Clusters of community exposure to coastal flooding hazards based on storm and sea level rise scenarios—implications for adaptation networks in the San Francisco Bay region
Sea level is projected to rise over the coming decades, further increasing the extent of flooding hazards in coastal communities. Efforts to address potential impacts from climate-driven coastal hazards have called for collaboration among communities to strengthen the application of best practices. However, communities currently lack practical tools for identifying potential partner communities based on similar hazard exposure characteristics. This study uses statistical cluster analysis to identify similarities in community exposure to flooding hazards for a suite of sea level rise and storm scenarios. We demonstrate this approach using 63 jurisdictions in the San Francisco Bay region of California (USA) and compare 21 distinct exposure variables related to residents, employees, and structures for six hazard scenario combinations of sea level rise and storms. Results indicate that cluster analysis can provide an effective mechanism for identifying community groupings. Cluster compositions changed based on the selected societal variables and sea level rise scenarios, suggesting that a community could participate in multiple networks to target specific issues or policy interventions. The proposed clustering approach can serve as a data-driven foundation to help communities identify other communities with similar adaptation challenges and to enhance regional efforts that aim to facilitate adaptation planning and investment prioritization.
KeywordsClimate change Adaptation Flooding Exposure Cluster analysis
The authors would like to thank Sandrine Dudoit for early discussions about clustering stability. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.
This study is part of the Resilient Infrastructure as Seas Rise (RISeR) project, supported by the National Science Foundation Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) Award 1541181.
US Geological Survey (USGS)-affiliated authors are supported by the USGS Land Change Science Program and the USGS Coastal and Marine Geology Program.
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