Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5449-2022
© 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-26-5449-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2022
Research article |
| 01 Nov 2022
| 01 Nov 2022
Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
Kieran M. R. Hunt
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
National Centre for Atmospheric Sciences, University of Reading, Reading, UK
Gwyneth R. Matthews
Department of Meteorology, University of Reading, Reading, UK
European Centre for Medium-Range Weather Forecasts, Reading, UK
Florian Pappenberger
European Centre for Medium-Range Weather Forecasts, Reading, UK
Christel Prudhomme
European Centre for Medium-Range Weather Forecasts, Reading, UK
Department of Geography and Environment, Loughborough University, Loughborough, UK
UK Centre for Ecology and Hydrology, Wallingford, UK
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Short summary
In this study, we use three models to forecast river streamflow operationally for 13 months (September 2020 to October 2021) at 10 gauges in the western US. The first model is a state-of-the-art physics-based streamflow model (GloFAS). The second applies a bias-correction technique to GloFAS. The third is a type of neural network (an LSTM). We find that all three are capable of producing skilful forecasts but that the LSTM performs the best, with skilful 5 d forecasts at nine stations.
In this study, we use three models to forecast river streamflow operationally for 13 months...
