Current Overview on Science and Technology Research Vol. 2

Abstract

This study aimed to provide useful information for modeling monthly streamflow in Rivers, developing the appropriate strategy for managing the surface water under consideration and forming the basis of planning of major water resources. The management of extreme occurrences like floods and drought, as well as the ideal construction of water storage facilities and drainage networks, depend on the dynamic and precise forecasting of monthly streamflow processes of a river. The White Nile, Blue Nile, Atbara River, and main Nile are just a few of the rivers chosen for this study. The goal of this study is to provide the most effective linear stochastic model for predicting 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. Based on graphical and numerical criteria, the performance of model forecasts was compared. The outcome shows that for monthly streamflow in Rivers, deasonalized autoregressive moving average (DARMA) models outperform seasonal autoregressive integrated moving average (SARIMA) models.