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An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization

Author

Listed:
  • Richard Arsenault

    (Université Laval
    Rio Tinto)

  • Marco Latraverse

    (Rio Tinto)

  • Thierry Duchesne

    (Université Laval)

Abstract

Ensemble streamflow prediction (ESP) has been widely used to gain insight on possible future inflows to hydropower reservoirs. However underestimation of climate, model structure and initial condition uncertainty often leads to under-dispersed ESP forecasts. In this paper, we present a novel approach called “Hindcast-mode Uncertainty Estimation” (HUE) to efficiently add variability in ESP forecasts to reduce their under-dispersion. The method was tested on a Canadian catchment used by Rio Tinto – Aluminium division to produce hydropower for their aluminium smelting plants. This project was focused on correcting long-term predictions of freshet runoff volumes to optimize drawdown volumes, with up to 6 months of lead time. It was found that by adding an error term to the hydrological model’s snow water equivalent (SWE) state variable at the time of forecast in hindcasting mode, the resulting simulation could be forced to perfectly reproduce the freshet runoff volume. This error term was computed for all years on record which enabled modeling of the error’s distribution. This distribution can then be sampled from to add noise to the model’s SWE at the start of a new ESP forecast. Results show that the current winter ESP forecasts are strongly under-dispersed for the freshet runoff volume estimation and that the proposed method is able to widen the ESPs to correct the under-dispersion problem. This was validated by using Talagrand diagrams which shifted from a U-shape (prior to HUE) to a uniform distribution (with HUE). The project objectives of correcting the ESP forecast’s under-dispersion in spring runoff estimations was thus attained with minimal effort, bypassing the need to perform more complex ensemble data assimilation techniques.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:waterr:v:30:y:2016:i:12:d:10.1007_s11269-016-1425-4
    DOI: 10.1007/s11269-016-1425-4
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    References listed on IDEAS

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    1. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    2. Zhao Liu & Yiping Guo & Lixia Wang & Qing Wang, 2015. "Streamflow Forecast Errors and Their Impacts on Forecast-based Reservoir Flood Control," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4557-4572, September.
    3. Richard Arsenault & François Brissette & Jean-Stéphane Malo & Marie Minville & Robert Leconte, 2013. "Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2075-2087, May.
    4. H. Zeinivand & F. Smedt, 2009. "Hydrological Modeling of Snow Accumulation and Melting on River Basin Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2271-2287, September.
    5. Jie Chen & François Brissette, 2015. "Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3329-3342, July.
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    Cited by:

    1. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
    2. Jiazheng Lu & Jun Guo & Li Yang & Xunjian Xu, 2017. "Research of reservoir watershed fine zoning and flood forecasting method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1291-1306, December.
    3. 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.

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