| 10 Sep 2025
A process-informed framework linking temperature-rainfall projections and urban flood modeling
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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
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The authors present a process-based framework that integrates temperature-conditioned rainfall projection, stochastic storm transposition, and high-resolution hydrodynamic flood modelling to assess future pluvial flood hazards. The topic is relevant to the cope of HESS, and the manuscript is well-written, concise, and straightforward to understand. The work is methodologically innovative and supported by an impressive modelling effort. However, the following points need to be addressed before publication. My suggestion is for a revision of minor to moderate extent.
1. The framework explicitly assumes that changes in short-duration extreme rainfall are primarily thermodynamic, with limited influence from dynamical changes (Section 2). While this assumption is discussed later, it underpins the entire DSQM–SST-AUTOSHED cascade. Can the authors more rigorously justify this assumption for the Beijing region, particularly given evidence of circulation-driven changes in East Asian summer rainfall? A sensitivity or discussion contrasting thermodynamic vs. dynamic contributions would strengthen the credibility of the projections.
2. In this study, 1-km CMORPH-based rainfall is employed to drive street-scale pluvial flood simulations. With emerging radar datasets at 500 m or 100 m that more finely capture convective structures, please clarify how the proposed framework would be extended to incorporate these higher-resolution inputs for future flood projection.
3. In the absence of a detailed sewer network, a uniform drainage rate is subtracted over road surfaces. How realistic is this assumption under extreme rainfall events at the 100-year return level? The authors should clarify whether drainage saturation or failure could alter the relative changes in inundation depth and duration under warming.
4. The results indicate that storms become more intense but spatially smaller, yet flood depth and velocity still increase. It deserves deeper discussion why reduced storm areas result in more severe pluvial flood hazards.
5. The framework is positioned as broadly applicable, yet it is tailored to convective storms and pluvial flooding. Please more clearly delimit the domain of applicability. In particular, under what climatic and surface conditions would the current DSQM–SST–AUTOSHED cascade not be expected to perform well, and how might this influence use by practitioners?
6. The terms ‘regional warming levels’ and ‘warming scenarios’ are used interchangeably. Please choose one primary term, such as ‘regional warming levels’, and define it clearly once (relative to 1998-2019 baseline). Use it consistently in figures, captions, and text.