Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2939-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2939-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2022
Research article |
| 15 Jun 2022
| 15 Jun 2022
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
Gwyneth Matthews
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, United Kingdom
Christopher Barnard
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Hannah Cloke
Department of Meteorology, University of Reading, Reading, United Kingdom
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden
Sarah L. Dance
Department of Meteorology, University of Reading, Reading, United Kingdom
Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom
Toni Jurlina
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Cinzia Mazzetti
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Christel Prudhomme
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Department of Geography, University of Loughborough, Loughborough, United Kingdom
UK Centre for Ecology and Hydrology, Wallingford, United Kingdom
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Cited
12 citations as recorded by crossref.
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. 10.1016/j.jhydrol.2025.132904
- Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
- Optimising ensemble streamflow predictions with bias correction and data assimilation techniques M. Tanguy et al. 10.5194/hess-29-1587-2025
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Towards Informed Water Resources Planning and Management P. Reggiani et al. 10.3390/hydrology9080136
- Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations H. Hooker et al. 10.5194/nhess-23-2769-2023
- Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia M. Bari et al. 10.3390/w16101438
- Assessing the impact of weather forecast uncertainties in crop water stress model predictions B. Tarraf et al. 10.1016/j.agrformet.2024.109934
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- Urban flooding digital twin system framework C. Ge & S. Qin 10.1080/21642583.2025.2460432
12 citations as recorded by crossref.
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Improve streamflow simulations by combining machine learning pre-processing and post-processing Y. Zhang et al. 10.1016/j.jhydrol.2025.132904
- Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
- Optimising ensemble streamflow predictions with bias correction and data assimilation techniques M. Tanguy et al. 10.5194/hess-29-1587-2025
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Towards Informed Water Resources Planning and Management P. Reggiani et al. 10.3390/hydrology9080136
- Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations H. Hooker et al. 10.5194/nhess-23-2769-2023
- Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia M. Bari et al. 10.3390/w16101438
- Assessing the impact of weather forecast uncertainties in crop water stress model predictions B. Tarraf et al. 10.1016/j.agrformet.2024.109934
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- Urban flooding digital twin system framework C. Ge & S. Qin 10.1080/21642583.2025.2460432
Latest update: 30 Mar 2025
Short summary
The European Flood Awareness System creates flood forecasts for up to 15 d 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 does not 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.
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the...
