年最大日降水量序列非一致性分析——以长江中下游干流8站为例  

Nonstationary Analysis of Annual Maximum Daily Precipitation Series:Taking 8 Stations in the Middle-Lower Reaches of Main Stream of the Yangtze River as the Example

作　　者：邓清文 倪玲玲 王栋 DENG Qingwen;NI Lingling;WANG Dong(School of Earth Sciences and Engineer of Nanjing University,Nanjing 210023,China;Nanjing Hydraulic Research Institute,Nanjing 210029,China)

机构地区：[1]南京大学地球科学与工程学院水科学系,江苏南京210033 [2]南京水利科学研究院,江苏南京210029

出　　处：《水文》2023年第2期58-65,共8页Journal of China Hydrology

基　　金：国家自然科学基金面上项目(52079036)。

摘　　要：全球气候变化环境下,极端降水序列一致性受到挑战,因此对其非一致性进行研究分析具有重要意义。本研究综合多种分析方法,对长江中下游干流8个站点1960—2020年间年最大日降水量序列的非一致性进行分析。采取滑动平均法、Mann-Kendall趋势检验法和Pettitt突变点检验法对所选站点年最大日降水量序列进行趋势和突变分析,应用GAMLSS模型对所选站点的年最大日降水量序列分别以时间t和多气象因子作为协变量进行非一致性分析,挑选出最优的拟合函数,探究年最大日降水序列的规律。结果显示,宝山站、南通站未来年最大日降水量可能增大,荆州站未来年最大日降水量可能会减少并趋于平缓;相较于以时间t为单一协变量,以气象因子作为多重协变量的分布函数拟合结果,对年最大日降水序列的非一致性具有更好的描述效果。Under the global climate change,the consistency of extreme precipitation series is challenged,so it is of great signifi⁃cance to study and analyze its inconsistency.This study integrates a variety of analysis methods to analyze the nonstationary of the annual maximum daily precipitation series of 8 stations in the middle and lower reaches of the Yangtze River from 1960 to 2020.The moving average,Mann-Kendall trend test and Pettitt mutation point test are used to analyze the trend and mutation of the annual maximum daily precipitation series of the selected stations.The GAMLSS model is used to analyze the nonstation⁃ary of the selected annual maximum daily precipitation series with time t and meteorological factors(Nino1+2,Nino3,Nino4,Nino3.4,PDO,SOI)as covariates when selecting the best fitting function to explore the law of the annual maximum precipitation series.The results show that the maximum daily precipitation of Baoshan Station and Nantong station may increase in the future while the maximum daily precipitation of Jingzhou station may decrease and tend to be flat in the future.Comparing with the dis⁃tribution function fitting results with time t as a single covariate and meteorological factors as multiple covariates,the latter has a better description effect on the nonstationary of annual maximum daily precipitation series.

关 键 词：非一致性 极端降水 GAMLSS模型 

分 类 号：P333[天文地球—水文科学]