Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 4
Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-2045-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2045-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 4
Research article
 | 
19 Apr 2021
Research article |  | 19 Apr 2021

Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network

Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter

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Short summary
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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