A process-informed framework linking temperature-rainfall projections and urban flood modeling
Rattachement africain : ch, cn, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract. Predicting changes in urban pluvial flood hazards under climate warming is crucial for risk mitigation and disaster management. A key challenge in simulating future urban flood hazards is the scarcity of high-resolution rainfall projections, particularly at the sub-daily and kilometer scales required for hydrodynamic modeling. We present a cascading process-informed framework that requires minimal observed climatic data, enabling scenario analysis even in data-scarce cities. This framework consists of a distribution‐based spatial quantile mapping (DSQM) method to morph observed rainfall fields conditioned on temperature changes, a stochastic storm transposition (SST) method to account for the spatial variability of urban rainfall, and a rain‐on‐grid hydrodynamic model (AUTOSHED) for efficient simulation of urban pluvial floods at high spatio-temporal resolution. The framework allows the generation of stochastic rainfall fields under different rainfall return levels and regional warming levels. It supports the quantification of changes in future urban flood statistics with detailed hazard maps of inundation depth, duration, and flow velocity. We select the metropolitan area of Beijing (300 km2) as a case study and utilize gridded hourly and 1 km rainfall data to simulate flood evolution at 5 min and 5 m resolution under regional warming levels of 1 °C, 3 °C, and 5 °C relative to the period 1998–2019. Our results show that with rising temperatures, regional storms tend to become more intense but smaller in spatial extent, which may in turn drive increased flood depth, accelerated flow velocity, and deeper inundation, collectively elevating pluvial flood risk. Specifically, mean rainfall intensity increases by 6 %, 11 %, and 20 % (respectively with the warming levels), peak flood depth exhibits a nonlinear increase of 4 %, 7 %, and 8 %, due to the complex interactions of reduced storm area, increased storm intensities, and rainfall spatial variability. The proposed DSQM–SST–AUTOSHED framework offers a data-driven, physically grounded approach to assess urban flood risk under regional warming, and only requires observed rainfall fields and reanalysis temperature datasets, readily accessible from public sources, making the approach easily extendable to other cities.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A process-informed framework linking temperature-rainfall projections and urban flood modeling
- Date Crossref
- 12/08/2026
- Éditeur
- Copernicus GmbH
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Lausanne Expertise Center for Climate Extremes pays non établi dans la noticeUniversité ou école supérieure
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Tsinghua University State Key Laboratory of Hydro-science and Engineering pays non établi dans la noticeUniversité ou école supérieure
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University of Wisconsin–Madison pays non établi dans la noticeUniversité ou école supérieure
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ETH Zurich pays non établi dans la noticeUniversité ou école supérieure
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Beijing Water Science and Technology Institute pays non établi dans la noticeStructure de recherche
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University of Wisconsin-Madison Department of Civil and Environmental Engineering pays non établi dans la noticeUniversité ou école supérieure
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Institute of Environmental Engineering pays non établi dans la noticeStructure de recherche
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Future Cities Laboratory pays non établi dans la noticeStructure de recherche
Expertise Center for Climate Extremes — University of Lausanne, State Key Laboratory of Hydro-science and Engineering — Tsinghua University et University of Wisconsin–Madison, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.