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

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

doi:https://doi.org/10.5194/hess-25-2685-2021

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

Data sets

Benchmark models Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing https://doi.org/10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1

Extended Maurer forcings Frederik Kratzert https://doi.org/10.4211/hs.17c896843cf940339c3c3496d0c1c077

Extended NLDAS forcings Frederik Kratzert https://doi.org/10.4211/hs.0a68bfd7ddf642a8be9041d60f40868c

Pre-trained models Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing https://doi.org/10.5281/zenodo.4670268

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.