基于Copula函数的锦河与连锦河洪水遭遇分析
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摘要
以实测洪峰流量估算Copula函数参数,建立联合概率分布模型计算连锦河和锦河洪水遭遇的概率。首先,建立4种最常见的Copula函数,并用Q-Q图及优度评价准则评价拟合效果,选择效果最佳的Frank函数进行洪水遭遇分析。结果表明,锦河与连锦河同时发生同一量级的洪水概率很小,锦河与连锦河同时发生20年一遇洪水的概率不大于0. 61%,锦河10年一遇的大洪水遭遇连锦河20年一遇的洪水的概率高达23. 01%。最后运用MIKE11模型计算该工况下锦河与连锦河的洪水水面线,其洪水遭遇概率的研究与水面线的计算成果对该市防洪工程具有重大意义。
The Copula function parameters were estimated by the measured flood peak flow,and the joint probability distribution model was established to calculate the flood encounters probability of the Lianjin River and Jin River. First,four kinds of the most common Copula functions were established,and the fitting effect was evaluated by Q-Q graph and goodness evaluation criterion,and the Frank function with the best effect was selected for flood encounter analysis. The results showed that the probability of flooding in the same magnitude of Jin River and Lianjin River is relatively small. The probability of a flood in the Jin River and Lianjin River at the same time is less than 0. 61%. The probability of a once every 10 years flood in the Jin River and once every 20 years flood in Lianjin River was as high as 23. 01%. Finally,the MIKE11 model was used to calculate the flood surface line of Jin River and Lianjin River under this condition. The study of the flood encounter probability and the calculation result of the water surface line are of great significance to the flood control project of the city.
The Copula function parameters were estimated by the measured flood peak flow,and the joint probability distribution model was established to calculate the flood encounters probability of the Lianjin River and Jin River. First,four kinds of the most common Copula functions were established,and the fitting effect was evaluated by Q-Q graph and goodness evaluation criterion,and the Frank function with the best effect was selected for flood encounter analysis. The results showed that the probability of flooding in the same magnitude of Jin River and Lianjin River is relatively small. The probability of a flood in the Jin River and Lianjin River at the same time is less than 0. 61%. The probability of a once every 10 years flood in the Jin River and once every 20 years flood in Lianjin River was as high as 23. 01%. Finally,the MIKE11 model was used to calculate the flood surface line of Jin River and Lianjin River under this condition. The study of the flood encounter probability and the calculation result of the water surface line are of great significance to the flood control project of the city.
引文
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[2]侯芸芸,宋松柏,赵丽娜,等.基于Copula函数的3变量洪水频率研究[J].西北农林科技大学学报(自然科学版),2010,38(2):219-228.
[3]陈璐,郭生练,张洪刚,等.长江上游干支流洪水遭遇分析[J].水科学进展,2011,22(3):323-330.
[4]李子远.非一致性洪水遭遇风险分析及其对河道防洪影响评价[J].天津:天津大学,2015.
[5]叶姗姗,叶兴成,王以超,等.基于Copula函数的设计暴雨雨型研究[J].水资源与水工程学报,2018,29(3):63-68.
[6]高玉琴,叶柳,赖丽娟.基于G-H copula函数的秦淮河流域洪水风险分析[J].水资源与水工程学报,2018,29(1):172-177.
[7]王亚雄,刘祖发,陈俊合.基于Copula函数的飞来峡水库坝址洪水峰量联合分布研究[J].广东水利水电,2014(5):18-22.
[8] SALVADORI G,DE MICHELE C. Frequency analysis via copulas:Theoretical aspects and applications to hydrological events[J]. Water Resources Research,2004,40(12):1-17.
[9]POULIN A,HUARD D,FAVRE A C,et al. Importance of tail dependence in bivariate frequency analysis[J]. Journal of Hydrologic Engineering,2007,12(4):394-403.
[10] HOCHRAINER-STIGLER S,PFLUG G,DIECKMANN U,et al. Integrating systemic risk and risk analysis using copulas[J]. International Journal of Disaster Risk Science,2018,9(4):561-567.
[11]李亦凡,李订芳.二维非对称copula函数在干旱特性联合概率中的应用[J].水资源与水工程学报,2016,27(5):236-240.
[12]成丹,陈正洪,方怡.宜昌市区短历时暴雨雨型特征[J].暴雨灾害,2015,34(3):249-253.
[13]袁超,宋松柏.基于Copula函数的水文干旱联合概率分布研究[J].水利与建筑工程学报,2016,14(6):50-53.
[14]郭生练,闫宝伟,肖义. Copula函数在多变量水文分析计算中的应用及研究进展[J].水文,2008,28(3):1-7.
[15]韩晓庆. Copula函数的理论及其应用[D].温州:温州大学,2013.
[16]闫宝伟,郭生练,郭靖,等.多变量水文分析计算方法的比较[J].武汉大学学报,2009,42(1):10-15.
[17]孙鹏,张强,陈晓红.基于Copula函数的鄱阳湖流域极值流量遭遇频率与灾害风险[J].湖泊科学,2011,23(2):183-190.
[18]王文圣,丁晶,邓育仁.非参数统计方法在水文水资源中的应用与展望[J].水科学进展,1999,10(4):458-463.
[19]孔鲁志.基于MIKE模型的山区性天然河道水力特性数值仿真应用研究[D].昆明:云南农业大学.
[20]徐海亮.西江流域洪水灾害和水文变异分析[J].人民珠江,2007(4):42-46.