Articles | Volume 22, issue 3
https://doi.org/10.5194/hess-22-2023-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-22-2023-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Benchmarking ensemble streamflow prediction skill in the UK
European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield
Park, Reading, RG2 9AX, UK
Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB,
UK
Christel Prudhomme
European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield
Park, Reading, RG2 9AX, UK
Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB,
UK
Department of Geography, Loughborough University, Loughborough,
Leicestershire, LE11 3TU, UK
Simon Parry
Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB,
UK
Katie Smith
Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB,
UK
Maliko Tanguy
Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB,
UK
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- Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
- GloFAS-ERA5 operational global river discharge reanalysis 1979–present S. Harrigan et al. 10.5194/essd-12-2043-2020
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- Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting D. McInerney et al. 10.1029/2019WR026979
- Improving Flood Forecasting Skill by Combining Ensemble Precipitation Forecasts and Multiple Hydrological Models in a Mountainous Basin Y. Xiang et al. 10.3390/w16131887
- Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia M. Bari et al. 10.3390/w16101438
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
- Hydrological drought forecasts outperform meteorological drought forecasts S. Sutanto et al. 10.1088/1748-9326/ab8b13
- A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information D. Tian et al. 10.1007/s00477-021-02023-y
- Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
- Quantifying multi-year hydrological memory with Catchment Forgetting Curves A. de Lavenne et al. 10.5194/hess-26-2715-2022
- Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
- Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale G. Piazzi et al. 10.1029/2020WR028390
3 citations as recorded by crossref.
- Assessing the skill of the UK Hydrological Outlook D. Boorman & S. Turner 10.1080/02626667.2019.1679375
- Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales C. Prudhomme et al. 10.1080/02626667.2017.1395032
- Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region A. Muhammad et al. 10.3390/w10111604
Latest update: 14 Dec 2024
Short summary
We benchmarked when and where ensemble streamflow prediction (ESP) is skilful in the UK across a diverse set of 314 catchments. We found ESP was skilful in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. Results have practical implications for current operational use of the ESP method in the UK.
We benchmarked when and where ensemble streamflow prediction (ESP) is skilful in the UK across a...
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