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Utilization of the Bayesian Method to Improve Hydrological Drought Prediction Accuracy

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  • Deg-Hyo Bae

    (Sejong University)

  • Kyung-Hwan Son

    (Sejong University
    Yeongsan River Flood Control Office, Ministry of Land, Infrastructure and Transport)

  • Jae-Min So

    (Sejong University)

Abstract

This study established a hydrological drought forecasting system based on the Bayesian method and evaluated its utilization for South Korea. The regression result between Historical Runoff (HR) and Ensemble Streamflow Prediction Runoff (ESP_R) was used as prior information in the Bayesian method. Additionally Global seasonal forecast System 5 Runoff (GS5_R) produced using a dynamic prediction method was used in a likelihood function. Bayesian Runoff (BAY_R), as posterior information, was generated and compared with the ESP_R and GS5_R results for predictive ability evaluation. The Standardized Runoff Index (SRI) was selected for the drought prediction, and the BAY_SRI, GS5_SRI and ESP_SRI were computed using BAY_R, GS5_R and ESP_R, respectively. The Correlation Coefficient (CC), Nash-Sutcliffe Efficiency (NSE) and Receiver Operating Characteristic (ROC) score of BAY_SRI were the highest, and the Root Mean Square Error (RMSE) of BAY_SRI was the lowest among the methods. The Bayesian method improved the behavioral and quantitative error of drought prediction and the predictive ability of the occurrence of drought. In particular, the simulation accuracy was significantly improved during the flood season. Additionally, BAY_SRI represented past drought scenarios better than did the other two methods. Overall, we found that the Bayesian method could be applied for hydrological drought predictions for based on 1- and 2-month lead times.

Suggested Citation

  • Deg-Hyo Bae & Kyung-Hwan Son & Jae-Min So, 2017. "Utilization of the Bayesian Method to Improve Hydrological Drought Prediction Accuracy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3527-3541, September.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:11:d:10.1007_s11269-017-1682-x
    DOI: 10.1007/s11269-017-1682-x
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    References listed on IDEAS

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    1. Andrew Gelman, 2003. "A Bayesian Formulation of Exploratory Data Analysis and Goodness‐of‐fit Testing," International Statistical Review, International Statistical Institute, vol. 71(2), pages 369-382, August.
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    Cited by:

    1. Mojtaba Sadegh & Morteza Shakeri Majd & Jairo Hernandez & Ali Torabi Haghighi, 2018. "The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1867-1881, March.
    2. Quang-Tuong Vo & Jae-Min So & Deg-Hyo Bae, 2020. "An Integrated Framework for Extreme Drought Assessments Using the Natural Drought Index, Copula and Gi* Statistic," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1353-1368, March.
    3. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.

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    Keywords

    Hydrological drought prediction; Bayesian method; ESP; GS5; SRI;
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