Copula-based bivariate flood frequency analysis in a changing climate—A case study in the Huai River Basin, China

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• 作者：Kai Duan ; Yadong Mei ; Liping Zhang
• 关键词：flood ; climate change ; copulas ; bivariate distribution
• 刊名：Journal of Earth Science
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：27
• 期：1
• 页码：37-46
• 全文大小：2,155 KB
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• 作者单位：Kai Duan (1) (2)
  Yadong Mei (1) (2)
  Liping Zhang (1) (2)
  
  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
  2. Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan University, Wuhan, 430072, China
  
• 刊物主题：Earth Sciences, general; Geotechnical Engineering & Applied Earth Sciences; Biogeosciences; Geochemistry; Geology;
• 出版者：Springer Berlin Heidelberg
• ISSN：1867-111X

文摘

Copula-based bivariate frequency analysis can be used to investigate the changes in flood characteristics in the Huai River Basin that could be caused by climate change. The univariate distributions of historical flood peak, maximum 3-day and 7-day volumes in 1961–2000 and future values in 2061–2100 projected from two GCMs (CSIRO-MK3.5 and CCCma-CGCM3.1) under A2, A1B and B1 emission scenarios are analyzed and compared. Then, bivariate distributions of peaks and volumes are constructed based on the copula method and possible changes in joint return periods are characterized. Results indicate that the Clayton copula is more appropriate for historical and CCCma-CGCM3.1 simulating flood variables, while that of Frank and Gumbel are better fitted to CSIRO-MK3.5 simulations. The variations of univariate and bivariate return periods reveal that flood characteristics may be more sensitive to different GCMs than different emission scenarios. Between the two GCMs, CSIRO-MK3.5 evidently predicts much more severe flood conditions in future, especially under B1 scenario, whereas CCCma-CGCM3.1 generally suggests contrary changing signals. This study corroborates that copulas can serve as a viable and flexible tool to connect univariate marginal distributions of flood variables and quantify the associated risks, which may provide useful information for risk-based flood control. Key Words flood climate change copulas bivariate distribution