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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Cited articles

Adadi, A. and Berrada, M.: Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access, 6, 52138–52160, https://doi.org/10.1109/ACCESS.2018.2870052, 2018. a
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Bihlo, A.: A generative adversarial network approach to (ensemble) weather prediction, Neural Netw., 139, 1–16, https://doi.org/10.1016/j.neunet.2021.02.003, 2021. a
Breinl, K. and Di Baldassarre, G.: Space-time disaggregation of precipitation and temperature across different climates and spatial scales, J. Hydrol.: Reg. Stud., 21, 126–146, https://doi.org/10.1016/j.ejrh.2018.12.002, 2019. a
Burian, S. J., Durrans, S. R., Tomic̆, S., Pimmel, R. L., and Chung Wai, N.: Rainfall Disaggregation Using Artificial Neural Networks, J. Hydrol. Eng-ASCE, 5, 299–307, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(299), 2000. a
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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.