International Journal of Disaster Risk Science >

2026 , Vol. 17 >Issue 01: 128 - 147

DOI: https://doi.org/10.1007/s13753-025-00683-w

ARTICLES

Assessing and Optimizing Urban Dynamic Resilience to Extreme Rainfall from Shock to Recovery

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  • 1School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China;
    2Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
    3Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576, Singapore;
    4College of Management, Sichuan Agricultural University, Chengdu 611130, China;
    5School of Economics and Management, Sichuan Normal University, Chengdu 610101, China;
    6Department of Geography, National University of Singapore, Singapore 119077, Singapore
*Ming Wang, wangming@bnu.edu.cn

Accepted date: 2025-11-13

  Online published: 2026-03-16

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Abstract

Climate change has intensified extreme rainfall events, challenging progress toward SDG 11's urban resilience targets. Current assessment methods often neglect dynamic recovery processes and regional precipitation disparities. We propose a three-phase framework combining interpretable machine learning (ML) and factorial experiments, using the Prep_shock index that integrates standardized rainfall intensity, capital exposure, and historical probability, to evaluate the dynamic resilience of 220+ Chinese cities from 2019 to 2022. Key findings reveal that: (1) The Prep_shock index effectively eliminates north-south precipitation biases, identifying Shandong coastal cities and Yangtze River Delta city clusters (36.2%) as high-resilience areas, in contrast to Henan Province. COVID-19 exacerbated systemic risks in megacities, undermining their capital protection capacities. (2) Spatial diagnostics classify 75.6% of the cities into Quadrant III (the balanced resilience category), with recovery times decreasing from the west to the east. Super-large cities like Zhengzhou (2021) exhibited critical recovery deficiencies (Quadrant IV). (3) Interpretable ML models (XGBoost/EBM) identify redundancy as the dominant resilience driver—robustness governs baseline resilience, while recovery relies on emergency support (for example, hospital beds density and fiscal inputs) and redundant infrastructure (for example, road network density). (4) Factorial experiments reveal optimization trade-offs: simultaneous enhancement of rapidity and redundancy diminishes their individual benefits, necessitating context-specific prioritization. The study advances dynamic resilience assessment methods and proposes quadrant-specific strategies for tailored urban adaptation.

Key words： Dynamic assessment; Extreme rainfall; Factorial analysis; Machine learning; Resilience optimization; Urban resilience

Cite this article

Linmei Zhuang, Ming Wang, Kai Liu, Loon Ching Tang, Jidong Wu, Dingde Xu, Junfei Liu, Jiawang Zhang, Jiarui Yang, Yi Ren, Dong Xu . Assessing and Optimizing Urban Dynamic Resilience to Extreme Rainfall from Shock to Recovery[J]. International Journal of Disaster Risk Science, 2026 , 17(01) : 128 -147 . DOI: 10.1007/s13753-025-00683-w

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