Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 6
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

Viewed

Total article views: 3,384 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,093 239 52 3,384 30 22
  • HTML: 3,093
  • PDF: 239
  • XML: 52
  • Total: 3,384
  • BibTeX: 30
  • EndNote: 22
Views and downloads (calculated since 03 Jul 2024)
Cumulative views and downloads (calculated since 03 Jul 2024)

Viewed (geographical distribution)

Total article views: 3,384 (including HTML, PDF, and XML) Thereof 3,384 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Mar 2025
Download
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.
Read more
Share