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Oral

2025 Symposium on Hydro-climate and Climate Change (Watershed)

Advancing the Science of Extreme Storms to Support FEMA’s Future of Flood Risk Data Initiative

Monday, May 19, 2025
10:15 AM – 10:30 AM Alaska Daylight Time (AKDT)
Room: Room 1
Abstract Submission: This presentation provides an overview of joint efforts by FEMA, the US Army Corps of Engineers, the University of Wisconsin-Madison, and the University of North Carolina at Chapel Hill to improve the utilization of extreme rainfall data in flood hazard and risk modeling. The ultimate goal is to support FEMA’s Future of Flood Risk Data (FFRD) initiative, which will provide a comprehensive picture of flood hazard and risk across the country using new data, modeling, and information sharing technologies. A critical element within FFRD is more realistic treatment of extreme rainfall and its role in both pluvial and fluvial flood hydrology across a wide range of watershed scales. To accomplish this, FFRD will combine modern gridded precipitation data, Stochastic Storm Transposition (SST), and physically-based distributed hydrologic and hydraulic modeling. High-resolution gridded precipitation data provides unprecedented depictions of both watershed- and subwatershed-scale extreme rainfall. These data help to drive SST, which is a method to create large numbers of realistic spatiotemporal rainstorm scenarios. When combined with distributed hydrologic models such as HEC-HMS, SST is able to generate defensible, reproducible flood simulations for flood frequency analysis and flood hazard mapping. Taken together, these tools offer important benefits over more traditional floodplain hazard estimation techniques. Nonetheless, important challenges remain. These include identifying the geospatial domains over which SST should be applied, how to perform SST in ways that ensures realism both across spatial scales and over seasonal to interannual timescales, and how to handle the spatiotemporally-varying quality of gridded precipitation data. Additional challenges lie in how to scale these methodologies from individual watershed case studies to an objective, transparent, and reproducible nationwide program.

Learning Objectives/Expected Outcome (Optional) :