JWARP  Vol.8 No.2 , February 2016
Comparison of Stochastic Models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and Its Tributaries
Abstract: The dynamic and accurate forecasting of monthly streamflow processes of a river are important in the management of extreme events such as floods and drought, optimal design of water storage structures and drainage network. Many Rivers are selected in this study: White Nile, Blue Nile, Atbara River and main Nile. This paper aims to recommend the best linear stochastic model in forecasting monthly streamflow in rivers. Two commonly hydrologic models: the deseasonalized autoregressive moving average (DARMA) models and seasonal autoregressive integrated moving average (SARIMA) models are selected for modeling monthly streamflow in all Rivers in the study area. Two different types of monthly streamflow data (deseasonalized data and differenced data) were used to develop time series model using previous flow conditions as predictors. The one month ahead forecasting performances of all models for predicted period were compared. The comparison of model forecasting performance was conducted based upon graphical and numerical criteria. The result indicates that deasonalized autoregressive moving average (DARMA) models perform better than seasonal autoregressive integrated moving average (SARIMA) models for monthly streamflow in Rivers.
Cite this paper: Elganiny, M. and Eldwer, A. (2016) Comparison of Stochastic Models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and Its Tributaries. Journal of Water Resource and Protection, 8, 143-153. doi: 10.4236/jwarp.2016.82012.

[1]   Wang, W. (2006) Stochasticity, Nonlinearity and Forecasting of Streamflow Processes. IOS Press, Amsterdam.

[2]   Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994) Time Series Analysis: Forecasting and Control. Prentice-Hall, Upper Saddle River.

[3]   Brockwell, P.J. and Davis, R.A. (2002) Introduction to Time Series and Forecasting. Springer, New York.

[4]   Hipel, K.W. and McLeod, A.I. (1994) Time Series Modeling of Water Resources and Environmental Systems. Elsevier Science, Amsterdam.

[5]   Mondal, M.S. and Wasimi, S.A. (2007) Choice of Model Type in Stochastic River Hydrology. Proceedings of the 1st International Conference on Water & Flood Management (ICWFM-2007), Dhaka, 12-14 March 2007, 633-640.

[6]   Bender, M. and Simonovic, S. (1994) Time-Series Modeling for Long-Range Streamflow Forecasting. Journal of Water Resources Planning and Management, 120, 857-870.

[7]   Ghanbarpour, M.R., Abbaspour, K.C., Jalalvand, G. and Moghaddam, G.A. (2010) Stochastic Modeling of Surface Stream Flow at Different Time Scales: Sangsoorakh karst Basin, Iran. Journal of Cave and Karst Studies, 72, 1-10.

[8]   McKerchar, A.I. and Delleur, J.W. (1974) Application of Seasonal Parametric Linear Stochastic Models to Monthly Flow Data. Water Resources Research, 10, 246-255.

[9]   Noakes, D.J., McLeod, A.I. and Hipel, K.W. (1985) Forecasting Monthly Riverflow Time Series. International Journal of Forecasting, 1, 179-190.

[10]   Salas, J.D. (1992) Analysis and Modeling of Hydrologic Time Series. In: Maidment, D.R., Ed., Handbook of Hydrology, McGraw-Hill, New York, 19.1-19.72.

[11]   Rabenja, A.T., Ratiarison, A. and Rabeharisoa, J.M. (2009) Forecasting of the Rainfall and the Discharge of the Namorona River in Vohiparara and FFT Analyses of These Data. Proceedings of 4th International Conference in High-Energy Physics, Antananarivo, 1-12.

[12]   Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control. Revised Edition, Holden-Day, San Francisco.

[13]   Hipel, K.W., McLeod, A.I. and Lennox, W.C. (1977) Advances in Box-Jenkins Modeling, Part One, Model Construction. Water Resources Research, 13, 567-575.

[14]   Ljung, G.M. and Box, G.E.P. (1978) On a Measure of Lack of Fit in Time Series Models. Biometrika, 65, 297-303.

[15]   Chow, V.T., Maidment, D.R. and Mays, L.W. (1988) Applied Hydrology. McGraw-Hill Book Company, New York, 572.

[16]   Razali, N. and Wah, Y.B. (2011) Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling Tests. Journal of Statistical Modeling and Analytics, 2, 21-33.

[17]   Shahin, M. (1985) Hydrology of the Nile Basin. Elsevier Sciences Publishing co., New York.