Post‐processing of medium‐range probabilistic hydrological forecasting: impact of forcing, initial conditions and model errors

Abstract

Abstract The impact of errors in the forcing, errors in the model structure and parameters, and errors in the initial conditions is investigated in a simple hydrological ensemble prediction system. The hydrological model is based on an input nonlinearity connected with a linear transfer function and forced by precipitation forecasts from the European Centre for Medium‐Range Weather Forecast (ECMWF) Ensemble Prediction System (EPS). The post‐processing of the precipitation and/or the streamflow using information from the reforecasts performed by ECMWF is tested. For this purpose, hydrological reforecasts are obtained by forcing the hydrological model with the precipitation from the reforecast data. In the present case study, it is found that the post‐processing of the hydrological ensembles with a statistical model fitted on the hydrological reforecasts improves the verification scores better than the use of post‐processed precipitation ensembles. In the case of large biases in the precipitation, combining the post‐processing of both precipitation and streamflow allows for further improvements. During the winter, errors in the initial conditions have a larger impact on the scores than errors in the model structure as designed in the experiments. Errors in the parameter values are largely corrected with the post‐processing. Copyright © 2014 John Wiley & Sons, Ltd.