Uncertainty Quantification for Multivariate Eco-Hydrological Risk in the Xiangxi River within the Three Gorges Reservoir Area in China

作     者：Yurui Fan Guohe Huang Yin Zhang Yongping Li 

作者机构：College of Engineering Design and Physical Sciences Brunel University London Uxbridge Middlesex UB8 3PH UK State Key Laboratory of Water Environment School of Environment Beijing Normal University Beijing 100875 China College of Engineering and Mines University of Alaska fairbanks fairbanks AK 99775 USA 

基　　金：This work was jointly funded by the National Natural Science Foundation of China (51520105013 and 51679087) and the National Key Research and Development Plan of China (2016YFC0502800) 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2018年第4卷第5期

页      码：617-626页

摘      要：This study develops a multivariate eco-hydrological risk-assessment framework based on the multivari-ate copula method in order to evaluate the occurrence of extreme eco-hydrological events for the Xiangxi River within the Three Gorges Reservoir （TGR） area in China. Parameter uncertainties in marginal distri-butions and dependence structure are quantified by a Markov chain Monte Carlo （MCMC） algorithm. Uncertainties in the joint return periods are evaluated based on the posterior distributions. The proba- bilistic features of bivariate and multivariate hydrological risk are also characterized. The results show that the obtained predictive intervals bracketed the observations well, especially for flood duration. The uncertainty for the joint return period in ＂AND＂ case increases with an increase in the return period for univariate flood variables. Furthermore, a low design discharge and high service time may lead to high bivariate hydrological risk with great uncertainty.

主 题 词：Flood risk CopulaMultivariate flood frequency analysis Distribution Markov chain Monte Carlo 

学科分类：082802[082802] 081803[081803] 08[工学] 0828[工学-建筑类] 0818[工学-交通运输类] 

核心收录：

D　O　I：10.1016/j.eng.2018.06.006

馆 藏 号：203381212...