Encounter risk prediction of rich-poor precipitation using a combined copula
Encounter risk prediction of rich-poor precipitation using a combined copula
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使用组合 copula 进行富贫降水风险预测
DOI:
10.1007/s00704-022-04092-7
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发表时间:
2022-05
影响因子:
3.4
通讯作者:
Hongrui Wang
中科院分区:
文献类型:
--
作者:
Longxia Qian;Xiaojun Wang;Mei Hong;SuZhen Dang;Hongrui Wang
Encounter risk precipitation of rich-poor precipitation is beneficial for the utilization of flood resources and rational allocation of water resources which often involves a challenging task—estimating the joint probability distribution function (PDF) of multiple hydrologic variables using copulas. This paper introduced a linear combination of three copulas (combined copula) to study probabilistic characteristics of precipitation in two watersheds. To validate the performance of the combined copula, four experiments were employed to identify the joint distribution for the summer monthly precipitation and annual precipitation at two pairs of neighboring stations in Jinghe River, China, which were then compared with three individual copulas, namely, Gumbel copula, Clayton copula, and Frank copula. All the experiments showed that the combined copula performed much better than any of the three individual copulas. The combined copula was further applied to predict the synchronous-asynchronous probabilities of the summer monthly precipitation and annual precipitation at those four stations in Jinghe River. The rich-normal-poor synchronous encounter probabilities of the summer monthly precipitation reach up to 0.7 and 0.63 for Guyuan-Pingliang stations and Huanxian-Xifeng stations, respectively. The rich-normal-poor synchronous encounter probabilities of the annual precipitation reach up to 0.6 and 0.59 for the Guyuan-Pingliang stations and the Huanxian-Xifeng stations, respectively. Moreover, the encounter probability of rich-poor precipitation between receiving areas of Haihe River and upper reaches of Han River was calculated by the combined copula, and the probability that is suitable to transfer water is about 0.35.
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影响因子:
4.3
作者:
Jinping Zhang;Yong Zhao;Weihua Xiao
通讯作者:
Weihua Xiao
影响因子:
6.4
作者:
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H. Vyver;J. V. D. Bergh
影响因子:
4.2
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C. Saad;S. El Adlouni;A. St‐Hilaire;P. Gachon
DOI:
10.1007/s11431-010-4158-2
发表时间:
2010-11
期刊:
Science China Technological Sciences
影响因子:
--
作者:
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通讯作者:
Siyi Hu;Zongzhi Wang;Yintang Wang;Haoyun Wu;Ju-liang Jin;Xiang Feng;Liang Cheng
DOI:
10.2139/ssrn.293423
发表时间:
2001-11
期刊:
Econometrics eJournal
影响因子:
--
作者:
Andrew J. Patton
通讯作者:
Andrew J. Patton