Articles | Volume 29, issue 13
https://doi.org/10.5194/hess-29-2811-2025
https://doi.org/10.5194/hess-29-2811-2025
Research article
 | 
04 Jul 2025
Research article |  | 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, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron

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Cited articles

Agarap, A. F.: Deep learning using rectified linear units (relu), arXiv [preprint], https://doi.org/10.48550/arXiv.1803.08375, 2018. 
Althoff, D. and Rodrigues, L. N.: Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment, J. Hydrol., 600, 126674, https://doi.org/10.1016/j.jhydrol.2021.126674, 2021. 
Althoff, D., Rodrigues, L. N., and Silva, D. D.: Addressing hydrological modeling in watersheds under land cover change with deep learning, Adv. Water Resour., 154, 103965, https://doi.org/10.1016/j.advwatres.2021.103965, 2021. 
Arsenault, R. and Brissette, F.: Determining the optimal spatial distribution of weather station networks for hydrological modeling purposes using RCM datasets: An experimental approach, J. Hydrometeorol., 15, 517–526, https://doi.org/10.1175/jhm-d-13-088.1, 2014a. 
Arsenault, R. and Brissette, F. P.: Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches, Water Resour. Res., 50, 6135–6153, https://doi.org/10.1002/2013WR014898, 2014b. 
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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.
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