Reconstructing Repetitive Flood Exposure Across 78 Events From 1996 to 2020 in North Carolina, USA

Abstract

Measuring flooding through time is crucial for understanding exposure and vulnerability — key components to estimating flood risks and impacts. Yet, historical records of flood inundation are sparse. In this study, we reconstruct flood extents for 78 damaging events in eastern North Carolina between 1996 and 2020 using high-resolution geospatial data and address-level National Flood Insurance Program (NFIP) records. We train random forest models on NFIP-based labeled flood presence and absence data and a suite of geospatial predictors. Then, we predict the probability of flood damage at every 30 m grid cell within our model domain. Our models achieve an average Area Under the Curve of 0.76 and outperform flood extent estimates from process-based and remote sensing models when evaluated against NFIP data for six events. We find that approximately 90,000 (2.3%) buildings in our study area flooded at least once, of which over 20,000 (0.53%) flooded more than once. Our estimate is more than double the number of buildings that filed NFIP claims between 1996 and 2020. Furthermore, 43% of flooded buildings are located outside the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area. Our results illustrate that flood exposure, especially repetitive exposure, is much more widespread than previously recognized. By generating a comprehensive record of past flood extents using address-level observations of damage, we create a first-of-its-kind geospatial database that can be used to identify locations of repetitive flooding. This represents a crucial first step in examining the dynamic relationships between flood exposure, vulnerability, and risk.