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Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region

Schubert, Jochen1; Sanders, Brett1; Mach, Katharine2

Published Jul 08, 2024 on Dryad. https://doi.org/10.5061/dryad.kd51c5bcz

Data files

Jul 08, 2024 version files 439.33 MB

Abstract

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. The data in this repository supports an assessment of the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S.. For the analysis, FSF data was compared to PRIMo-Drain, a flood hazard model that resolves drainage infrastructure and fine resolution drainage dynamics.

In the linked journal manuscript, 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 ~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.