Towards urban place-based resilience modeling: Mixed methods for a flood resilience assessment index

Abstract

Large-scale socioeconomic vulnerability models commonly used in flood hazard assessments grapple with data limitations and struggle to fully capture diversity in vulnerability and resilience stemming from America’s sociopolitical history. In response, we developed a prototype for a place-based Flood Resilience Assessment Index (FRAI) using tract-level geographies that illustrates human-centric frameworks for quantifying flood resilience in the U.S. For these purposes, we define flood resilience as the likelihood a tract will rebound from a flood disaster. This framework can be used in tandem with flood risk models. We employ mixed methods in geospatial processing, including dasymetric interpolation and network analysis to model access. We also standardize variables by percentage to enable temporal analyses and equity-centered narrative framing. While the resulting scores for a five-county pilot study correlate with those of leading vulnerability indices, FRAI leverages diverse data sources and novel methods to represent the changing landscapes, resources, and needs of urban cores and growing suburbs. Future trajectories for FRAI will continue to define and refine methods for diverse datasets, employ participatory methods for emergency managers and residents of flood-prone communities in value-setting, weighting, and validation, and identify policy and practice avenues.