As the longest river in Asia and the third-longest river in the world, the Yangtze River drains a large land area of the Eurasian continent. Seasonal prediction of the Yangtze River runoff is of crucial importance yet is a challenging issue. In this study, observed monthly runoff data are used to develop a new Yangtze River runoff index (YRI) for the 1950–2016 period. The YRI is not only able to quantify the runoff state of the Yangtze River but is also able to evaluate the intensity of the East Asian summer monsoon (EASM). The YRI is highly correlated with summer precipitation in the Yangtze River basin. It can also capture the principal components of the EASM circulation system.
To predict the Yangtze River summer runoff, we employed a partial least squares (PLS) regression method to seek sea surface temperature (SST) modes in the previous winter associated with the YRI time series. The findings indicate that the first SST mode exhibits a strong link with the decaying phase of El Niño (or La Niña), while the second SST mode is related to a persistant mega-La Niña (or mega-El Niño). These suggest that an El Niño–Southern Oscillation (ENSO) or mega-ENSO may be an essential source of predictability for the Yangtze River summer runoff. After a 47-year training period (1950–1996), a physical-empirical PLS model is built then a 3-month-lead forecast is used to validate the model from 1997 to 2016. The PLS model exhibits promising predictive skill that is better than some state-of-the-art reanalysis data systems. 相似文献