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.Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis
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- Final revised paper (published on 06 Oct 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 20 Aug 2024)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2024-2134', Emilio Graciliano Ferreira Mercuri, 21 Sep 2024
- AC1: 'Reply on RC1', Jean-Luc Martel, 07 Nov 2024
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RC2: 'Comment on egusphere-2024-2134', Andre Ballarin, 26 Dec 2024
- AC2: 'Reply on RC2', Jean-Luc Martel, 24 Jan 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (27 Feb 2025) by Zhongbo Yu

AR by Jean-Luc Martel on behalf of the Authors (11 Apr 2025)
Author's response
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ED: Referee Nomination & Report Request started (22 Apr 2025) by Zhongbo Yu
RR by Andre Ballarin (23 Apr 2025)

ED: Publish subject to minor revisions (review by editor) (17 Jun 2025) by Zhongbo Yu

AR by Jean-Luc Martel on behalf of the Authors (27 Jun 2025)
Author's response
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ED: Publish as is (30 Jun 2025) by Zhongbo Yu

AR by Jean-Luc Martel on behalf of the Authors (30 Jun 2025)
The manuscript entitled "Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis" presents an interesting comparison between a distributed hydrological model (HYDROTEL) and Long Short-Term Memory (LSTM) deep learning models. Below are some points regarding its methodology, results, and potential areas for improvement:
1. LSTM is one class of machine learning algorithms. There are other types being used with good quality of results such as Convolutional Neural Networks (CNNs), Random Forests, or Gradient Boosted Trees. This should be considered in the literature review and/or as a future development.
2. One of the key methods tested, oversampling of extreme peak streamflow events, performed poorly. This suggests a more nuanced approach to data augmentation might be required. Future work could explore advanced synthetic data generation techniques like the Synthetic Minority Over-sampling Technique (SMOTE) rather than simply replicating extreme events. One example is the paper: Wu, Yirui, Yukai Ding, and Jun Feng. "SMOTE-Boost-based sparse Bayesian model for flood prediction." EURASIP Journal on Wireless Communications and Networking 2020 (2020): 1-12.
3. The multihead attention mechanism did not significantly improve the LSTM model’s performance. This raises questions about whether it was fully optimized or if a different attention configuration could be more effective. The complexity added by the attention mechanism might not have been justified, given the size of the dataset. I know that the codes were shared, but some diagram and/or a more complete description of the attention mechanism would be interesting to be added, to help future research in the area.
4. One of the paper's recurring challenges is the inherent scarcity of extreme flood events, which makes it difficult for LSTMs to train effectively. Although the study attempts to mitigate this issue, it highlights that LSTMs struggle with rare event prediction without sufficient data. The paper could benefit from exploring more advanced techniques for handling imbalanced datasets, such as ensemble methods or using generative models to simulate extreme events.
5. Given the results across different test periods, there seems to be a risk of overfitting, particularly in models like LSTM-Combined. The paper could benefit from a more thorough discussion and results presentation on the loss function variation during training and testing epochs.
6. The authors could provide some explanation about the reasons why floods are occurring in Quebec, Canada. Is it increasing the frequency over the years? Are soil or land use reasons for that? Is it related to climate change?
Overall, the paper provides valuable insights into the utility of LSTMs for hydrological modeling, especially in terms of hybrid model approaches.