Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jul 2022
Research article |
| 05 Jul 2022
| 05 Jul 2022
Deep learning rainfall–runoff predictions of extreme events
National Water Center, National Oceanic and Atmospheric Administration, Tuscaloosa, AL, USA
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Frederik Kratzert
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Daniel Klotz
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Martin Gauch
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Guy Shalev
Google Research, Tel Aviv, Israel
Oren Gilon
Google Research, Tel Aviv, Israel
Logan M. Qualls
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Hoshin V. Gupta
Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ, USA
Grey S. Nearing
Google Research, Mountain View, CA, USA
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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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We present multi-timescale Short-Term Memory (MTS-LSTM), a machine learning approach that predicts discharge at multiple timescales within one model. MTS-LSTM is significantly more accurate than the US National Water Model and computationally more efficient than an individual LSTM model per timescale. Further, MTS-LSTM can process different input variables at different timescales, which is important as the lead time of meteorological forecasts often depends on their temporal resolution.
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Short summary
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a...
