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Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation

Author

Listed:
  • Yuannan Long

    (Changsha University of Science & Technology
    Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province
    Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province)

  • Hui Wang

    (Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province)

  • Changbo Jiang

    (Changsha University of Science & Technology
    Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province
    Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province)

  • Shang Ling

    (Hunan Hydro & Power Design Institute)

Abstract

Reservoir inflow forecasts are important for guiding reservoir operation. This study proposes an integrated framework of incorporating different forms of seasonal inflow forecasts in identifying the optimal releases policy. Gridded precipitation forecasts from climate models have been widely used for forecasting inflow. Both precipitation forecasts and soil moisture estimates are used as predictors to provide one-season-ahead reservoir inflow forecasts by constructing a regression problem. Principal component analysis is used to reduce the dimension of the regression problem, and a Bayesian regression technique is employed to generate various forms of inflow forecasts such as deterministic, probabilistic and ensemble forecasts. Two optimization models are constructed to couple with different forms of inflow forecasts. The first model aims to maximize hydropower generation and the second one aims to minimize end-of-season reservoir storage deviation from the target storage. Both single-value inflow and ensemble forecasts are incorporated to find the optimal water releasing policy considering inflow uncertainty and end-of-season reservoir storage requirement. The proposed methodology is demonstrated for Huangcai Reservoir in southern China. Bayesian regression technique shows good performance of seasonal inflow forecasts with a Pearson correlation of 0.8 and rank probability score of 0.4, which outperforms climatology. The coupling of ensemble inflow forecasts and optimization models provides water managers a set of release policies considering inflow uncertainty.

Suggested Citation

  • Yuannan Long & Hui Wang & Changbo Jiang & Shang Ling, 2019. "Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3743-3757, September.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:11:d:10.1007_s11269-019-02330-8
    DOI: 10.1007/s11269-019-02330-8
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    References listed on IDEAS

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    1. Richard Arsenault & Marco Latraverse & Thierry Duchesne, 2016. "An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4363-4380, September.
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