Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 11
Articles | Volume 29, issue 11
https://doi.org/10.5194/hess-29-2393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2393-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 11
Research article
 | 
10 Jun 2025
Research article |  | 10 Jun 2025

Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts

Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 571 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
509 48 14 571 11 13 23
  • HTML: 509
  • PDF: 48
  • XML: 14
  • Total: 571
  • Supplement: 11
  • BibTeX: 13
  • EndNote: 23
Views and downloads (calculated since 23 Sep 2024)
Cumulative views and downloads (calculated since 23 Sep 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 571 (including HTML, PDF, and XML) Thereof 549 with geography defined and 22 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Jul 2025
Download
Short summary
Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Read more
Share