Improvement of watershed flood forecasting by typhoon rainfall climate model with an ANN-based southwest monsoon rainfall enhancement

JOURNAL OF HYDROLOGY

卷:506

Article

Pan, Tsung-Yi¹  Yang, Yi-Ting²  Kuo, Hung-Chi²  Tan, Yih-Chi^1,3  Lai, Jihn-Sung^1,4  Chang, Tsang-Jung^1,3  Lee, Cheng-Shang^2,5  Hsu, Kathryn Hua¹ 

[1] Natl Taiwan Univ, Ctr Weather Climate & Disaster Res, Taipei 10764, Taiwan

[2] Natl Taiwan Univ, Dept Atmospher Sci, Taipei 10764, Taiwan

[3] Natl Taiwan Univ, Dept Bioenvironm Syst Engn, Taipei 10764, Taiwan

[4] Natl Taiwan Univ, Hydrotech Res Inst, Taipei 10764, Taiwan

[5] Taiwan Typhoon & Flood Res Inst, Taipei, Taiwan

关键词: Southwest monsoon;    Statistical typhoon rainfall forecasting;    Artificial neural network;    Water vapor flux;    Flood forecasting;   

DOI  :  10.1016/j.jhydrol.2013.08.018

来源: Elsevier

【 摘 要 】

This paper improves the typhoon flood forecasting over a watershed in a mountainous island of Taiwan. In the presence of the stiff topography in Taiwan, the typhoon rainfall is often phased-locked with terrain and the typhoon rainfall in general is best predicted by the typhoon rainfall climate model (TRCM) (Lee et al., 2006). However, the TRCM often underestimates the rainfall amount in cases of slowing moving storms with strong southwest monsoon supply of water vapor flux. We apply an artificial neural network (ANN) based southwest monsoon rainfall enhancement (AME) to improve TRCM rainfall forecasting for the Tsengwen Reservoir watershed in the southwestern Taiwan where maximum typhoon rainfall frequently occurred. Six typhoon cases with significant southwest monsoon water vapor flux are used for the test cases. The precipitations of seven rain gauge stations in the watershed and the southwest monsoon water vapor flux are analyzed to get the spatial distribution of the effective water vapor flux threshold, and the threshold is further used to build the AME model. The results indicate that the flux threshold is related to the topographic lifting of the moist air, with lower threshold in the upstream high altitude stations in the watershed. The lower flux threshold allows a larger rainfall amount with AME. We also incorporated the rainfall prediction with a state space neural network (SSNN) to simulate rainfall-runoff processes. Our improved method is robust and produces better flood predictions of total rainfall and multiple rainfall peaks. The runoff processes in the watershed are improved in terms of coefficient of efficiency, peak discharge, and total volume. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

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