Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 5
Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2685-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2685-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 5
Research article
 | 
20 May 2021
Research article |  | 20 May 2021

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing

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Short summary
We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.
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