Prediction of Chinese Loess Plateau summer rainfall using Pacific Ocean spring sea surface temperature

H Yasuda, Ronny Berndtsson, T. Saito, H. Anyoji, X. Zhang

Research output: Contribution to journalArticlepeer-review

9 Citations (SciVal)

Abstract

The Loess Plateau in China constitutes in important source area for both water and sediments to the Yellow River. Thus, improved prediction techniques of rainfall may lead to better estimation of discharge and sediment content for the Yellow River. Consequently. the objective of this study was to establish better links between rainfall of the Loess Plateau in China and sea surface temperature (SST) in the Pacific Ocean. Results showed that there is a strong lagged correlation between and SST and rainfall. The SST for Micronesia and areas South of the Aleutian Islands showed significant correlations (s.f. < 0.001: 99.9%) with rainfall over the dryer region of the Loess Plateau for a lag of 4 to 6 months. The SST over the equator On the cast Pacific Ocean also showed significant negative correlation with rainfall. Low and middle latitude areas (S10-20 degrees and around 30 degrees) of the south-east Pacific Ocean displayed significant positive and negative correlation with rainfall on the semiarid Loess Plateau. The differenced SST values (positive SST minus negative SST) increased these correlation,, with rainfall. An artificial neural network (ANN) model was used to predict Summer rainfall from the differenced SST during, the spring period. The correlation between predicted and observed months), rainfall was in general larger than 0.7. This indicates that major annual rainfall (during summer season) can be predicted with good accuracy using the suggested approach. Copyright (D 2008 John Wiley & Sons. Ltd.
Original languageEnglish
Pages (from-to)719-729
JournalHydrological Processes
Volume23
Issue number5
DOIs
Publication statusPublished - 2009

Subject classification (UKÄ)

  • Water Engineering

Keywords

  • rainfall
  • summer
  • Loess Plateau rainfall in China
  • sea Surface temperature
  • Pacific Ocean
  • artificial neural network model

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