National-Scale Flood Hazard Data Unfit for Urban Risk Management

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo-Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example, by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%-a similar to 1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models. Flooding presents a significant risk to human activities and development, and its impacts have been rapidly increasing over recent decades. However, government flood mapping in the U.S. has not kept pace with adaptation needs, and communities have now turned to other sources of information to inform planning and design decisions. This study examines the uncertainties of flood hazard data available from the First Street Foundation across Los Angeles, California, the second largest city in the U.S. With a comparision to two different models that more fully capture processes known to affect urban flooding, we show concerning levels of uncertainty in the First Street Foundation data at scales of municipalities and properties. These results highlight the need for more robust validation of urban flood hazard models, and caution against overliance of First Street Foundation data for urban flood management. Flood risks are concentrated in urban areas, where national-scale hazard models are less accurate Flood exposure estimates become increasingly uncertain at finer scales and may misrepresent the social distribution of risk Refined and validated urban flood models are needed to effectively and equitably manage increasingly severe flood risks