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Prediction of Chinese Loess Plateau summer rainfall using Pacific Ocean spring sea surface temperature
Author(s) -
Yasuda H.,
Berndtsson R.,
Saito T.,
Anyoji H.,
Zhang X.
Publication year - 2008
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.7172
Subject(s) - loess plateau , sea surface temperature , climatology , plateau (mathematics) , precipitation , environmental science , equator , spring (device) , latitude , loess , sediment , geology , oceanography , geography , meteorology , geomorphology , mechanical engineering , mathematical analysis , mathematics , geodesy , soil science , engineering
The Loess Plateau in China constitutes an 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 east Pacific Ocean also showed significant negative correlation with rainfall. Low and middle latitude areas (S10–20° and around 30° ) 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 correlations 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 monthly 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 © 2008 John Wiley & Sons, Ltd.