Preprints
https://doi.org/10.5194/hess-2021-539
https://doi.org/10.5194/hess-2021-539

  10 Nov 2021

10 Nov 2021

Review status: this preprint is currently under review for the journal HESS.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Gwyneth Matthews1, Christopher Barnard2, Hannah Cloke1,2,3,4,5, Sarah L. Dance1,6, Toni Jurlina2, Cinzia Mazzetti2, and Christel Prudhomme2,7,8 Gwyneth Matthews et al.
  • 1Department of Meteorology, University of Reading, Reading, United Kingdom
  • 2European Centre for Medium-range Weather Forecasts, Reading, United Kingdom
  • 3Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom
  • 4Department of Earth Sciences, Uppsala University, Uppsala, Sweden
  • 5Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden
  • 6Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom
  • 7Department of Geography, University of Loughborough, Loughborough, United Kingdom
  • 8UK Centre for Ecology and Hydrology, Wallingford, United Kingdom

Abstract. 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 both in terms of 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 days) 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 to make more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Gwyneth Matthews et al.

Status: open (until 05 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Gwyneth Matthews et al.

Data sets

Post-processed reforecasts of the European Flood Awareness System and related evaluation data Gwyneth Matthews, Christopher Barnard http://dx.doi.org/10.17864/1947.333

Model code and software

Post-processed reforecasts of the European Flood Awareness System and related evaluation data Gwyneth Matthews, Christopher Barnard http://dx.doi.org/10.17864/1947.333

Gwyneth Matthews et al.

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Short summary
The European Flood Awareness System creates flood forecasts for up to 15 days in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used doesn’t represent the flow in the rivers correctly. We found that by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected thus becoming more useful.