Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1587-2025
https://doi.org/10.5194/hess-29-1587-2025
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

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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.
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