Coastal Change Likelihood (CCL): Methodology and expansion into other U.S. regions

Keywords: coastal change, hazards, Great Lakes, decision support, GIS
Abstract Type: Poster Abstract

Abstract

Coastal and climate change hazards are increasingly impacting coastal landscapes and their communities with varying and compounding effects. The U.S. Geological Survey’s (USGS) Coastal Change Likelihood (CCL) assessment synthesizes existing coastal datasets that describe the coast and the hazards impacting it and predicts spatially explicit change likelihood within the coming decade. CCL coverage is being expanded beyond the pilot region in the Northeast U.S. into other areas, including the Great Lakes region of the U.S. Through a Great Lakes Restoration Initiative (GLRI) collaboration among federal, state, and academic research teams and stakeholders, modifications to the CCL methodology, initially applied to an open-ocean coastal setting, were developed for the Great Lakes to produce hazard layers that include static lake levels, annual ice cover, and wave and surge predictions under a variety of future climate scenarios. Using ArcGIS Pro, we developed geoprocessing methods for consolidating landscape data from multiple datasets to address some gaps in the source data to produce the best possible product from available sources. These methods could potentially be addressed for other U.S. regions where data may be more limited. Initial results, data gap solutions, and next steps in CCL product development for the Great Lakes and beyond are presented along with some best practices and examples of CCL data product application for coastal management and decision support purposes.