水力发电学报2025，Vol.44Issue(6)：72-88,17.DOI:10.11660/slfdxb.20250608
非一致性极值降雨空间聚类和频率及对气候因子响应研究
Nonstationary extreme rainfall spatial clustering and frequency responses to climate drivers
摘要
Abstract
Under global climate change,spatial clustering variations in extreme rainfall time series and their responses to climate drivers are critical to storm risk assessment for a river basin.In this study,we first take the Xiang River basin as the study area,and divide its extreme rainfall series into three clustering regions by applying the partitioning around medoids(PAM)algorithm that is based on the special variogram F-madogram.Then,for each clustering region,the climate drivers are identified by testing their significant correlation with extreme rainfall series from most rainfall stations.Finally,we take the extreme rainfall events of clustering center stations as a representative of the clustering region,and construct a non-stationary extreme rainfall frequency model based on the Bayesian inference.The modeling results reveal the extreme clustering algorithm gives better predictions of the extreme value series than the K-means clustering algorithm.The time-varying models using climate drivers as covariates have the best modeling performance and lowest uncertainties.We demonstrate that the probability of extreme rainfall events in this basin will be increased effectively,especially its rainfall intensity,if three conditions occurred in previous year-the values of North Atlantic Oscillation Index were negative,the sea level pressure in western Pacific Ocean rose,and the sea surface temperature in eastern Pacific Ocean rose.The results help evaluate and forecast the risks of extreme rainfall events in the Xiang River basin.关键词
极值空间聚类/变差函数/围绕中心点划分聚类算法/气候驱动/非一致性极值降雨/湘江流域Key words
extreme spatial clustering/variogram/partitioning around medoids algorithm/climate driver/nonstationary extreme rainfall/Xiang River basin分类
地球科学引用本文复制引用
曾杭,周洋,李建柱,杨琦,黄佳期..非一致性极值降雨空间聚类和频率及对气候因子响应研究[J].水力发电学报,2025,44(6):72-88,17.基金项目
国家自然科学基金项目(52279022) （52279022）
湖南省教育厅科学研究项目(24B0329) （24B0329）
湖南省研究生科研创新项目资助(LXBZZ2024210) （LXBZZ2024210）
