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Long-Term Stochastic Modeling of Monthly Streamflow in River Nile

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  • Shokry Abdelaziz

    (Department of Civil Engineering, Faculty of Engineering—Rabigh Branch, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Civil Engineering, Faculty of Engineering, El Mataria, Helwan University, Cairo 11718, Egypt)

  • Ahmed Mohamed Mahmoud Ahmed

    (Construction and Building Engineering Department, Faculty of Engineering and Technology, Arab Academy for Science Technology and Maritime Transport, Cairo 11757, Egypt)

  • Abdelhamid Mohamed Eltahan

    (Construction and Building Engineering Department, Faculty of Engineering and Technology, Arab Academy for Science Technology and Maritime Transport, Cairo 11757, Egypt)

  • Ahmed Medhat Ismail Abd Elhamid

    (National Water Research Center (NWRC), Hydraulics Research Institute (HRI), El-Qanater El-Khiriaya 13621, Egypt)

Abstract

Synthetic time series created from historical streamflow data are thought of as substitute events with a similar likelihood of recurrence to the real event. This technique has the potential to greatly reduce the uncertainty surrounding measured streamflow. The goal of this study is to create a synthetic streamflow model using a combination of Markov chain and Fourier transform techniques based on long-term historical data for the Nile River. First, the Markov chain’s auto-regression is applied, in which the data’s trend and seasonality are discovered and eliminated before applying the Pearson III distribution function. The Pearson III distribution function is substituted by a discrete Fourier transform (DFT) technique in the second approach. The applicability of the two techniques to simulate the streamflow between 1900 and 1999 is evaluated. The ability of the generated series to maintain the four most important statistical properties of the samples of monthly flows, i.e., the mean, standard deviation, autocorrelation lag coefficient, and cumulative distribution, was used to assess the quality of the series. The results reveal that the two techniques, with small differences in accuracy, reflect the monthly variation in streamflow well in terms of the three mentioned parameters. According to the coefficient of determination (R2) and normalized root mean square error ( NRMSE ) statistics, the discrete Fourier transform (DFT) approach is somewhat superior for simulating the monthly predicted discharge.

Suggested Citation

  • Shokry Abdelaziz & Ahmed Mohamed Mahmoud Ahmed & Abdelhamid Mohamed Eltahan & Ahmed Medhat Ismail Abd Elhamid, 2023. "Long-Term Stochastic Modeling of Monthly Streamflow in River Nile," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2170-:d:1045470
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    References listed on IDEAS

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    1. Guilherme Armando Almeida Pereira & Álvaro Veiga, 2019. "Periodic Copula Autoregressive Model Designed to Multivariate Streamflow Time Series Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3417-3431, August.
    2. Eid, Helmy M. & El-Marsafawy, Samia M. & Ouda, Samiha A., 2007. "Assessing the economic impacts of climate change on agriculture in Egypt : a ricardian approach," Policy Research Working Paper Series 4293, The World Bank.
    3. Kai Lun Chong & Sai Hin Lai & Ahmed El-Shafie, 2019. "Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2015-2032, April.
    4. Mihretab G. Tedla & Mohamed Rasmy & Katsunori Tamakawa & Hemakanth Selvarajah & Toshio Koike, 2022. "Assessment of Climate Change Impacts for Balancing Transboundary Water Resources Development in the Blue Nile Basin," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
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