The performance of a river flow forecasting model employing a Kalman filtering algorithm was evaluated for increasing forecast lead times. The expected decrease in forecast accuracy was quantified and a decrease in forecast precision was noted for increased lead times. The merits of external estimates of meteorological inputs to the model were evaluated through an examination of different forecasting options. It was revealed that even noisy estimates of meteorological events improved the flow forecasts.Key words: forecasting, Kalman filter, real time, precipitation, snowmelt.