Uncertainty Quantification for Multivariate Eco-Hydrological Risk in the Xiangxi River within the Three Gorges Reservoir Area in China. (October 2018)

• Record Type: Journal Article
• Authors: Fan, Yurui; Huang, Guohe; Zhang, Yin; Li, Yongping
• Is Part Of: Engineering. Volume 4:Number 5(2018)
• Page Start: 617
• Page End: 626
• Publication Date: 2018-10
• Subjects: Flood risk -- Copula -- Multivariate flood frequency analysis -- Distribution -- Markov chain Monte Carlo; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2018.06.006
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: 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) algorithm. 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.