Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 6
Articles | Volume 26, issue 6
https://doi.org/10.5194/hess-26-1673-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1673-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 6
Research article
 | Highlight paper
 | 
31 Mar 2022
Research article | Highlight paper |  | 31 Mar 2022

Uncertainty estimation with deep learning for rainfall–runoff modeling

Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing

Viewed

Total article views: 37,598 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
21,926 15,498 174 37,598 204 201
  • HTML: 21,926
  • PDF: 15,498
  • XML: 174
  • Total: 37,598
  • BibTeX: 204
  • EndNote: 201
Views and downloads (calculated since 14 Apr 2021)
Cumulative views and downloads (calculated since 14 Apr 2021)

Viewed (geographical distribution)

Total article views: 37,598 (including HTML, PDF, and XML) Thereof 34,401 with geography defined and 3,197 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Nov 2025
Download
Short summary
This contribution evaluates distributional runoff predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.
Read more
Share