Mapping Flood Hazard Across Mainland China Through a Physics‐Based Global Flood Model With Embedded Reservoir Operation Scheme

ABSTRACT China has complex topography, diverse flood mechanisms, and high population exposure, making it highly vulnerable to flooding, highlighting the need for robust national‐scale hazard assessments to identify flood‐prone regions. However, most existing hazard studies are limited to regional scales or rely on empirical indicator‐based methods that overlook flood dynamics. While some global‐scale studies use physics‐based modeling, they offer little insight into China and rarely consider reservoir operations. This study advances national‐scale flood hazard mapping for China using the hydrodynamic Global Flood Model, CaMa‐Flood (v4.2). Simulations driven by ERA5‐Reanalysis runoff showed stronger agreement with observed streamflow than ERA5‐Land. Flood frequency analysis identified the nonparametric Kernel Density Estimator as the most suitable approach. The resulting 0.05° flood hazard maps reveal that nearly half of mainland China faces some level of 1‐in‐100‐year flood hazard, with 26% in the high to very high category. Incorporating reservoir operations reduced the number of national high hazard areas by up to 31%, underscoring their vital role in mitigation. The derived hazard, population exposure, and GDP‐based analysis provide a data‐driven foundation for national and provincial flood risk management, offering a scalable framework for robust hazard assessment and improved exposure and flood risk evaluation.

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Publication Details

Journal
Journal of Flood Risk Management
Published
2026-01-04
DOI
https://doi.org/10.1111/jfr3.70165
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Mapping Flood Hazard Across Mainland China Through a Physics‐Based Global Flood Model With Embedded Reservoir Operation Scheme

Slobodan P. Simonović, Jayesh Parmar, Subhankar Karmakar, Yuexiao Liu et al.
Journal of Flood Risk Management
Flood Risk Assessment and Management
article

Mapping Flood Hazard Across Mainland China Through a Physics‐Based Global Flood Model With Embedded Reservoir Operation Scheme

Slobodan P. Simonović, Jayesh Parmar, Subhankar Karmakar, Yuexiao Liu, Cheng Zhang
article en

Abstract

ABSTRACT China has complex topography, diverse flood mechanisms, and high population exposure, making it highly vulnerable to flooding, highlighting the need for robust national‐scale hazard assessments to identify flood‐prone regions. However, most existing hazard studies are limited to regional scales or rely on empirical indicator‐based methods that overlook flood dynamics. While some global‐scale studies use physics‐based modeling, they offer little insight into China and rarely consider reservoir operations. This study advances national‐scale flood hazard mapping for China using the hydrodynamic Global Flood Model, CaMa‐Flood (v4.2). Simulations driven by ERA5‐Reanalysis runoff showed stronger agreement with observed streamflow than ERA5‐Land. Flood frequency analysis identified the nonparametric Kernel Density Estimator as the most suitable approach. The resulting 0.05° flood hazard maps reveal that nearly half of mainland China faces some level of 1‐in‐100‐year flood hazard, with 26% in the high to very high category. Incorporating reservoir operations reduced the number of national high hazard areas by up to 31%, underscoring their vital role in mitigation. The derived hazard, population exposure, and GDP‐based analysis provide a data‐driven foundation for national and provincial flood risk management, offering a scalable framework for robust hazard assessment and improved exposure and flood risk evaluation.

Journal of Flood Risk ManagementVol. 19(1)
Western University (CA), Indian Institute of Technology Bombay (IN), China Institute of Water Resources and Hydropower Research (CN)
China Institute of Water Resources and Hydropower Research, Indian Institute of Technology Bombay
Openalex Percentile: Top 98%
Flood Risk Assessment and Management
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