Optimising ensemble streamflow predictions with bias correction and data assimilation techniques

Tanguy, Maliko; Eastman, Michael; Chevuturi, Amulya; Magee, Eugene; Cooper, Elizabeth; Johnson, Robert H. B.; Facer-Childs, Katie; Hannaford, Jamie

doi:https://doi.org/10.5194/hess-29-1587-2025

Articles | Volume 29, issue 6

https://doi.org/10.5194/hess-29-1587-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-29-1587-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 29, issue 6

Research article

|

25 Mar 2025

Research article |

| 25 Mar 2025

Optimising ensemble streamflow predictions with bias correction and data assimilation techniques

Maliko Tanguy, Michael Eastman, Amulya Chevuturi, Eugene Magee, Elizabeth Cooper, Robert H. B. Johnson, Katie Facer-Childs, and Jamie Hannaford

Data sets

Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) M. Tanguy et al. https://doi.org/10.5285/dbf13dd5-90cd-457a-a986-f2f9dd97e93c

Climate hydrology and ecology research support system potential evapotranspiration dataset for Great Britain (1961-2017) E. L. Robinson et al. https://doi.org/10.5285/9116e565-2c0a-455b-9c68-558fdd9179ad

Historic Gridded Potential Evapotranspiration (PET) based on temperature-based equation McGuinness-Bordne calibrated for the UK (1891-2015) M. Tanguy et al. https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c

Model code and software

Suite of GR Hydrological Models for Precipitation-Runoff Modelling L. Coron et al. https://cran.r-project.org/web/packages/airGR/index.html

Ensemble Forecast Verification for Large Data Sets MeteoSwiss https://cran.r-project.org/web/packages/easyVerification/index.html

Short summary

Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.