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Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change
Author(s) -
Xu Pengcheng,
Wang Dong,
Singh Vijay P.,
Lu Huayu,
Wang Yuankun,
Wu Jichun,
Wang Lachun,
Liu Jiufu,
Zhang Jianyun
Publication year - 2020
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd032780
Subject(s) - copula (linguistics) , multivariate statistics , quantile , flood myth , environmental science , climate change , marginal distribution , climatology , multivariate analysis , hazard , econometrics , statistics , mathematics , geography , geology , random variable , oceanography , chemistry , archaeology , organic chemistry
Abstract In recent years copulas have been widely employed in multivariate modeling of hydrological extremes. However, anthropogenic and climate changes have greatly impacted the probabilistic behavior of these extremes and have challenged the stationarity assumption of the marginal distributions of individual characteristics of the extremes as well as their dependence structure inherent in copula‐based modeling. This study developed a dynamic copula‐based multivariate risk analysis model (DCM) to analyze seasonal flood extremes ( MWL and AMS ), observed at Yichang Station in Yangtze River basin, China. The model entailed three parts: (1) Time‐varying moment models, combined with log‐likelihood ratio tests, were employed to explore whether the trend‐caused nonstationarity existed in the marginal distributions or the dependence structure of the seasonal flood extremes; (2) a nonstationary multivariate probability distribution was developed using a dynamic marginal and copula‐based model, incorporating different large‐scale climate forcings (NAO, SOI, and NINO3.4), meteorological factor (rainfall and temperature) and reservoir index as covariates; and (3) flood hazard was quantified using the multivariate hazard model. The climate‐related nonstationarity‐based multivariate frequency model through the incorporation of climatic indices would help predict the hazard level of a certain quantile pair for the next year of the observed period.