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.