Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling

When choosing the rainfall‐runoff modeling approach to be integrated in a river flow forecasting system, two crucial issues are the minimum data requirement for calibration purposes and the reliability of the predictions over different time horizons (lead‐times). The paper presents an investigation of the real‐time forecasting ability of a conceptual and a neural network model, comparing the performances obtainable for increasing lead‐times and analyzing the influence of the amount of the calibration data over two real‐data case studies. Neural networks proved to be an excellent tool for the real‐time rainfall‐runoff simulation of continuous periods (including low, average and peak flows), provided that an extensive set of hydro‐meteorological data is available for calibration purposes. On the other hand, the comparison highlights that a conceptual formulation may allow a significant forecasting improvement in comparison with the data‐driven approach when focusing on the prediction of flood events and especially in case of a limited availability of calibration data.