Zou et al. (2026) A process-informed framework linking temperature-rainfall projections and urban flood modeling

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-08-12
• Authors: Wenyue Zou, Ruidong Li, Jovan Blagojević, Péter Molnár, Yue Zhu, Yongkun Li, Guangheng Ni, Nadav Peleg
• DOI: 10.5194/hess-30-5097-2026

Research Groups

• Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
• Expertise Center for Climate Extremes, University of Lausanne, Lausanne, Switzerland
• Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
• State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
• Future Cities Laboratory, Singapore-ETH Centre, Singapore
• University of Macau, Macao SAR, China

Short Summary

This study presents a process-informed framework linking temperature-rainfall projections and urban flood modeling. The framework is designed to project future changes in regional storm and flood statistics under climate warming conditions.

Objective

• To develop a computationally efficient approach for assessing future changes in urban pluvial floods, considering the scarcity of high-resolution rainfall projections.
• To link temperature-rainfall projections with urban flood modeling using a process-informed framework that addresses the challenges of simulating future urban flood hazards.

Study Configuration

• Spatial Scale: 300 km^2 metropolitan area of Beijing, China
• Temporal Scale: Hourly and daily time steps for rainfall data, and sub-daily to hourly time scales for flood simulations

Methodology and Data

• Models used:
  • Distribution-based Spatial Quantile Mapping (DSQM) method
  • Stochastic Storm Transposition (SST) model
  • AUTOSHED urban pluvial flood model
• Data sources:
  • Gridded hourly rainfall fields at 1 km resolution from CMORPH product
  • Hourly and 25 km near-surface air temperature data from ERA5 climate reanalysis
  • Digital elevation model (DEM) with 0.5 m resolution
  • Land use map, soil hydraulic properties, leaf area index, building footprint, and road network layers

Main Results

• The framework projects future changes in regional storm and flood statistics under climate warming conditions.
• The results show that with rising temperatures, regional storms tend to become more intense but smaller in spatial extent, leading to increased local flood depth, accelerated flow velocity, and deeper inundation.
• Mean rainfall intensity increases by 6%, 11%, and 20% (respectively) with the warming levels, while peak flood depth exhibits a nonlinear increase of 4%, 7%, and 8%.

Contributions

• The proposed framework offers a data-driven, physically grounded, and efficient approach to assess urban flood risk under regional warming.
• The study demonstrates the importance of considering spatial variability in rainfall when estimating future changes in urban pluvial floods.

Funding

• This research was funded by the Swiss National Science Foundation (SNSF) through project number 200021_172626.

Citation

@article{Zou2026processinformed,
  author = {Zou, Wenyue and Li, Ruidong and Wright, Daniel B. and Blagojević, Jovan and Molnár, Péter and Hussain, Mohammad A. and Zhu, Yue and Li, Yongkun and Ni, Guangheng and Peleg, Nadav},
  title = {A process-informed framework linking temperature-rainfall projections and urban flood modeling},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-5097-2026},
  url = {https://doi.org/10.5194/hess-30-5097-2026}
}