New perspectives on urban stormwater management in China, with a focus on extreme rainfall events

Climate change, especially the rise in extreme rainfall events, combined with urbanization, poses significant challenges to traditional hydrological methodologies. These developments call for researchers to reassess conventional analytical and numerical approaches and make the necessary adjustments to address these evolving conditions. In this study, we begin by analyzing the patterns and mechanisms of the extreme rainfall event in Zhengzhou on July 20, 2021 (“7.20” extreme rainfall in Zhengzhou). Building on this analysis, we offer insights and propose solutions to key issues in urban stormwater management. A bibliometric analysis of the Zhengzhou event indicates that urban flooding is primarily driven by climate change, with urbanization being a secondary factor. Additionally, the integration of machine learning with traditional numerical hydrological models, such as the widely used SWMM model, has emerged as a research trend to enhance the accuracy and comprehensiveness of parameter estimation. New urban stormwater management techniques and strategies are essential for improving urban flooding resilience, including a three-level collaborative governance system, urban green-gray-blue facilities, smart integrated management, and digital twin technologies. Furthermore, fully coupled water management models and strategies that incorporate all elements of hydrological cycles are essential for addressing complex urban hydrological challenges.

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Publication Details

Journal
Natural Hazards
Published
2024-11-06
DOI
https://doi.org/10.1007/s11069-024-06994-3
Citations
3
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.85

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article

New perspectives on urban stormwater management in China, with a focus on extreme rainfall events

Luwen Zhuang, Jun Xia, Kairong Lin, Pengjun Li et al.
3 citations
Natural Hazards
Flood Risk Assessment and Management
0.85
article

New perspectives on urban stormwater management in China, with a focus on extreme rainfall events

Luwen Zhuang, Jun Xia, Kairong Lin, Pengjun Li, Dunxian She, Qiang Wang, Chen Qiu-ling
article en
3 citations

Abstract

Climate change, especially the rise in extreme rainfall events, combined with urbanization, poses significant challenges to traditional hydrological methodologies. These developments call for researchers to reassess conventional analytical and numerical approaches and make the necessary adjustments to address these evolving conditions. In this study, we begin by analyzing the patterns and mechanisms of the extreme rainfall event in Zhengzhou on July 20, 2021 (“7.20” extreme rainfall in Zhengzhou). Building on this analysis, we offer insights and propose solutions to key issues in urban stormwater management. A bibliometric analysis of the Zhengzhou event indicates that urban flooding is primarily driven by climate change, with urbanization being a secondary factor. Additionally, the integration of machine learning with traditional numerical hydrological models, such as the widely used SWMM model, has emerged as a research trend to enhance the accuracy and comprehensiveness of parameter estimation. New urban stormwater management techniques and strategies are essential for improving urban flooding resilience, including a three-level collaborative governance system, urban green-gray-blue facilities, smart integrated management, and digital twin technologies. Furthermore, fully coupled water management models and strategies that incorporate all elements of hydrological cycles are essential for addressing complex urban hydrological challenges.

Natural HazardsVol. 121(4)
Sun Yat-sen University (CN), Wuhan University of Technology (CN), Chinese Academy of Sciences (CN), Wuhan University (CN), Pearl River Hydraulic Research Institute (CN), Institute of Geographic Sciences and Natural Resources Research (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Climate action
Openalex Percentile: Top 27%
Flood Risk Assessment and Management
0.85
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