Articles | Volume 25, issue 6
Hydrol. Earth Syst. Sci., 25, 3207–3225, 2021
https://doi.org/10.5194/hess-25-3207-2021
Hydrol. Earth Syst. Sci., 25, 3207–3225, 2021
https://doi.org/10.5194/hess-25-3207-2021

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

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (11 Feb 2021) by Nadav Peleg
AR by Sebastian Scher on behalf of the Authors (25 Mar 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (30 Mar 2021) by Nadav Peleg
RR by Daniele Nerini (08 Apr 2021)
ED: Publish as is (09 Apr 2021) by Nadav Peleg
AR by Sebastian Scher on behalf of the Authors (16 Apr 2021)  Author's response
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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.