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A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook

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Abstract

This study proposed a stochastic approach to forecast water-shortage probabilities for the coming three months in central Taiwan. Monte Carlo method is used to repeat random sampling from the seasonal weather outlook. For each Monte Carlo trial, the monthly rainfalls and monthly mean temperatures for one to three months ahead in eleven upstream catchments of central Taiwan can be obtained. Further, the disaggregation model is used to convert the monthly values into daily rainfall and temperature series. The HBV-based hydrological model uses the daily series to simulate daily inflows for each catchment as the input of system dynamic model for simulating the water budget of water resources system. After all the Monte Carlo trails, the monthly water-shortage probabilities for one to three months ahead can be calculated. The results reveal that the proposed approach can reasonably forecast the water-shortage conditions for one to three months ahead, which are beneficial for regional drought warning and decision support of drought-disaster prevention.

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Acknowledgments

The authors would like to thank the Water Resources Agency, Ministry of Economic Affairs, Taiwan for financially supporting the project entitled “Assessment and adaptation strategy of climate change impacts on flood and drought disasters in central Taiwan” (Project No.: MOEAWRA1010295; MOEAWRA1020314).

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Authors and Affiliations

  1. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, No. 1, University Rd., Tainan, 701, Taiwan

    Pao-Shan Yu, Tao-Chang Yang, Chen-Min Kuo & Yi-Tai Wang

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  1. Pao-Shan Yu
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  2. Tao-Chang Yang
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  3. Chen-Min Kuo
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  4. Yi-Tai Wang
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Correspondence to Pao-Shan Yu.

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Yu, PS., Yang, TC., Kuo, CM. et al. A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook. Water Resour Manage 28, 3905–3920 (2014). https://doi.org/10.1007/s11269-014-0717-9

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  • DOI: https://doi.org/10.1007/s11269-014-0717-9

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