RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2019, Vol. 28 >> Issue (08): 1898-1908.doi: 10.11870/cjlyzyyhj201908013

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

LI Jia-jia1,2, HE Xin-guang1,2, LU Xi-an1,2    

  1. (1. College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China;2. Key Laboratory of Geospatial Big Data Mining and Application, Hunan Province, Changsha 410081, China)
  • Online:2019-08-20 Published:2019-08-19

Abstract: 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.

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