Seasonal Prediction Of The Yangtze River Runoff Using A Partial Least Squares Regression Model

The Yangtze River Basin is a highly developed region,where prone to floods and droughts.Seasonal prediction of the Yangtze River runoff is of crucial importance yet a challenging issue.In this study,hydrological records were collected to examine the temporal and spatial distribution of runoff in this drainage basin.An apparent difference of runoff variations between the upper and mid-lower Yangtze reaches was detected in response to El Ni?o-Southern Oscillation(ENSO).The upper basin usually experiences floods or droughts during the summer of ENSO developing years,while the mid-lower runoff variations tend to coincide with ENSO decaying phases.Composite analysis is employed to investigate the underlying mechanism and results show that the Western Pacific Subtropical High(WPSH)exhibits large variability on its western side in summer with different ENSO phases.During the La Ni?a developing summers,the WPSH is significantly enhanced with its westward extension over the upper basin.Anomalous water vapor converges in its northwest edge thus favoring upper-basin flooding.In addition,when the El Ni?o decaying phases occur,the position of the WPSH?s ridge line shifts to the mid-lower reaches and the runoff is enhanced here.Other situations tend to be opposite.To predict the Yangtze River summer runoff,we employed a partial-least square(PLS)regression method to seek for sea surface temperature modes in prior winter associated with the YRI.The findings indicate that the first two modes may be an essential predictability source.The first SST mode exhibits intimate linkage with a decaying phase of ENSO,while the second SST mode is related to a persisting mega-ENSO.After a 47-year training(1950-1996),a physical-empirical(PE)PLS model is built,and then 3-month-lead forecast is performed to validate the model from 1997 to 2016.The PE PLS model exhibits a promising prediction skill,which provides a predictive reference for the Yangtze River runoff variations.