Characterizing the Uncertainty and Feasibility of Generating Nationwide Probabilistic Flood Depths for Risk Planning

There is a substantial gap in our ability to measure and communicate flood risk across the U.S. driven by the lack of continuous probabilistic flood depth data. The Federal Emergency Management Agency's (FEMA) Hazus Program provides state-of-the-art methodologies for estimating structure-level losses due to natural hazards using hazard inputs from authoritative agencies like the U.S. Geological Survey for earthquakes and the National Oceanic and Atmospheric Administration for hurricanes. However, responsibilities for measuring and storing flood hazard information remain distributed across siloed groups. FEMA regulatory flood maps do not have sufficient detail or geographic coverage to produce nationwide, structure-level flood risk measurements, and engineering-grade hydrologic modeling outputs are too computationally intensive to be generated nationwide. Event-specific flood hazard data do not capture the full range of risk in a given community. FEMA's Hazus Program and hydrology experts from the Pacific Northwest National Laboratory (PNNL) are collaborating to fill this gap in national flood hazard and risk information. We are completing a feasibility study for the automated generation of nationwide multi-return period depth grids using an extreme flood event model developed by PNNL using 2D hydrodynamics. The project team is comparing flood depths and associated risk measurements derived from PNNL models at representative locations across the U.S. with flood data derived from FEMA regulatory information and Hazus models. These comparisons will help the risk planning community understand how different flood hazard sources impact risk assessment results. If sufficiently accurate depth grids and flood loss estimates can be produced using PNNL methodologies at test locations, the PNNL methodology can be expanded nationwide using contextual guidance provided by this feasibility study. The resulting nationwide, probabilistic flood depth data can be used in risk assessments and planning for communities where better flood hazard data do not exist, contributing to a more data-driven risk reduction policy-making process across the U.S.