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Flooding occurs at different scales and unevenly affects urban populations based on the broader social, ecological, and technological system (SETS) characteristics particular to cities. As hydrological models improve in spatial scale and account for more mechanisms of flooding, there is a continuous need to examine the re- lationships between flood exposure and SETS drivers of flood vulnerability. In this study, we related fine-scale measures of future flood exposure—the First Street Foundation’s Flood Factor and estimated change in chance of extreme flood exposure—to SETS indicators like building age, poverty, and historical redlining, at the parcel and census block group (CBG) scales in Portland, OR, Phoenix, AZ, Baltimore, MD, and Atlanta, GA. We used standard regression models and accounted for spatial bias in relationships. The results show that flood exposure was more often correlated with SETS variables at the parcel scale than at the CBG scale, indicating scale dependence. However, these relationships were often inconsistent among cities, indicating place-dependence. We found that marginalized populations were significantly more exposed to future flooding at the CBG scale. Combining newly-available, high-resolution future flood risk estimates with SETS data available at multiple scales offers cities a new set of tools to assess the exposure and multi-dimensional vulnerability of populations. These tools will better equip city managers to proactively plan and implement equitable interventions to meet evolving hazard exposure.
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National‐Scale Flood Hazard Data Unfit for Urban Risk Management
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. 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 ∼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.
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- PAR ID:
- 10531288
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth's Future
- Volume:
- 12
- Issue:
- 7
- ISSN:
- 2328-4277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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