Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4951-2025
© 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-29-4951-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2025
Research article |
| 06 Oct 2025
| 06 Oct 2025
Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis
Jean-Luc Martel
CORRESPONDING AUTHOR
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
François Brissette
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
Simon Lachance-Cloutier
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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Cited
11 citations as recorded by crossref.
- A Hybrid STL - GEV and RNNs Models Approach for Monthly Extreme Discharge Forecasting in the Mono Basin S. Souvenir et al. https://doi.org/10.11648/j.wros.20261503.12
- Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
- A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
- PatchX: A dual-branch embedding framework with cross-attention for streamflow forecasting J. Jiang et al. https://doi.org/10.1016/j.envsoft.2026.107176
- A New Multiscale Deep Learning Model for Daily Runoff Prediction in Snow-Influenced Alpine Catchments P. Luo et al. https://doi.org/10.3390/rs18193309
- PINN-Sed: Interconnected Physics-Informed Machine Learning Model for High Resolution Suspended Sediment Transport in River Network A. Haddadchi et al. https://doi.org/10.1061/JHEND8.HYENG-14926
- Evaluating best machine learning model for discharge prediction under dam-regulated river using a novel approach of hydro feature engineering P. Dubey et al. https://doi.org/10.1016/j.watres.2026.126564
- Streamflow Estimation at Ungauged Sections in a Nested Basin Using an HEG-LSTM Model Y. He et al. https://doi.org/10.3390/w18172145
- A differentiable Xin’anjiang model incorporating transfer learning for cross-basin flood prediction under data scarcity Y. Chai et al. https://doi.org/10.1016/j.jhydrol.2026.136321
- Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer https://doi.org/10.5194/hess-30-1877-2026
- The ability of LSTM to model snowmelt versus rainfall generated floods S. Bakke et al. https://doi.org/10.5194/hess-30-6207-2026
11 citations as recorded by crossref.
- A Hybrid STL - GEV and RNNs Models Approach for Monthly Extreme Discharge Forecasting in the Mono Basin S. Souvenir et al. https://doi.org/10.11648/j.wros.20261503.12
- Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
- A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
- PatchX: A dual-branch embedding framework with cross-attention for streamflow forecasting J. Jiang et al. https://doi.org/10.1016/j.envsoft.2026.107176
- A New Multiscale Deep Learning Model for Daily Runoff Prediction in Snow-Influenced Alpine Catchments P. Luo et al. https://doi.org/10.3390/rs18193309
- PINN-Sed: Interconnected Physics-Informed Machine Learning Model for High Resolution Suspended Sediment Transport in River Network A. Haddadchi et al. https://doi.org/10.1061/JHEND8.HYENG-14926
- Evaluating best machine learning model for discharge prediction under dam-regulated river using a novel approach of hydro feature engineering P. Dubey et al. https://doi.org/10.1016/j.watres.2026.126564
- Streamflow Estimation at Ungauged Sections in a Nested Basin Using an HEG-LSTM Model Y. He et al. https://doi.org/10.3390/w18172145
- A differentiable Xin’anjiang model incorporating transfer learning for cross-basin flood prediction under data scarcity Y. Chai et al. https://doi.org/10.1016/j.jhydrol.2026.136321
- Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer https://doi.org/10.5194/hess-30-1877-2026
- The ability of LSTM to model snowmelt versus rainfall generated floods S. Bakke et al. https://doi.org/10.5194/hess-30-6207-2026
Saved (final revised paper)
Latest update: 17 Oct 2026
Short summary
This study explores six methods to improve the ability of long short-term memory (LSTM) neural networks to predict peak streamflows, crucial for flood analysis. By enhancing data inputs and model techniques, the research shows that LSTM models can match or surpass traditional hydrological models in simulating peak flows. Tested on 88 catchments in Quebec, Canada, these methods offer promising strategies for better flood prediction.
This study explores six methods to improve the ability of long short-term memory (LSTM) neural...
