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Multivariate analysis for medium- and long-range forecasting of Nile River flow to mitigate drought and flood risks

Author

Listed:
  • Hossam M. Ahmed

    (Cairo University)

  • Ayman G. Awadallah

    (Fayoum University)

  • Alaa El-Din M. El-Zawahry

    (Cairo University)

  • Khaled H. Hamed

    (Cairo University)

Abstract

The Nile River provides Egypt with most of its water resources. Medium- and long-rage forecasts of Nile flows at Aswan have been recognized as of significant importance to allow better management and operation of the water resource facilities and mitigate the risks of both droughts and floods. In this study, a wide range of climate indices and atmospheric fields were used as potential predictors for long-range forecasting of Nile streamflow for one flood season ahead (July–October). The approach followed in this study focuses on searching for potential predictors, reducing the pool of potential predictors by using multivariate statistical analysis, applying sequentially, Canonical Correlation Analysis, Principal Component Analysis, and multiple linear regression to robustly forecast the Nile flow. The proposed approach proved to be very useful for improving long-range Nile River flow forecasting. It revealed the adequacy of the models and enhanced the accuracy of the predictions of the full spectrum of droughts and floods, both in the calibration and validation phases, over the simple stepwise regression method using all climate indices and atmospheric fields as potential predictors.

Suggested Citation

  • Hossam M. Ahmed & Ayman G. Awadallah & Alaa El-Din M. El-Zawahry & Khaled H. Hamed, 2022. "Multivariate analysis for medium- and long-range forecasting of Nile River flow to mitigate drought and flood risks," 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. 110(1), pages 741-763, January.
    • Handle: RePEc:spr:nathaz:v:110:y:2022:i:1:d:10.1007_s11069-021-04968-3
    DOI: 10.1007/s11069-021-04968-3
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    References listed on IDEAS

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    1. Basak, Gopal K & Chan, Ngai Hang & Palma, Wilfredo, 2001. "The Approximation of Long-Memory Processes by an ARMA Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 367-389, September.
    2. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    3. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
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