Variabilities of the spring river runoff system in East China and their relations to precipitation and sea surface temperature

Abstract

AbstractThe annual cycle and variabilities of the spring river runoff in East China (EC) at the continental scale and their relationship with precipitation, sea surface temperature (SST), and El Niño‐Southern Oscillation(ENSO) are investigated. Monthly mean data from 72 runoff stations and 160 precipitation stations in EC, covering a period between 1951 and 1983, are used for this study. The seasonal evolution of runoff depth is generally consistent with that of the rainfall but has more regional characteristics. The dominant spatial patterns and temporal variations of spring runoff and precipitation are studied with the empirical orthogonal function (EOF) analysis. The leading EOFs of spring runoff and precipitation show the intensity of runoff and precipitation in South China and are highly correlated with each other, indicating a direct response of runoff to precipitation, which is different from the summer situation. Statistic analysis suggests that the evolution of ENSO event may exert a strong influence on these two modes. The second EOFs of runoff and precipitation present a north‐south oscillation in EC with Nanling Mountain as the boundary and are also well correlated. These modes are highly related to the distribution of topography and may not be influenced by the ENSO. The third EOFs exhibit east–west oscillations in EC in both runoff and precipitation. The interdecadal relationship between spring precipitation/runoff and SST is further studied by singular value decomposition (SVD) analysis. The results indicate that the first coupled mode is dominant with the associated SST anomalies as an ENSO‐like interdecadal SST signal and indicates pronounced warming (SST) and wetting (runoff) trends in North China after the mid‐1970s, consistent with the observed trends of SST and runoff. The second coupled mode links the subtropical SST to the main spring runoff EOF mode in South China. Copyright © 2008 Royal Meteorological Society