Quantifying multivariate flood risk under nonstationary condition

Rongrong Li (), Lihua Xiong (), Cong Jiang (), Wenbin Li () and Chengkai Liu ()
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Rongrong Li: Wuhan University
Lihua Xiong: Wuhan University
Cong Jiang: China University of Geosciences
Wenbin Li: Wuhan University
Chengkai Liu: Wuhan University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 1, No 49, 1187 pages

Abstract: Abstract Risk analysis of extreme hydrological events is a key issue in the decision-making process for river basin management. For a given multivariate flood event, the corresponding flood risk under stationary condition is usually estimated by a constant exceedance probability. However, under the nonstationary condition, the exceedance probability for a given multivariate flood event varies from year to year, therefore, the method of quantifying flood risk based on the stationary assumption is no longer applicable to the nonstationary cases. This paper aimed at quantifying multivariate flood risk under nonstationary condition by constructing multivariate flood distribution based on a dynamic C-vine copula. The five-dimensional multivariate flood series of the Upper Yangtze River is selected as a case study. Future climate change and reservoirs are considered as covariates to carry out the multivariate flood frequency analysis, and multivariate flood risk corresponding to different exceedance probabilities (AND, Kendall and OR cases) is further calculated. It is found that both marginal distributions and dependence structure of the multivariate flood series are nonstationary due to the impacts of climate change and reservoir regulation. For multivariate flood risk, the nonstationary case is significantly different from the stationary case, and the marginal nonstationarities play a dominant role in affecting the multivariate flood risk. In addition, risk associated with AND exceedance probability is more sensitive to the nonstationarity of the marginal distribution and dependence structure than risks associated with Kendall and OR exceedance probability. The multivariate flood risk analysis considering the joint characteristics of multiple flood attributes might be helpful to decision-makers for flood control and water resources management under a changing environment.

Keywords: Multivariate flood risk; Dynamic C-vine copula; Exceedance probability; Nonstationary condition (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05716-x

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