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水力发电学报 ›› 2025, Vol. 44 ›› Issue (6): 72-88.doi: 10.11660/slfdxb.20250608

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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

  

  • 出版日期:2025-06-25 发布日期:2025-06-25

Nonstationary extreme rainfall spatial clustering and frequency responses to climate drivers

  • Online:2025-06-25 Published:2025-06-25

摘要: 随着全球气候变化影响,极值降雨序列的空间聚类演变和气候响应特征对流域暴雨风险估计有非常重要的意义。本文以湘江流域为研究区域,采用基于变差函数F-madogram的围绕中心点划分(PAM)聚类算法将流域极值降雨序列分为3个聚类分区;筛选出与各聚类分区大部分站点相关性显著的气候驱动因子,以各聚类中心站点为代表,构建基于贝叶斯的非一致性极值降雨频率计算模型。结果表明:极值聚类算法相较K均值聚类法更适用于极值降雨序列;以气候因子为协变量的时变矩模型表现最优,且其不确定性区间最小;当前一年北大西洋涛动为负相、同年西太平洋海面气压升高和东太平洋海面温度上升时,湘江流域上中下游夏季极值降雨频率增加,为湘江流域极值暴雨风险估计和预测提供科学依据。

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

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.

Key words: extreme spatial clustering, variogram, partitioning around medoids algorithm, climate driver, nonstationary extreme rainfall, Xiang River basin

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