Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis

Martel, Jean-Luc; Arsenault, Richard; Turcotte, Richard; Castañeda-Gonzalez, Mariana; Brissette, François; Armstrong, William; Mailhot, Edouard; Pelletier-Dumont, Jasmine; Lachance-Cloutier, Simon; Rondeau-Genesse, Gabriel; Caron, Louis-Philippe

doi:https://doi.org/10.5194/hess-29-4951-2025

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.

https://doi.org/10.5194/hess-29-4951-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 29, issue 19

Research article

|

06 Oct 2025

Research article |

| 06 Oct 2025

Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis

Jean-Luc Martel, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, François Brissette, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Simon Lachance-Cloutier, Gabriel Rondeau-Genesse, and Louis-Philippe Caron

Supplement

https://doi.org/10.5194/hess-29-4951-2025-supplement

Data sets

HYSETS - A 14425 watershed Hydrometeorological Sandbox over North America R. Arsenault, F. Brissette, J. L. Martel, M. Troin, G. Lévesque, J. Davidson-Chaput, M. Castañeda Gonzalez, A. Ameli, and A. Poulin https://doi.org/10.17605/OSF.IO/RPC3W

A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds R. Arsenault et al. https://doi.org/10.1038/s41597-020-00583-2

ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Copernicus Climate Change Service, Climate Data Store 0.24381/cds.adbb2d47

Model code and software

LSTM for FFA - codes and data R. Arsenault, J.-L. Martel, and F. Brissette https://osf.io/zwtnq/

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.