Fragility-Based Flood Risk Modeling to Quantify the Effect of Policy Change on Losses at the Community Level

Flooding is a devastating natural hazard whose consequences include loss of life, and damage to community infrastructure, with even further impacts resulting from interdependencies of physical and non-physical systems. Flood risk prediction is a critical component of a comprehensive risk-informed decision framework and is used in combination with information on community resilience planning strategies, flood impacts, and recovery. In this research, a physics-based flood risk model was developed to determine flood hazard characteristics and their corresponding level of damage at the community level. Fragility functions for the impacted buildings from an extensive past field study were used to capture the effect of policy change in terms of increasing first-floor elevation on flood losses to the building stock in the illustrative example community. The unique point about this study is overcoming the flood-related data scarcity by considering different resources, models, and modern technology using Google Street Map View to collect buildings information. In addition to, the algorithm that was developed to handle the spatial characteristics of these data. Therefore, the provided framework can provide policymakers the ability to explore the financial effect of policy changes and allow them to better mitigate flood risk and increase the community resiliency.