Incorporating forecasts of rainfall in two hydrologic models used for medium-range streamflow forecasting

This study reports on the performance of two medium-range streamflow forecast models: 1 a multilayer feed-forwardartificial neural network; and 2 a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models.The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitativeprecipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilianhydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km2, with lead times up to12 days, at daily intervals. The Nash–Sutcliffe efficiency index, the root-mean-square error, the mean absolute error, and the mean relativeerror were used to assess the relative performance of the models. Results showed that the performance of streamflow forecasts wasstrongly dependent on the quality of quantitative precipitation forecasts used. The artificial neural network ANN method seemed to beless sensitive to precipitation forecast error relative to the distributed hydrological model. Hence, the latter presented a better skill in flowforecasting when using the more accurate perfect precipitation forecast. The conceptual hydrological model also demonstrates betterforecast skill than ANN models for longer lead times, when the representation of the rainfall-runoff process and of the water storage inthe watershed becomes more important than the flow routing along the drainage network. In addition, results obtained by incorporating aquantitative precipitation forecast in both models performed better than the current streamflow obtained by the Brazilian national electricsystem operator using statistical models which do not utilize information on precipitation, whether observed or forecast