ABSTRACT The dynamics and accurate forecasting of streamflow processes of a river are important in the management of extreme events such as floods and droughts, optimal design of water storage structures and drainage networks. In this study, attempt was made at investigating the appropriateness of stochastic modelling of the streamflow process of the Benue River using data-driven models based on univariate streamflow series. To this end, multiplicative seasonal Autoregressive Integrated Moving Average (ARIMA) model was developed for the logarithmic transformed monthly flows. The seasonal ARIMA model’s performance was compared with the traditional Thomas-Fiering model forecasts, and results obtained show that the multiplicative seasonal ARIMA model was able to forecast flow logarithms. However, it could not adequately account for the seasonal variability in the monthly standard deviations. The forecast flow logarithms therefore cannot readily be transformed into natural flows; hence, the need for cautious optimism in its adoption, though it could be used as a basis for the development of an Integrated Riverflow Forecasting System (IRFS). Since forecasting could be a highly “noisy” application because of the complex river flow system, a distributed hydrological model is recommended for real-time forecasting of the river flow regime especially for purposes of sustainable water resources management.
Cite this paper
O. ., Y. Martins, I. Ahaneku and M. Sadeeq, "Parametric Linear Stochastic Modelling of Benue River flow Process," Open Journal of Marine Science, Vol. 1 No. 3, 2011, pp. 73-81. doi: 10.4236/ojms.2011.13008.
 L. Garrote and R. L Bras, “A Distributed Model for Real-Time Flood Forecasting Using Digital Elevation Models,” Journal of Hydrology, Vol. 167, No. 1, 1995, pp. 279-306. doi:10.1016/0022-1694(94)02592-Y
 J. C. Refsgaard and J. Knudsen, “Operational Validation and Intercomparison of Dif-ferent Types of Hydrological Models,” Water Resources Research, Vol. 32, No. 7, 1996, pp. 2189-2202. doi:10.1029/96WR00896
 E Todini, “The ARNO Rainfall-Runoff Model,” Journal of Hydrology, Vol. 175, No. 2, 1996, pp. 339-382.
 J. Buchtele, V. Elias, M. Tesar and A. Herman, “Runoff Components Simulated by Rainfall-Runoff Models,” Journal of Hydrological Sciences, Vol. 41, No. 1, 1996, pp. 49-60. doi:10.1080/02626669609491478
 K. L. Hsu, H. V. Gupta and S. Sorooshian, “Artificial Neural Network Modelling of Therainfall-Runoff Process," Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955
 D. Mukherjee and N. Mansour, “Estimation of Flood Forecasting Errors and Flow-Duration Joint Probabilities of Exceedance,” Journal of Hydrologic Engineering, Vol. 122, No. 3, 1996, pp. 130-140.
 H. Ra-man and Sunikumar, “Multivariate Modelling of Water Resources Time Series Using Artificial Neural Networks,” Journal of Hydrological Sciences, Vol. 40, No. 4, 1995, pp. 145-163.
 C. M. Zealand, D. H. Burn and S. P. Simonovic, “Short Term Forecasting Using Artificial Neural Networks. Journal of Hydrology, Vol. 214, No. 1-4, 1999, pp. 32-48.
 N. T. Kottegoda, “Stochastic Water Resources Technology,” The Macmillan Press Ltd, London, 1980, pp. 2-3, 21, 112-113.
 G. E. P. Box and G. M Jenkins, “Time Series Analysis Forecasting and Control,” Holden-Day Press, San Francisco, 1976, pp. 32-100.
 R. F. Carlson, A. J. A. McCormick and D. G. Watts, “Application of Linear Random Models to Four Annual Flow Series,” Water Resources Research, Vol. 6, No. 4, 1970, pp. 1070-1078. doi:10.1029/WR006i004p01070
 A. I. McKerchar and J. W. Delleur, “Application of Seasonal Para-metric Linear Stochastic Models to Monthly Flow Data,” Water Resources Research, Vol. 10, No. 2, 1974, pp. 246-254. doi:10.1029/WR010i002p00246
 H. A. Thomas and M. B. Fiering, “Mathematical Synthesis of Streamflow Sequences for the Analysis of River Basins by Simulations,” In Design of Water Resource Systems, Edited by Mass et al., Harvard University Press, Cambridge, 1962, pp. 459-493.