Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 6
Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 6
Technical note
 | 
11 Jun 2021
Technical note |  | 11 Jun 2021

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Sebastian Scher and Stefanie Peßenteiner

Viewed

Total article views: 3,085 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,172 842 71 3,085 95 102
  • HTML: 2,172
  • PDF: 842
  • XML: 71
  • Total: 3,085
  • BibTeX: 95
  • EndNote: 102
Views and downloads (calculated since 30 Sep 2020)
Cumulative views and downloads (calculated since 30 Sep 2020)

Viewed (geographical distribution)

Total article views: 3,085 (including HTML, PDF, and XML) Thereof 2,928 with geography defined and 157 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Nov 2025
Download
Short summary
In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can learn what a typical distribution over the day looks like.
Read more
Share