长江流域月降水的EEMD多时间尺度遥相关分析

基于长江流域138个气象站1961~2016年的逐月降水观测资料，应用集合经验模态分解（EEMD）方法，分别对各站点的月降水序列进行EEMD分解，然后，运用时滞相关分析和逐步变量选择的方法，以识别长江流域月降水周期振荡和长期趋势的显著影响因子，并构建多元线性回归模型对长江流域月降水进行预测。结果表明：（1）近50多年来，长江流域各站点的月降水呈现出显著的季节、年际和年代际尺度振荡特征。（2）流域内各站点月降水的长期变化趋势存在着较大的空间差异性，表现为金沙江、雅砻江、大渡河以及鄱阳湖流域是月降水长期趋势显著增加的集中区，而岷江中游以及洞庭湖流域的南部是月降水长期趋势显著减少的集中区。（3）厄尔尼诺1+2区的平均海表温度（NINO1+2）的过去模式是影响长江流域月降水周期振荡的主要气候因子，而全球平均气温距平（GlobalT）是影响长江流域月降水长期趋势的主要气候因子。（4）基于已识别的影响因子构建的月降水量预测模型在旱季的预报性能高于雨季，并在长江上游地区的预报性能高于其中下游地区。

Multi-time Scale Teleconnection Analysis of Monthly Precipitation in the Yangtze River Basin Based on the EEMD

In order to analyze the periodic characteristics and long-term trend of monthly precipitation in the Yangtze River Basin, the monthly precipitation time series at each of stations was decomposed by the ensemble empirical mode decomposition (EEMD) based on the monthly precipitation observations at 138 meteorological stations during 1961-2016 in the basin. Then, the lag-time correlation analysis and stepwise variable selection were employed to identify the significant climate factors impacting the periodic oscillations and long-term trend of monthly precipitation. Finally, using the identified large-scale climate factors as the forecasting variables of monthly precipitation, the multivariate linear regression model was established at each station for predicting monthly precipitation at that station. The results are as follows: (1) In the last 50 years, the monthly precipitation in the Yangtze River Basin exhibits remarkable seasonal, interannual and interdecadal oscillations. (2) There exists a large spatial difference in the long-term change trend of monthly precipitation at the different stations in the basin. The Jinsha River, the Yalong River, the Dadu River and the Poyang Lake Basin are covered mainly by a significantly increasing long-term trend of monthly precipitation, while a significantly decreasing long-term trend of monthly precipitation occurs mainly the middle reaches of the Minjiang River and the southern part of the Dongting Lake basin. (3) The average sea surface temperature over El Nio 1+2 area (NINO1+2) is a dominated climatic factor influencing the periodic oscillations of monthly precipitation, while the global mean temperature anomaly (GlobalT) is an important climatic factor impacting the long-term trend of monthly precipitation in the Yangtze River Basin. (4) The built monthly precipitation prediction model based on the identified climate factors has higher prediction performance in winter dry season than the summer rainy season, and in the upper reaches than the middle and lower reaches of the Yangtze River Basin.