Study on Risk Attribution of Extreme Temperature Events in the Yarlung Zangbo River Basin Based on Nonstationary GEV Models
Under the background of climate change and human activities,the non-stationary characteristics caused by the trend of hydrometeorological extreme value series in the the Yarlung Zangbo River Basin has imposed impact on the time-varying evolution characteristics of risk of extremes.This study constructed a dynamic generalized extreme value distribution(GEV)model by taking the physical driving factors related to human activities and climate change as the covariates of model parameters.Taking the extreme daily high temperature and low temperature series of the Yarlung Zangbo River Basin as the research object,this study quantitatively evaluated the impact of human activities and climate change factors represented by urbanization on the risk of extreme high temperature and low temperature events in the basin.Based on a phase-wise model optimization strategy,the optimization process of the optimal non-stationary GEV model was greatly simplified.This study proposed a slope comparison method based on linear regression to achieve attribution and stripping of the response relationship between human activities,climate change,and extreme temperature event risk.The analysis results show that the extreme high temperature and extreme low temperature sequences in Yarlung Zangbo River show a significant upward trend of 5％.The human activities represented by urbanization have brought a certain degree of mitigation to the risks posed by extreme high temperature events in the watershed.Climate change related factors,especially global warming and local warming effects,are the main driving factors for the continuous increase of extreme high temperature risks.
generalized extreme value distributionnonstationarityfrequency analysisextreme value eventYarlung Zangbo River
万方数据
维普
