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

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.