Comparison of Stochastic Models in Forecasting Monthly Streamflow in Rivers: A Case Study of River Nile and Its Tributaries  

作　　者：Mohammed A. Elganiny Alaa Esmaeil Eldwer Mohammed A. Elganiny;Alaa Esmaeil Eldwer(Irrigation Eng. and Hydraulics Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt)

机构地区：[1]Irrigation Eng. and Hydraulics Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt

出　　处：《Journal of Water Resource and Protection》2016年第2期143-153,共11页水资源与保护（英文）

摘　　要：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.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.

关 键 词：Monthly Streamflow River Nile DARMA Model SARIMA Model Stochastic Model 

分 类 号：P33[天文地球—水文科学]