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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (08 Feb 2021) by Fabrizio Fenicia
AR by Martin Gauch on behalf of the Authors (15 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (02 Mar 2021) by Fabrizio Fenicia
RR by Jens Kiesel (05 Mar 2021)
ED: Publish subject to technical corrections (17 Mar 2021) by Fabrizio Fenicia
AR by Martin Gauch on behalf of the Authors (18 Mar 2021)  Author's response    Manuscript
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.