Evaluating the post-processing of the European Flood Awareness System’s continental scale streamflow forecasts

Floods are the most common and disastrous natural hazards and due to climate change and socio-economic growth floods are becoming more destructive. Early warning systems are one of the best ways to decrease the effect of floods by increasing preparedness. However, uncertainties are inevitably introduced throughout streamflow forecasting systems and untreated they can limit the value of the forecasts to end-users. In recent decades several post-processing methods have been introduced and shown to improve forecast skill through bias and spread correction. The European Flood Awareness System, part of the European Commission's Copernicus Emergency Management Service, post-processes its medium-range ensemble flood forecasts at over a thousand stations across Europe where historic and near real-time observations are available. A combination of different techniques, namely the Model Conditional Processor, Ensemble Model Output Statistics, and the Kalman Filter are used to account for hydrological and meteorological uncertainties using recent observations and forecasts. Evaluation of the post-processing method is performed using two years of twice-weekly reforecasts. In general, the skill of the forecasts is improved, but the magnitude of this improvement decreases at longer lead-times as recent observations become less impactful. The improvement from post-processing is found to vary substantially between the stations and the continuous ranked probability skill score is used to investigate the impact of different station characteristics. Low-lying large catchments are shown to have the greatest increase in skill from post-processing whereas small high-elevation catchments are harder to correct. However, it was found that the flood magnitudes observed in the historic record are of greater importance than the length of the record itself for determining the effectiveness of the post-processing method.