The Nile river flow is negatively correlated with the Sea Surface Temperature (SST) in the tropical eastern Pacific Ocean (TEP), an index of El Nino - Southern Oscillation (ENSO). In this paper, we combine several sources of information, including ENSO, rainfall over Ethiopia and the recent history of river flow in the Nile, in order to obtain accurate forecasts of the Nile flood at Aswan. Bayesian theorem is used in developing the discriminant forecasting algorithm. We use conditional categoric probability to describe the flood forecasts, and define a synoptic index to measure the forecasts skill. Our results show that ENSO information is the only valuable predictor for the long-range forecasts (lead time longer than the hydrological response time scale, 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 time scale).