Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5449–5472, 2022
https://doi.org/10.5194/hess-26-5449-2022
Hydrol. Earth Syst. Sci., 26, 5449–5472, 2022
https://doi.org/10.5194/hess-26-5449-2022
Research article
01 Nov 2022
Research article | 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 et al.

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Cited articles

Adnan, R. M., Zounemat-Kermani, M., Kuriqi, A., and Kisi, O.: Machine learning method in prediction streamflow considering periodicity component, in: Intelligent data analytics for decision-support systems in hazard mitigation, Springer, 383–403, https://doi.org/10.1007/978-981-15-5772-9_18, 2021. a
Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute global relief model: procedures, data sources and analysis, Technical Memorandum NESDIS NGDC-24, NOAA, https://doi.org/10.7289/V5C8276M, 2009. a
Bennett, A. and Nijssen, B.: Deep learned process parameterizations provide better representations of turbulent heat fluxes in hydrologic models, Water Resour. Res., 57, e2020WR029328, https://doi.org/10.1029/2020WR029328, 2021. a
Beven, K. J.: Rainfall-runoff modelling: the primer, John Wiley & Sons, ISBN 978-0-470-71459-1, 2011. a
Booker, D. and Woods, R.: Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments, J. Hydrol., 508, 227–239, 2014. a
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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.