Articles | Volume 28, issue 17
https://doi.org/10.5194/hess-28-4187-2024
Special issue:
https://doi.org/10.5194/hess-28-4187-2024
Opinion article
 | 
12 Sep 2024
Opinion article |  | 12 Sep 2024

HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin

Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing

Related authors

Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025,https://doi.org/10.5194/hess-29-1749-2025, 2025
Short summary
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025,https://doi.org/10.5194/hess-29-1277-2025, 2025
Short summary
GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre
Claudia Färber, Henning Plessow, Simon Mischel, Frederik Kratzert, Nans Addor, Guy Shalev, and Ulrich Looser
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-427,https://doi.org/10.5194/essd-2024-427, 2024
Preprint under review for ESSD
Short summary
A data-centric perspective on the information needed for hydrological uncertainty predictions
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024,https://doi.org/10.5194/hess-28-4099-2024, 2024
Short summary
Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions
Daniel Klotz, Martin Gauch, Frederik Kratzert, Grey Nearing, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3665–3673, https://doi.org/10.5194/hess-28-3665-2024,https://doi.org/10.5194/hess-28-3665-2024, 2024
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025,https://doi.org/10.5194/hess-29-1703-2025, 2025
Short summary
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025,https://doi.org/10.5194/hess-29-1725-2025, 2025
Short summary
Extended-range forecasting of stream water temperature with deep-learning models
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025,https://doi.org/10.5194/hess-29-1685-2025, 2025
Short summary
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025,https://doi.org/10.5194/hess-29-1749-2025, 2025
Short summary
Projections of streamflow intermittence under climate change in European drying river networks
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025,https://doi.org/10.5194/hess-29-1615-2025, 2025
Short summary

Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a, b, c, d
BAFG: The Global Runoff Data Centre, 56068 Koblenz, Germany, https://www.bafg.de/GRDC (last access: 24 July 2024), 2024. a
Beck, H. E., van Dijk, A. I., De Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of hydrologic model parameters, Water Resour. Res., 52, 3599–3622, 2016. a, b
Frame, J. M., Kratzert, F., Raney, A., Rahman, M., Salas, F. R., and Nearing, G. S.: Post-processing the national water model with long short-term memory networks for streamflow predictions and model diagnostics, J. Am. Water Resour. Assoc., 57, 885–905, 2021. a
Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall–runoff predictions of extreme events, Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, 2022. a, b, c
Download
Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Share
Special issue