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.
Highlights
Streamflow forecasting is of great importance to water resources management and planning
The autocorrelation function (ACF) graphs show an attenuating sine wave pattern that reflects the random periodicity of the data
This study aims to select the suitable stochastic model in forecasting monthly streamflow in rivers
Summary
Streamflow forecasting is of great importance to water resources management and planning. How to cite this paper: Elganiny, M.A. and Eldwer, A.E. (2016) Comparison of Stochastic Models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and Its Tributaries. E. Eldwer operations and irrigation management, as well as institutional and legal aspects of water resources management and planning. Eldwer operations and irrigation management, as well as institutional and legal aspects of water resources management and planning Due to their importance, a large number of forecasting models have been developed for Streamflow forecasting, including concept-based process-driven models such as the low flow recession model and rainfall-runoff models, and statistics-based data-driven models such as regression models, stochastic time series models [1]
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