Characterizing the number and nature of historical floods and implications for exposure characterization in New England, 2000–2018 - Muskaan Khemani; Zachary Popp; M Patricia Fabian; Jonathan I Levy; Chad W Milando; Keith R Spangler; Mariangelí Echevarría-Ramos; Amruta Nori-Sarma (Environmental Research: Health, 2025)
Flooding has been shown to impact physical and mental health, but such studies use different flood datasets that may disagree on the number and nature of flood events with important health implications of such exposure misclassification. A systematic examination of how different flooding data sources characterize exposure is necessary to better understand the effects of flooding on health. We characterized the number and nature of historical floods in the New England region of the United States (US), aggregated at county resolution, using the global flood database (GFD), dartmouth flood observatory (DFO), national weather service (NWS) storm events database, US geological survey (USGS) stream gages, and Parameter-elevation Relationships on Independent Slope Model (PRISM) precipitation data from 2000 to 2018. We characterize flooding frequency for hydrometeorological data sources (i.e. USGS and PRISM) and use these continuous daily time series to quantify the intensity of floods across different data sources. We then compared all data sources with respect to how they captured flooding in extent, frequency, duration, and intensity. Flood frequency ranged from five (GFD) to 213 distinct events (PRISM) across data sources, with flood duration from a median of three (NWS/USGS) to 14 d (GFD) and counties affected ranging from a median of two (USGS) to 14 d (GFD). The DFO (74.6%), GFD (69.2%), and USGS (67.0%) had the greatest percentages of identified county flood events that were concordant with at least one other flood data source. Data sources differed in flood geographic patterns, timing, flood intensity, and the number of impacted counties. Our findings reinforce the importance of using multiple flood data sources to characterize the impact of flooding in health studies. Selecting the most appropriate data sources and metrics given the health outcome of interest is key, as well as evaluating the sensitivity of conclusions to alternative relevant flooding exposure metrics.