Identifying key drivers of urban flood resilience for effective management: Insights and implications

Abstract

Enhancing urban resilience is a powerful strategy for mitigating floods caused by both intensive human activities and climate change. However, existing studies have limitations, highlighting the need for a more comprehensive framework for assessing flood resilience based on the resilience evolution process. Therefore, the objective of this study was to develop an integrated framework for evaluating urban flood resilience, incorporating Bayesian networks and Geographic Information Systems (GIS) to explore the driving mechanisms behind flood resilience with the Beijing-Tianjin-Hebei urban agglomeration in China as a case study. The results indicated that: (1) inundation risk, population risk, and flooding mitigation were the most critical indicators influencing urban flood resilience; (2) Chengde and Tangshan emerged as key areas with high resistance capabilities, while Zhangjiakou and Baoding showed notable strengths in functional recovery; (3) the average value of urban flood resilience decreased from 0.58 under a 5-year rainfall return period to 0.54 under a 100-year rainfall return period, representing a 5.6 % decrease, with Zhangjiakou exhibiting the highest flood resilience. These findings are of significant importance for policymakers involved in flood risk management.

Keywords

Urban flood resilience / BN-GIS model / Resilience theory / Multi-indicator system / The Beijing-Tianjin-Hebei region

CRediT authorship contribution statement

Yongyang Wang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Yulei Xie: Investigation. Lei Chen: Visualization. Pan Zhang: Funding acquisition.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grants No. 52409016 and 52200212), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515110717). The authors would like to extend the appreciation to the editors and the anonymous reviewers for their efforts in helping improve the quality of this paper.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100278.

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