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. In the present study we analyze theeffect of bias adjustments in both meteorological and streamflow forecasts onthe skill and statistical consistency of monthly streamflow and yearlyminimum daily flow forecasts. Both raw and preprocessed meteorological seasonal forecasts from the European Centre for Medium-RangeWeather Forecasts (ECMWF) are used as inputs to a spatially distributed,coupled surface–subsurface hydrological model based on the MIKE SHE code.Streamflow predictions are then generated up to 7 months in advance. Inaddition to this, we post-process streamflow predictions using an empiricalquantile mapping technique. Bias, skill and statistical consistency are thequalities evaluated throughout the forecast-generating strategies and weanalyze where the different strategies fall short to improve them. ECMWFSystem 4-based streamflow forecasts tend to show a lower accuracy level thanthose generated with an ensemble of historical observations, a methodcommonly known as ensemble streamflow prediction (ESP). This is particularlytrue at longer lead times, for the dry season and for streamflow stationsthat exhibit low hydrological model errors. Biases in the mean are betterremoved by post-processing that in turn is reflected in the higher level ofstatistical consistency. However, in general, the reduction of these biasesis not sufficient to ensure a higher level of accuracy than the ESPforecasts. This is true for both monthly mean and minimum yearly streamflowforecasts. We discuss the importance of including a better estimation of theinitial state of the catchment, which may increase the capability of thesystem to forecast streamflow at longer leads.

hydrology and earth system sciences

Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency