The natural variability in the annual flow of the Nile is significantly regulated by the El Nino-Southern Oscillation (ENSO). In this paper, several sources of information are combined, including ENSO, rainfall over Ethiopia, and the recent history of river flow in the Nile, in order to obtain accurate forecasts of the Nile hood at Aswan. The Bayesian theorem is used in developing the discriminant forecasting algorithm. Conditional categoric probabilities are used to describe the flood forecasts, and a synoptic index is defined to measure the forecasts’ skill. The results presented show that ENSO information is the only valuable predictor for the long-range forecasts (lead time longer than the hydrological response timescale, which is 2-3 months in this study). However, the incorporation of the rainfall and river flow information in addition to the ENSO information significantly improves the quality of the medium-range forecasts (lead time shorter than the hydrological response timescale).
