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Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECM