Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition

摘要

Streamflow is the result of complex climatic and hydrological interactions that are driven by atmosphere-ocean circulation. Teleconnection analysis of streamflow is significant for identifying the atmosphere- and climate-related indices of hydrology series. To mine the physical information of streamflow series more effectively, ensemble empirical mode decomposition (EEMD) was employed to decompose streamflow series into inherent stationary components with different periodic oscillations and a trend. In this study, important climate indices were identified based on the correlation coefficients, which were calculated using cross-correlation. According to the selected indicators, the decomposed and streamflow series were regressed using an artificial neural network (ANN) and support vector regression (SVR). In total, 130 climate phenomenon indices, pre-runoff, and 1-12-month time lags were considered as teleconnection variables. The results show that EEMD can be used to extract the period and trend of streamflow, and that the decomposed hydrology components have much stronger correlations than the original runoff with the climate phenomenon indices. The original streamflow was the most closely correlated with the series from the previous month, which is an autocorrelation. However, more physical information was obtained through the teleconnection of the sub-streamflow series. The periodic oscillations was explained by relatively diverse atmospheric circulation and sea surface temperature indices with different time lags. The low-frequency periodic series were better represented than the high-frequency series by the climate phenomenon indices. In addition, Nino 3.4, Nino 4, the Warm-pool ENSO Index, and the ENSO Modoki Index were representative of the high-frequency component; the mid-frequency signals were sensitive to the Solar Flux Index, Total Sunspot Number Index, Pacific Decadal Oscillation Index, and Southern Oscillation Index; the low-frequency series were influenced by the Atlantic Multi-decadal Oscillation Index, North African Subtropical High Area Index, North Atlantic Triple Index, etc.; and the Indian Ocean Basin-Wide Index, Atlantic Multi-decadal Oscillation Index, Western Pacific Warm Pool Strength Index, and East Pacific 850 mb Trade Wind Index expressed well the long-term trend of monthly streamflow. Meanwhile, the regression results of the decomposed series obtained by the ANN and SVR exhibited better statistical performance than those of the original series, especially for the EEMD-SVR.