Decision Support for Building Flood Resilience: A Reproducible and Streamlined Approach to Geospatial Modeling of the Community Rating System

The Community Rating System (CRS) is a federal incentive program designed to help localities in the United States build flood resilience. Effective participation in the program requires extensive geospatial modeling, which can put a strain on communities operating with limited funding and staff time. Reproducibility, in particular, is a major shortcoming of existing resources related to the CRS. To address these challenges, we develop and demonstrate a more streamlined approach to CRS modeling using open-source software tools to automate workflows. Methodological improvements are made with respect to three flood control strategies: Open Space Preservation (OSP) in the floodplain, Higher Regulatory Standards (HRS) in developed areas, and Acquisition & Relocation (A&R) of repetitively flooded structures. This study helps communities more effectively navigate the CRS program in a number of ways. By including OSP, HRS, and A&R, we provide a holistic approach to flood mitigation that conforms to local conditions. By quantifying insurance savings, we strengthen a communitys bargaining power to implement adaptation programs and enforce related policies. Finally, by identifying areas that are most susceptible to flood damage, we help communities prioritize resilience-building activities. The outcome of this study is a general workflow and accompanying online tool for mapping OSP, HRS, and A&R opportunities, estimating potential flood insurance discounts in the community, and distributing a vulnerability index with a machine learning algorithm. This serves to help local governments visualize geospatial data, disseminate information to the public, and more effectively engage stakeholders in the decision-making process.