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 la...
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| Format: | Article |
| Language: | English |
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Wiley
2024-07-01
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| Series: | Earth's Future |
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| Online Access: | https://doi.org/10.1029/2024EF004549 |
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| author | Jochen E. Schubert Katharine J. Mach Brett F. Sanders |
| author_facet | Jochen E. Schubert Katharine J. Mach Brett F. Sanders |
| author_sort | Jochen E. Schubert |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6ef0bf88f3b461e81011b9e9138044e |
| institution | Kabale University |
| issn | 2328-4277 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj-art-e6ef0bf88f3b461e81011b9e9138044e2025-01-29T07:58:52ZengWileyEarth's Future2328-42772024-07-01127n/an/a10.1029/2024EF004549National‐Scale Flood Hazard Data Unfit for Urban Risk ManagementJochen E. Schubert0Katharine J. Mach1Brett F. Sanders2Department of Civil and Environmental Engineering University of California, Irvine Irvine CA USADepartment of Environmental Science and Policy Rosenstiel School of Marine, Atmospheric, and Earth Science University of Miami Miami FL USADepartment of Civil and Environmental Engineering University of California, Irvine Irvine CA USAAbstract 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.https://doi.org/10.1029/2024EF004549flood riskexposureurban floodingsocial inequalitieshydrodynamic modelingclimate adaptation |
| spellingShingle | Jochen E. Schubert Katharine J. Mach Brett F. Sanders National‐Scale Flood Hazard Data Unfit for Urban Risk Management Earth's Future flood risk exposure urban flooding social inequalities hydrodynamic modeling climate adaptation |
| title | National‐Scale Flood Hazard Data Unfit for Urban Risk Management |
| title_full | National‐Scale Flood Hazard Data Unfit for Urban Risk Management |
| title_fullStr | National‐Scale Flood Hazard Data Unfit for Urban Risk Management |
| title_full_unstemmed | National‐Scale Flood Hazard Data Unfit for Urban Risk Management |
| title_short | National‐Scale Flood Hazard Data Unfit for Urban Risk Management |
| title_sort | national scale flood hazard data unfit for urban risk management |
| topic | flood risk exposure urban flooding social inequalities hydrodynamic modeling climate adaptation |
| url | https://doi.org/10.1029/2024EF004549 |
| work_keys_str_mv | AT jocheneschubert nationalscalefloodhazarddataunfitforurbanriskmanagement AT katharinejmach nationalscalefloodhazarddataunfitforurbanriskmanagement AT brettfsanders nationalscalefloodhazarddataunfitforurbanriskmanagement |