Teleconnection, Modeling, Climate Anomalies Impact and Forecasting of Rainfall and Streamflow of the Upper Blue Nile River Basin

The Nile River, the primary water resource and the life artery for its downstream countries such as Egypt and Sudan, exhibits strong seasonal fluctuations. The Upper Blue Nile basin (UBNB), the most significant tributary of the Nile, contributes more than half of the Nile's streamflow. Prompted by a lack of knowledge on the non-stationarity of hydro-climatic processes in the Ethiopian Highlands (EH), this thesis employed the non-stationary techniques of Wavelet principal component analysis (WPCA) and coherence analysis to identify the spatial, temporal and frequency variability regimes of these hydro-climatic processes of UBNB.;A fully distributed, physically-based model, a modified version of the Interactions Soil-Biosphere-Atmosphere model of Meteo France (MISBA), and a lumped, conceptual rainfall-runoff model, SAC-SMA of the US National Weather Service, was used to simulate the streamflow of UBNB. To study the potential hydrologic effect of climate anomalies on the UBNB, rainfall and temperature data observed when climate anomalies were active, were re-sampled and used to drive MISBA and SAC-SMA. The results provide useful information on the effects of global oceanic anomalies on the hydrology of UBNB.;An artificial neural network calibrated by a genetic algorithm (ANN-GA) model, was developed to forecast the seasonal rainfall of UBNB through teleconnection with selected oceanic sectors of SST. Results show that seasonal rainfall predicted by the ANN-GA agrees well with the observed rainfall data of UBNB. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the UBNB observed weekly rainfall characteristics.;ANN-GA was also forced with seasonal oceanic SST to directly forecast seasonal streamflow which was then disaggregated to weekly streamflow. Results indicate that forecasts with up to four months lead time based on climate indices achieve reasonable skill (correlation of 0.66), while combining rainfall and SST as predictors achieved better results (correlation of 0.83).;Knowledge gained on teleconnecting the climate of UBNB to oceanic SST and the possible impact of climate anomalies on the streamflow of UBNB will be useful for the planning and management of its water resources, especially during the threat of impending droughts.