COMPARISON OF STOCHASTIC MODELS IN FORECASTING MONTHLY STREAMFLOW INRIVERS, A CASE STUDY OF RIVER NILE AND ITS TRIBUTARIES

The dynamic and accurate forecasting of monthly streamflow processes of ariver are important in the management of extreme events such as floods anddrought, optimal design of water storage structures and drainage network.Many Rivers are selected in this study; White Nile; Blue Nile; Atbara Riverand main Nile. This paper aims to recommend the best linear stochasticmodel in forecasting monthly streamflow in rivers. Two commonlyhydrologic models; the deasonalized autoregressive moving average(DARMA) models and seasonal autoregressive integrated moving average(SARIMA) models are selected for modeling monthly streamflow in allRivers in the study area. Two different types of monthly streamflow data(deseasonalized data and differenced data) were used to develop time seriesmodel using previous flow conditions as predictors.The one month ahead forecasting performances of all models for predictedperiod were compared. The comparison of model forecasting performancewas conduct based upon graphical and numerical criteria. The result indicatethat deasonalized autoregressive moving average (DARMA) modelsperform better than seasonal autoregressive integrated moving average(SARIMA) models for monthly streamflow in Rivers.

Keywords:

Monthly stream flow , River Nile , DARMAmodel , SARIMA model , stochastic model