ISSN 0097-8078, Water Resources, 2018, Vol. 45, No. 3, pp. 326–337. © Pleiades Publishing, Ltd., 2018.
WATER RESOURCES AND THE REGIME OF WATER BODIES
Stochastic Forecasting Models of the Monthly Streamflow for the Blue Nile at Eldiem Station1 Mohamed A. Elganainy* and Alaa E. Eldwer Irrigation Engineering and Hydraulics Department, Faculty of Engineering, Alexandria University, Alexandria, 21544 Egypt *e-mail:
[email protected] Received December 10, 2015
Abstract⎯Egypt is almost totally dependent on the River Nile for satisfying about 95% of its water requirements. The River Nile has three main tributaries: White Nile, Blue Nile, and River Atbara. The Blue Nile contributes about 60% of total annual flow reached the River Nile at Aswan High Dam. The goal of this research is to develop a reliable stochastic model for the monthly streamflow of the Blue Nile at Eldiem station, where the Grand Ethiopian Renaissance Dam (GERD) is currently under construction with a storage capacity of about 74 billion m3. The developed model may help to carry out a reliable study on the filling scenarios of GERD reservoir and to minimize its expected negative side effects on Sudan and Egypt. The linear models: Deseasonalized AutoRegressive Moving Average (DARMA) model, Periodic AutoRegressive Moving Average (PARMA) model and Seasonal AutoRegressive Integrated Moving Average (SARIMA) model; and the nonlinear Artificial Neural Network (ANN) model are selected for modeling monthly streamflow at Eldiem station. The performance of various models during calibration and validation were evaluated using the statistical indices: Mean Absolute Error, Root Mean Square Error and coefficient of determination (R 2) which indicate the strength of fitting between observed and forecasted values. The results show that the performance of the nonlinear model (ANN) was much better than all investigated linear models (DARMA, PARMA and SARIMA) in forecasting the monthly flow discharges at Eldiem station. Keywords: Nile River, Blue Nile, Eldiem station, GERD, DARMA model, PARMA model, SARIMA, ANN DOI: 10.1134/S0097807818030041
INTRODUCTION The River Nile is the main source of water in Egypt, where Egypt relies on it to meet about 95% of its water requirements. The Nile Basin covers an area of about 2.9 million km2, approximately one-tenth of the surface area of Africa. It extends from about 4° S to 31° N latitude and from about 21°30′ E to 40°30′ E longitude. The length of the River Nile is about 6500 km [45]. The Nile basin is shared by 11 countries: Ethiopia, Sudan, South Sudan, Egypt, Tanzania, Burundi, Democratic Republic of Congo, Kenya, Eritrea, Rwanda, and Uganda. The River Nile has three main tributaries: White Nile, Blue Nile, and River Atbara as shown in Fig. 1. The River Nile depends largely on the Blue Nile, which contributes about 60% of total annual flow reached the River Nile at Aswan High Dam (AHD) [14, 50]. Four major multi-purpose dams along the upper Blue Nile in Ethiopia are proposed according to the study of the United States Bureau of Reclamation (USBR) in 1964. These dams are Karadobi, Mabil, Mendaia and Border (GERD) (Fig. 1). 1 The article is published in the original.
The GERD is the most downstream of the proposed dams’ sites and currently is under construction at Eldiem station, very close to the Ethiopian–Sudanese borders. GERD would regulate the water flow of the lower Blue Nile in its Sudanese part around the year. Consequently, this brings a group of benefits in the lower Blue Nile in its Sudanese part including flood protection which tends to increase horizontal irrigation expansion projects and increasing the efficiency of both Sennar and Roseires dams due to reducing siltation in there reservoirs. Many researches, using hydrologic models, are carried out to study the impacts of GERD construction on Sudan and Egypt. Among these researches King and Block [28] focused on the implications of filling “reservoir policies” on generated hydro-power of the dam and negative impacts on livelihoods in the downstream countries (Sudan and Egypt). Mulat and Moges [34] carried out a study to assess the potential impacts of the GERD on the performance of the AHD during its filling and operation phases using Mike Basin river basin simulation model. Abdelhaleem and Helal [1] studied the potential impact of the shortage of Egypt’s water resources that will reduce the releases from AHD due to the construction of the GERD
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STOCHASTIC FORECASTING MODELS OF THE MONTHLY STREAMFLOW
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Alexandria Cairo
DS RE
Egypt
Suez
Aswan
EA
Wadi Haifa Main Nile Port Sudan
Eritrea
Sudan Khartoum
Terezze Atbaka Eldiem Gerd
El Obeid Lower White Nile
Bahr el Ghazal
Asmara
Upper slue Nile Addis Ababa
South Sudan Sudo
Bara akabopibor
Juba Dr Congo
Ethiopia
Kenya
Victoria Nile Albert Nile
Uganda Kampala Lake Victoria Victoria Lake Rwanda Kigali Bujumbura Burundi Tanzania
Nairobi
Mombasa
0
250
500 km
Fig. 1. The River Nile basin and its tributaries.
using SOBEK model. Ahmed and Elsanabary [3], using HEC-RAS model, studied the hydrological and environmental impacts of GERD on Sudan and Egypt including the possibility of the dam failure. Ramadan et al. [38] studied the environmental impacts of GERD on the Egyptian water resources. They used the river basin modeling and simulation package (MODSIM). No doubt that developing a reliable stochastic model of the monthly streamflow for the Blue Nile at Eldiem station, where the GERD is currently under construction may help to carry out a reliable study on the filling scenarios and management of GERD reservoir and to minimize its expected negative side effects on both Sudan and Egypt. Such a model may WATER RESOURCES
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also help to develop a co-variate stochastic model between Eldiem and Aswan stations. It is widely recognized that time series modelling can be the better option for the area where nothing but only hydrological time series data are in hand. A time series model is an empirical model for simulating and forecasting the behavior of uncertain hydrologic system [27]. Time series analysis has been most widely used in previous decades because of its forecasting capability, inclusion of richer information, and more systematic way of building models in three modeling stages (identification, estimation, and diagnostic check), as standardized by [10]. The types of time series models are univariate, bivariate and multivariate time series models. The univariate time series models
