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Dynamic Regression Model for Hourly River Level Forecasting Under Risk Situations: an Application to the Ebro River

Author

Listed:
  • A. C. Cebrián

    () (University of Zaragoza)

  • J. Abaurrea

    (University of Zaragoza)

  • J. Asín

    (University of Zaragoza)

  • E. Segarra

    (University of Zaragoza)

Abstract

This work proposes a new statistical modelling approach to forecast the hourly river level at a gauging station, under potential flood risk situations and over a medium-term prediction horizon (around three days). For that aim we introduce a new model, the switching regression model with ARMA errors, which takes into account the serial correlation structure of the hourly level series, and the changing time delay between them. A whole modelling approach is developed, including a two-step estimation, which improves the medium-term prediction performance of the model, and uncertainty measures of the predictions. The proposed model not only provides predictions for longer periods than other statistical models, but also helps to understand the physics of the river, by characterizing the relationship between the river level in a gauging station and its influential factors. This approach is applied to forecast the Ebro River level at Zaragoza (Spain), using as input the series at Tudela. The approach has shown to be useful and the resulting model provides satisfactory hourly predictions, which can be fast and easily updated, together with their confidence intervals. The fitted model outperforms the predictions from other statistical and numerical models, specially in long prediction horizons.

Suggested Citation

  • A. C. Cebrián & J. Abaurrea & J. Asín & E. Segarra, 2019. "Dynamic Regression Model for Hourly River Level Forecasting Under Risk Situations: an Application to the Ebro River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 523-537, January.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:2:d:10.1007_s11269-018-2114-2
    DOI: 10.1007/s11269-018-2114-2
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    References listed on IDEAS

    as
    1. Ozgur Kisi, 2011. "Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 579-600, January.
    2. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    3. Shirisha Pulukuri & Venkata Reddy Keesara & Pratap Deva, 2018. "Flow Forecasting in a Watershed using Autoregressive Updating Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2701-2716, June.
    4. Wentao Xu & Cong Jiang & Lei Yan & Lingqi Li & Shuonan Liu, 2018. "An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1343-1366, March.
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