Forecasting of monthly streamflow for the White Nile River at Malakal station is a crucial aspect for different water resources projects in both countries Sudan and South Sudan. For instance, the operation of Jabal al Awliya dam in central Sudan entirely depends upon the measured flow of this station. In this paper, linear stochastic models well-known as seasonal autoregressive integrated moving average [SARIMA] models were used to model and forecast monthly flow of White Nile River in Malakal station, South Sudan. For the analysis, monthly flow data for the years running from 1970 up to 2013 were used. A scrutiny of the original series proves a yearly seasonal pattern. The results of Phillips-Perron (PP) test and Augmented Dickey Fuller (ADF) test on the streamflow series show that this series is not stationary. This non-stationarity was removed using first order seasonal differencing (i.e. twelve-monthly) preceding to the development of the model. The SARIMA (1,0,1)×(0,1,1)12 model was selected as the most suitable for modeling and forecasting monthly flow for White Nile River. It was found that the model was proper to forecast three successive years of monthly flow, which may help the experts to institute priorities for various water resources management in both countries.
| Published in | American Journal of Water Science and Engineering (Volume 7, Issue 3) |
| DOI | 10.11648/j.ajwse.20210703.12 |
| Page(s) | 103-112 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
White Nile River, South Sudan, Malakal, Stochastic Models, SARIMA
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APA Style
Tariq Mahgoub Mohamed. (2021). Forecasting of Monthly Flow for the White Nile River (South Sudan). American Journal of Water Science and Engineering, 7(3), 103-112. https://doi.org/10.11648/j.ajwse.20210703.12
ACS Style
Tariq Mahgoub Mohamed. Forecasting of Monthly Flow for the White Nile River (South Sudan). Am. J. Water Sci. Eng. 2021, 7(3), 103-112. doi: 10.11648/j.ajwse.20210703.12
AMA Style
Tariq Mahgoub Mohamed. Forecasting of Monthly Flow for the White Nile River (South Sudan). Am J Water Sci Eng. 2021;7(3):103-112. doi: 10.11648/j.ajwse.20210703.12
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Download@article{10.11648/j.ajwse.20210703.12,
author = {Tariq Mahgoub Mohamed},
title = {Forecasting of Monthly Flow for the White Nile River (South Sudan)},
journal = {American Journal of Water Science and Engineering},
volume = {7},
number = {3},
pages = {103-112},
doi = {10.11648/j.ajwse.20210703.12},
url = {https://doi.org/10.11648/j.ajwse.20210703.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajwse.20210703.12},
abstract = {Forecasting of monthly streamflow for the White Nile River at Malakal station is a crucial aspect for different water resources projects in both countries Sudan and South Sudan. For instance, the operation of Jabal al Awliya dam in central Sudan entirely depends upon the measured flow of this station. In this paper, linear stochastic models well-known as seasonal autoregressive integrated moving average [SARIMA] models were used to model and forecast monthly flow of White Nile River in Malakal station, South Sudan. For the analysis, monthly flow data for the years running from 1970 up to 2013 were used. A scrutiny of the original series proves a yearly seasonal pattern. The results of Phillips-Perron (PP) test and Augmented Dickey Fuller (ADF) test on the streamflow series show that this series is not stationary. This non-stationarity was removed using first order seasonal differencing (i.e. twelve-monthly) preceding to the development of the model. The SARIMA (1,0,1)×(0,1,1)12 model was selected as the most suitable for modeling and forecasting monthly flow for White Nile River. It was found that the model was proper to forecast three successive years of monthly flow, which may help the experts to institute priorities for various water resources management in both countries.},
year = {2021}
}
TY - JOUR T1 - Forecasting of Monthly Flow for the White Nile River (South Sudan) AU - Tariq Mahgoub Mohamed Y1 - 2021/08/18 PY - 2021 N1 - https://doi.org/10.11648/j.ajwse.20210703.12 DO - 10.11648/j.ajwse.20210703.12 T2 - American Journal of Water Science and Engineering JF - American Journal of Water Science and Engineering JO - American Journal of Water Science and Engineering SP - 103 EP - 112 PB - Science Publishing Group SN - 2575-1875 UR - https://doi.org/10.11648/j.ajwse.20210703.12 AB - Forecasting of monthly streamflow for the White Nile River at Malakal station is a crucial aspect for different water resources projects in both countries Sudan and South Sudan. For instance, the operation of Jabal al Awliya dam in central Sudan entirely depends upon the measured flow of this station. In this paper, linear stochastic models well-known as seasonal autoregressive integrated moving average [SARIMA] models were used to model and forecast monthly flow of White Nile River in Malakal station, South Sudan. For the analysis, monthly flow data for the years running from 1970 up to 2013 were used. A scrutiny of the original series proves a yearly seasonal pattern. The results of Phillips-Perron (PP) test and Augmented Dickey Fuller (ADF) test on the streamflow series show that this series is not stationary. This non-stationarity was removed using first order seasonal differencing (i.e. twelve-monthly) preceding to the development of the model. The SARIMA (1,0,1)×(0,1,1)12 model was selected as the most suitable for modeling and forecasting monthly flow for White Nile River. It was found that the model was proper to forecast three successive years of monthly flow, which may help the experts to institute priorities for various water resources management in both countries. VL - 7 IS - 3 ER -
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