Seasonal prediction of the Yangtze River runoff using a partial-least square regression model
【摘要】：As the longest river in Asia and the third-longest in the world, the Yangtze River drains large land area of the Eurasian continent. Seasonal prediction of the Yangtze River runoff is of crucial importance yet a challenging issue. In this study, observed monthly runoff data are applied to develop a new Yangtze River runoff index(YRI) for the period 1950-2016. The YRI can not only quantify the runoff state of the Yangtze River, but also evaluate the intensity of the East Asian summer monsoon(EASM). The YRI is highly correlated with summer precipitation in the Yangtze River basin and Northeast China. Meanwhile, it can also capture the principal components of the EASM circulation systems. To predict the Yangtze River summer runoff, we employed a partial-least square(PLS) regression method to seek for sea surface temperature(SST) modes in prior winter associated with the YRI time series. The findings indicate that the first SST mode exhibits intimate linkage with a decaying phase of El Nino(or La Nina), while the second SST mode is related to a persisting La Nina(or El Nino) and Pacific Decadal Oscillation(PDO). These suggest that El Nino/Southern Oscillation(ENSO) or PDO may be an essential predictability source for the Yangtze River summer runoff. After a 47-year training(1950-1996), a physical-empirical PLS model is built, and then 3-month-lead forecast is performed to validate the model from 1997 to 2016. The PLS model exhibits a promising prediction skill which is better than some state-of-the-art reanalysis data systems.