Zhou et al. (2025) Unraveling nonlinear urban waterlogging responses to rainfall structure: A data-driven analysis in a highly urbanized megacity

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-10-02
• Authors: Zhengzheng Zhou, Shuguang Liu, Li Sun, Yan Liu
• DOI: 10.1016/j.jhydrol.2025.134349

Research Groups

• College of Civil Engineering, Tongji University, Shanghai, China
• State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China
• Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, China Meteorological Administration, Shanghai, China
• Shanghai Flood and Drought Disaster Prevention Technology Center, Shanghai, China

Short Summary

This study investigates the nonlinear relationships between rainfall structure and urban waterlogging in Shanghai using a long-term dataset and data-driven models. It reveals that short-term rainfall intensities, temporal asymmetry, and peak-mean ratios are more critical drivers of waterlogging than total rainfall volume, exhibiting complex non-monotonic and threshold effects.

Objective

• To examine the nonlinear relationships between rainfall structure and urban waterlogging responses in Shanghai, a highly urbanized megacity.
• To quantify the nonlinear drivers of urban waterlogging and interpret model outputs using advanced data-driven techniques.

Study Configuration

• Spatial Scale: A highly urbanized megacity (Shanghai, China), utilizing a dense gauge network for co-monitored rainfall and waterlogging records.
• Temporal Scale: Long-term dataset of rainfall and waterlogging records, focusing on short-duration extreme events. Specific duration not provided.

Methodology and Data

• Models used: Gradient Boosting Regression (GBR) coupled with SHapley Additive exPlanations (SHAP) for quantifying nonlinear drivers and interpreting model outputs; Clustering analysis for identifying representative rainfall and response regimes.
• Data sources: Unique long-term dataset of co-monitored rainfall and waterlogging records collected from a dense gauge network within Shanghai. A comprehensive suite of spatiotemporal rainfall indicators was derived.

Main Results

• Short-term rainfall intensities, temporal asymmetry, and peak-mean ratios consistently explain urban waterlogging behavior more effectively than total rainfall volume.
• Response surface analysis revealed non-monotonic patterns and threshold effects in rainfall-waterlogging interactions, highlighting their complex nature.
• The study demonstrates that conventional flood models often overlook the nonlinear and heterogeneous dynamics of urban flooding by emphasizing total rainfall amounts or assuming linear responses.

Contributions

• Provides a structure-aware, interpretable modeling framework for urban hydrology, addressing the limitations of conventional models that rely on idealized rainfall inputs or assume linear responses.
• Offers a transferable data-driven approach for urban flood risk assessment and resilience planning in megacities facing similar convective rainfall regimes.
• Bridges data-driven modeling with practical flood management by quantifying nonlinear drivers and interpreting complex rainfall-waterlogging interactions.

Funding

Not specified in the provided text.

Citation

@article{Zhou2025Unraveling,
  author = {Zhou, Zhengzheng and Liu, Shuguang and Sun, Li and Liu, Yan},
  title = {Unraveling nonlinear urban waterlogging responses to rainfall structure: A data-driven analysis in a highly urbanized megacity},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134349},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134349}
}