Articles | Volume 29, issue 13
https://doi.org/10.5194/hess-29-2811-2025
© Author(s) 2025. 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-29-2811-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2025
Research article |
| 04 Jul 2025
| 04 Jul 2025
Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks
Jean-Luc Martel
CORRESPONDING AUTHOR
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
François Brissette
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
Richard Arsenault
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
Richard Turcotte
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Mariana Castañeda-Gonzalez
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
William Armstrong
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
Edouard Mailhot
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Jasmine Pelletier-Dumont
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Gabriel Rondeau-Genesse
Ouranos, Montréal, H3A 1B9, Canada
Louis-Philippe Caron
Ouranos, Montréal, H3A 1B9, Canada
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Short summary
This study compares long short-term memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrological models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
This study compares long short-term memory (LSTM) neural networks with traditional hydrological...
