Parametric Linear Stochastic Modelling of Benue River Flow Process,

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.