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The adaptive use of a conceptual model for real‐time flow forecasting is investigated. Maximum likelihood and ordinary least squares estimation criteria are considered, and the performance of maximum likelihood techniques for autocorrelated (AMLE) and heteroscedastic (HMLE) errors is analyzed jointly with that provided by the commonly used ordinary least squares estimation (OLSE) technique. Streamflow forecasts are compared for three rivers in central Italy, obtained by AMLE, HMLE, and OLSE adaptive calibration of a simple conceptual model describing the rainfall‐runoff transformation by accounting for Hortonian infiltration and linear basin response to rainfall excess. Although model residuals display both autocorrelation and heteroscedasticity, OLSE is found to provide a rather satisfactory performance. Because the OLSE technique also requires less computational effort compared to that for AMLE and HMLE, one could consider OLSE as a suitable option for real‐time model operation.
Brath et al. (Sun,) studied this question.