. Stakeholders are involved when they parametrize the model. Being entirely driven by the participants, the tool uses locally derived data on vulnerable assets such as real estate, economic activity, infrastructure, and natural resources.
The COAST approach assesses costs and benefits of adaptations to SLR scenarios by incorporating a variety of existing tools and datasets, including the U.S. Army Corps of Engineers' depth-damage functions; NOAA's Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model; and other flood methods, as well as projected SLR scenarios over time, property values, and infrastructure costs, into a comprehensive GIS-based picture of potential economic damage. COAST displays the location-specific avoided costs associated with particular adaptive actions, along with the costs incurred by implementing those actions, to assist coastal municipalities in selecting appropriate strategies. This tool also has applications for inland areas that include analyzing and displaying the economic impacts of any potential hazard event that can be mapped (e.g., extreme rainfall, fire) as well as the social and environmental impacts of those events. COAST bundles processes in Excel and the ArcGIS ArcGlobe application in the ArcGIS 3D Analyst extension.
Using 3D capabilities of the ArcGlobe application, economic floodplains can be modeled that show real property and building contents loss, lost infrastructure value, lost economic output, displaced persons, and affected natural resources. This approach allows modeling of SLR heights and storm surge frequency and intensity. Combined outputs of multiple future scenarios provide an opportunity for stakeholders to select future conditions that match their expectations and visualize the predicted damages using both action and no-action scenarios.
Author or Affiliated User:
- University of Southern Maine
- U.S. Environmental Protection Agency (EPA)
- Modeling tool