Spatial Dependence of Conditional Recurrence Periods for Extreme Rainfall in the Qiantang River Basin: Implications for Sustainable Regional Disaster Risk Governance

Climate change increases the intensity and frequency of extreme rainfall. Heavy rain is one of the main input sources for the complex water resources system in the watershed. Understanding its regional spatial correlation is of vital importance for promoting sustainable disaster management in the watershed. The Qiantang River Basin is a significant ecological and economic area in the Yangtze River Delta, yet systematic research on its multi-regional rainstorm-dependent structure remains insufficient. In this study, hourly rainfall data of the basin from 1950 to 2024 were used to construct marginal functions by using the peaks-over-threshold and the generalized Pareto distribution, and a mixed Copula model was established to describe the dependence structure of multi-regional extreme rainfall events. The model has been tested by RMSE and Cramér–von Mises statistics and shows reliable performance. The study reveals that the basin has a “double cluster” spatial pattern: the internal conditions of northern clusters (Hangzhou–Shaoxing) and southern clusters (Jinhua–Lishui–Quzhou) showed a strong dependence. On the contrary, under cluster conditions with low inter-regional dependence, all high-probability combinations occurred within the clusters, not outside them. This finding provides quantitative support for optimizing trans-regional emergency response, improving flood control resilience, and realizing precise allocation of resources, and is of great significance for promoting sustainable watershed governance.

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Publication Details

Journal
Sustainability
Published
2025-12-05
DOI
https://doi.org/10.3390/su172410896
Primary Topic
Hydrology and Drought Analysis
Type
article
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article

Spatial Dependence of Conditional Recurrence Periods for Extreme Rainfall in the Qiantang River Basin: Implications for Sustainable Regional Disaster Risk Governance

Jinglin Qian, Pujia Yu, Qiting Zhang, Xiaojun Jiang et al.
Sustainability
Hydrology and Drought Analysis
article

Spatial Dependence of Conditional Recurrence Periods for Extreme Rainfall in the Qiantang River Basin: Implications for Sustainable Regional Disaster Risk Governance

Jinglin Qian, Pujia Yu, Qiting Zhang, Xiaojun Jiang, Wu Yunxin
article en

Abstract

Climate change increases the intensity and frequency of extreme rainfall. Heavy rain is one of the main input sources for the complex water resources system in the watershed. Understanding its regional spatial correlation is of vital importance for promoting sustainable disaster management in the watershed. The Qiantang River Basin is a significant ecological and economic area in the Yangtze River Delta, yet systematic research on its multi-regional rainstorm-dependent structure remains insufficient. In this study, hourly rainfall data of the basin from 1950 to 2024 were used to construct marginal functions by using the peaks-over-threshold and the generalized Pareto distribution, and a mixed Copula model was established to describe the dependence structure of multi-regional extreme rainfall events. The model has been tested by RMSE and Cramér–von Mises statistics and shows reliable performance. The study reveals that the basin has a “double cluster” spatial pattern: the internal conditions of northern clusters (Hangzhou–Shaoxing) and southern clusters (Jinhua–Lishui–Quzhou) showed a strong dependence. On the contrary, under cluster conditions with low inter-regional dependence, all high-probability combinations occurred within the clusters, not outside them. This finding provides quantitative support for optimizing trans-regional emergency response, improving flood control resilience, and realizing precise allocation of resources, and is of great significance for promoting sustainable watershed governance.

SustainabilityVol. 17(24)
Zhejiang Ocean University (CN), Zhejiang University of Water Resource and Electric Power (CN)
Openalex Percentile: Top 64%
Hydrology and Drought Analysis
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