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

Model code and software

pr-disagg-radar-gan S. Scher https://github.com/sipposip/pr-disagg-radar-gan

pr-disagg-gan S. Scher and S. Peßenteiner https://doi.org/10.5281/zenodo.3733065

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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.