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

机译:中国三峡水库库区湘西河多元生态水文风险的不确定性量化

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摘要

This study develops a multivariate eco-hydrological risk-assessment framework based on the multivariate 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 distributions and dependence structure are quantified by a Markov chain Monte Carlo (MCMC) algotithm.Uncertainties in the joint return periods are evaluated based on the posterior distributions.The probabilistic 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.
机译:为了评估中国三峡库区(TGR)湘西河的极端生态水文事件的发生,本研究基于多元copula方法建立了多元生态水文风险评估框架。通过马尔可夫链蒙特卡洛(MCMC)算法对分布和依赖结构进行量化,并基于后验分布评估联合回归期的不确定性,并对二元和多元水文风险的概率特征进行了表征,结果表明所获得的结果预测间隔特别适合于洪水持续时间。“ AND”案例中联合回归期的不确定性随单变量洪水变量的回归期的增加而增加。此外,设计流量低且使用寿命长导致高度不确定的双变量水文风险。

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  • 来源
    《工程(英文)》 |2018年第005期|617-626|共10页
  • 作者单位

    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;

    state Key Laboratory of Water Environment, School of Environment Beijing Normal University, Beijing 100875, China;

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  • 正文语种 eng
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