摘要：To improve the management of operation system for the Roseires reservoir it is necessary to know the hydrological system of the Blue Nile river, which is the main water source of the reservoir. In this work, a Modified Thomas Fiering model for generating and forecasting monthly flow is used. The methodological procedure is applied on the data obtained at the gauging station of Eldeim in Blue Nile, Sudan. The study uses the monthly flows data for years 1965 to 2009. After estimation the model parameters, the synthetic time series of monthly flows are simulated. The results revealed that the model maintained most of the basic statistical descriptive parameters of historical data. Also, the Modified Thomas Fiering model is applied to predict the values of the next fifty-five years, with excellent results that conserved most basic statistical characteristics of runoff historical series. The Modified Thomas Fiering model is able to realistically reconstruct and predict the annual data and shows promising statistical indices.
其他摘要：To improve the management of operation system for the Roseires reservoir it is necessary to know the hydrological system of the Blue Nile river, which is the main water source of the reservoir. In this work, a Modified Thomas Fiering model for generating and forecasting monthly flow is used. The methodological procedure is applied on the data obtained at the gauging station of Eldeim in Blue Nile, Sudan. The study uses the monthly flows data for years 1965 to 2009. After estimation the model parameters, the synthetic time series of monthly flows are simulated. The results revealed that the model maintained most of the basic statistical descriptive parameters of historical data. Also, the Modified Thomas Fiering model is applied to predict the values of the next fifty-five years, with excellent results that conserved most basic statistical characteristics of runoff historical series. The Modified Thomas Fiering model is able to realistically reconstruct and predict the annual data and shows promising statistical indices.
