the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Oct 2025
RTSEvo v1.0: A Retrogressive Thaw Slump Evolution Model
Abstract. Widespread thermal degradation in permafrost regions is accelerating the development of retrogressive thaw slumps (RTS), which threaten ecological stability and infrastructure. Existing RTS modeling studies, however, are largely confined to static susceptibility mapping, lacking the capacity to predict their spatiotemporal evolution. To bridge this gap, we developed a new dynamic RTS evolution model (RTSEvo) that couples three modules: (1) a time-series forecast of regional RTS area, (2) a machine-learning module for pixel-level probability mapping, and (3) a constrained spatial allocation module that simulates RTS expansion by integrating neighborhood effects, stochasticity, and a novel retrogressive erosion factor. Validated using 2021 and 2022 manually interpreted RTS maps of the Beiluhe Basin, the model successfully simulated RTS growth, with the Logistic Regression-based model showing superior stability and accuracy. An interesting finding is that predictive skill is significantly enhanced by integrating process-based rules with statistical probability. The inclusion of a novel retrogressive erosion factor, which mechanistically simulates headwall retreat, proved critical, improving model performance by over 29.3% as measured by the Figure of Merit. The primary innovation of this study is the successful realization of a regional-scale dynamic simulation and prediction of RTS. This model offers a more robust scientific tool for RTS-related risk mitigation strategies.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(2340 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
CEC1: 'Comment on egusphere-2025-5005 - No compliance with the policy of the journal', Juan Antonio Añel, 08 Dec 2025
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AC1: 'Reply on CEC1', Zhuotong Nan, 08 Dec 2025
Dear EiC,
We created a Zenodo repository which corresponds to the github release v1.0, the exact version to generate the results in this manuscript, as soon as we received your comment. The link and doi are as follows,
doi: 10.5281/zenodo.17850642
link: https://doi.org/10.5281/zenodo.17850642 or https://zenodo.org/records/17850642
We will also make modifications to the text in code and data availability:
Code and data availability: The source code for the thaw slump evolution model is publicly available on GitHub (https://github.com/nanzt/RTSEvo) and the exact version used to generate the results presented here is archived on Zenodo (Xu and Nan, 2025a, https://doi.org/10.5281/zenodo.17850642). The inventory data of retrogressive thaw slumps across the Tibetan Plateau from 2016 to 2022 can be accessed at https://doi.org/10.5281/zenodo.10928346 (Xia et al., 2024b). The driving datasets and results for model simulations are available via https://doi.org/10.6084/m9.figshare.30317599 (Xu and Nan, 2025b).
Added references:
Xu, J., and Nan, Z.: nanzt/RTSEvo (v1.0-GMD), Zenodo [code], https://doi.org/10.5281/zenodo.17850642, 2025a.
Xu, J., and Nan, Z.: Datasets associated with “RTSEvo v1.0: A Retrogressive Thaw Slump Evolution Model” submited to Geoscientific Model Development, figshare [dataset], https://doi.org/10.6084/m9.figshare.30317599, 2025b.
Citation: https://doi.org/10.5194/egusphere-2025-5005-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 08 Dec 2025
Dear authors,
Many thanks for addressing this issue so quickly. I have checked the repository and we can consider now the current version of your manuscript in compliance with the code policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2025-5005-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 08 Dec 2025
-
AC1: 'Reply on CEC1', Zhuotong Nan, 08 Dec 2025
-
RC1: 'Comment on egusphere-2025-5005', Anonymous Referee #1, 29 Dec 2025
Gerneral commnets
The manuscript presents "RTSEvo v1.0," a dynamic evolution model for Retrogressive Thaw Slumps (RTS). The authors address a critical gap in current research by advancing from static susceptibility mapping to spatiotemporal simulation. I find the methodological framework to be highly innovative. This hybrid approach offers valuable insights into the morphological evolution of RTS and significantly contributes to our understanding of abrupt thaw processes and permafrost degradation mechanisms on the Qinghai-Tibet Plateau. The study is well-structured and tackles a timely issue in permafrost science. However, I have several concerns regarding the spatial scale of the input data, the transferability of the model, and the standardization of the source code that need to be addressed before publication.
Major comments
1. Scale Mismatch and Resolution Limitations: Retrogressive Thaw Slumps (RTS) are local-scale periglacial landforms. In this study, the selected RTS features have an average area of 2.61 ha, which corresponds to an approximate diameter of 160 m (assuming a square geometry). However, the spatial resolution of several predictor variables used in the model is relatively coarse; for instance, the NDVI data is at 250 m resolution. Even utilizing 30 m resolution datasets may be insufficient for simulating such small-scale geomorphological processes. Consequently, this "scale gap" represents a significant constraint on the model's precision. I strongly recommend that the authors include a serious discussion regarding this limitation in the manuscript.
2: Model Transferability and Generalizability The study constructs the RTSEvo model by combining physical process characterization with machine learning parameter calibration. While the model was validated using over 450 RTS inventory records with positive results, the spatial distribution of these samples is highly concentrated within the Beiluhe Basin. This spatial clustering implies that the climatic conditions, which are key drivers used to predict RTS state and evolution，are highly homogeneous across the training and validation datasets. Therefore, it is questionable whether the parameters calibrated in this specific region are transferable to other permafrost regions with different environmental settings. The authors must strictly clarify the model's transferability and discuss this potential lack of generalizability in the text.
3 Codes: Geoscientific Model Development (GMD) mandates that code be open-source and sufficiently documented to facilitate reuse by the community (and for the purpose of peer review). Upon reviewing the provided repository. I found that the current documentation is non-standard and lacks rigor. Notably, the code contains comments in languages other than English (e.g., Chinese characters are visible in comments. I strongly suggest the authors perform a thorough revision of the code and documentation to ensure it meets international standards and undergoes rigorous testing before final publication.
Specific comments
Figure A2: Regarding the final plot in Figure A2 (Active Layer Thickness): Why does the distribution exhibit a bimodal pattern with values clustered at two extremes (showing a difference of up to 1 m)? Please clarify the physical or data-processing reason for this distinct separation.
Citation: https://doi.org/10.5194/egusphere-2025-5005-RC1 - AC2: 'Reply on RC1', Zhuotong Nan, 27 Feb 2026
-
RC2: 'Comment on egusphere-2025-5005', Anonymous Referee #2, 06 Jan 2026
Xu et al.’s work fills a critical bottleneck in permafrost research by moving from static susceptibility assessment of RTS to dynamic modeling which can be used to predict the future development of RTS. This represents an outstanding contribution to the field. Overall, the proposed framework is well constructed, the modeling strategy is clearly implemented, and the results show reasonable skill in reproducing the observed patterns of RTS development. However, I also have some concerns regarding scale, sampling, and long-term validity require further clarification.
Major comments
- The model operates at a 10 m spatial resolution, while many key driving variables are resampled from much coarse resolutions (e.g., 1km). Please discuss how this resampling affects the precision of the probability estimation and the subsequent spatial allocation module.
- The FoM values appear numerically not very high. I know this framework adapted from LUCC modeling, please provide a comparison indicating if these metrics are consistent with or superior to those reported in related LUCC or other geohazard evolution studies.
- The model assumes that RTS state transitions are irreversible (line 459), which is noted as a limitation for long-term projections. Please clarify the physical justification for maintaining the assumption in v1.0 and discuss if a recovery state could be integrated in future versions. Furthermore, what is the specific temporal horizon (e.g., 10 vs. 50 years) over which the current model remains a reliable predictive tool?
- The use of 1:1 sampling ratio for RTS vs. non-RTS pixels (line 234) doesn't reflect the natural rarity of RTS in the landscape. Please address how this balanced training approach affects the calibration of the final probability maps and if any threshold adjustments were required.
Specific Comments
- Line 62: “guid the simulation” should be “guide the simulation”.
- Line 461: "longer temporal horizons" could be more specific (e.g., decadal to centennial scales).
- Line 433: Provide the full name of "SHAP" upon its first occurrence.
Citation: https://doi.org/10.5194/egusphere-2025-5005-RC2 - AC3: 'Reply on RC2', Zhuotong Nan, 27 Feb 2026
Interactive discussion
Status: closed
-
CEC1: 'Comment on egusphere-2025-5005 - No compliance with the policy of the journal', Juan Antonio Añel, 08 Dec 2025
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou have archived your code on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, the current situation with your manuscript is irregular. Please, publish your code in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Also, you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the information of the new repositories.
Finally, I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2025-5005-CEC1 -
AC1: 'Reply on CEC1', Zhuotong Nan, 08 Dec 2025
Dear EiC,
We created a Zenodo repository which corresponds to the github release v1.0, the exact version to generate the results in this manuscript, as soon as we received your comment. The link and doi are as follows,
doi: 10.5281/zenodo.17850642
link: https://doi.org/10.5281/zenodo.17850642 or https://zenodo.org/records/17850642
We will also make modifications to the text in code and data availability:
Code and data availability: The source code for the thaw slump evolution model is publicly available on GitHub (https://github.com/nanzt/RTSEvo) and the exact version used to generate the results presented here is archived on Zenodo (Xu and Nan, 2025a, https://doi.org/10.5281/zenodo.17850642). The inventory data of retrogressive thaw slumps across the Tibetan Plateau from 2016 to 2022 can be accessed at https://doi.org/10.5281/zenodo.10928346 (Xia et al., 2024b). The driving datasets and results for model simulations are available via https://doi.org/10.6084/m9.figshare.30317599 (Xu and Nan, 2025b).
Added references:
Xu, J., and Nan, Z.: nanzt/RTSEvo (v1.0-GMD), Zenodo [code], https://doi.org/10.5281/zenodo.17850642, 2025a.
Xu, J., and Nan, Z.: Datasets associated with “RTSEvo v1.0: A Retrogressive Thaw Slump Evolution Model” submited to Geoscientific Model Development, figshare [dataset], https://doi.org/10.6084/m9.figshare.30317599, 2025b.
Citation: https://doi.org/10.5194/egusphere-2025-5005-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 08 Dec 2025
Dear authors,
Many thanks for addressing this issue so quickly. I have checked the repository and we can consider now the current version of your manuscript in compliance with the code policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2025-5005-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 08 Dec 2025
-
AC1: 'Reply on CEC1', Zhuotong Nan, 08 Dec 2025
-
RC1: 'Comment on egusphere-2025-5005', Anonymous Referee #1, 29 Dec 2025
Gerneral commnets
The manuscript presents "RTSEvo v1.0," a dynamic evolution model for Retrogressive Thaw Slumps (RTS). The authors address a critical gap in current research by advancing from static susceptibility mapping to spatiotemporal simulation. I find the methodological framework to be highly innovative. This hybrid approach offers valuable insights into the morphological evolution of RTS and significantly contributes to our understanding of abrupt thaw processes and permafrost degradation mechanisms on the Qinghai-Tibet Plateau. The study is well-structured and tackles a timely issue in permafrost science. However, I have several concerns regarding the spatial scale of the input data, the transferability of the model, and the standardization of the source code that need to be addressed before publication.
Major comments
1. Scale Mismatch and Resolution Limitations: Retrogressive Thaw Slumps (RTS) are local-scale periglacial landforms. In this study, the selected RTS features have an average area of 2.61 ha, which corresponds to an approximate diameter of 160 m (assuming a square geometry). However, the spatial resolution of several predictor variables used in the model is relatively coarse; for instance, the NDVI data is at 250 m resolution. Even utilizing 30 m resolution datasets may be insufficient for simulating such small-scale geomorphological processes. Consequently, this "scale gap" represents a significant constraint on the model's precision. I strongly recommend that the authors include a serious discussion regarding this limitation in the manuscript.
2: Model Transferability and Generalizability The study constructs the RTSEvo model by combining physical process characterization with machine learning parameter calibration. While the model was validated using over 450 RTS inventory records with positive results, the spatial distribution of these samples is highly concentrated within the Beiluhe Basin. This spatial clustering implies that the climatic conditions, which are key drivers used to predict RTS state and evolution，are highly homogeneous across the training and validation datasets. Therefore, it is questionable whether the parameters calibrated in this specific region are transferable to other permafrost regions with different environmental settings. The authors must strictly clarify the model's transferability and discuss this potential lack of generalizability in the text.
3 Codes: Geoscientific Model Development (GMD) mandates that code be open-source and sufficiently documented to facilitate reuse by the community (and for the purpose of peer review). Upon reviewing the provided repository. I found that the current documentation is non-standard and lacks rigor. Notably, the code contains comments in languages other than English (e.g., Chinese characters are visible in comments. I strongly suggest the authors perform a thorough revision of the code and documentation to ensure it meets international standards and undergoes rigorous testing before final publication.
Specific comments
Figure A2: Regarding the final plot in Figure A2 (Active Layer Thickness): Why does the distribution exhibit a bimodal pattern with values clustered at two extremes (showing a difference of up to 1 m)? Please clarify the physical or data-processing reason for this distinct separation.
Citation: https://doi.org/10.5194/egusphere-2025-5005-RC1 - AC2: 'Reply on RC1', Zhuotong Nan, 27 Feb 2026
-
RC2: 'Comment on egusphere-2025-5005', Anonymous Referee #2, 06 Jan 2026
Xu et al.’s work fills a critical bottleneck in permafrost research by moving from static susceptibility assessment of RTS to dynamic modeling which can be used to predict the future development of RTS. This represents an outstanding contribution to the field. Overall, the proposed framework is well constructed, the modeling strategy is clearly implemented, and the results show reasonable skill in reproducing the observed patterns of RTS development. However, I also have some concerns regarding scale, sampling, and long-term validity require further clarification.
Major comments
- The model operates at a 10 m spatial resolution, while many key driving variables are resampled from much coarse resolutions (e.g., 1km). Please discuss how this resampling affects the precision of the probability estimation and the subsequent spatial allocation module.
- The FoM values appear numerically not very high. I know this framework adapted from LUCC modeling, please provide a comparison indicating if these metrics are consistent with or superior to those reported in related LUCC or other geohazard evolution studies.
- The model assumes that RTS state transitions are irreversible (line 459), which is noted as a limitation for long-term projections. Please clarify the physical justification for maintaining the assumption in v1.0 and discuss if a recovery state could be integrated in future versions. Furthermore, what is the specific temporal horizon (e.g., 10 vs. 50 years) over which the current model remains a reliable predictive tool?
- The use of 1:1 sampling ratio for RTS vs. non-RTS pixels (line 234) doesn't reflect the natural rarity of RTS in the landscape. Please address how this balanced training approach affects the calibration of the final probability maps and if any threshold adjustments were required.
Specific Comments
- Line 62: “guid the simulation” should be “guide the simulation”.
- Line 461: "longer temporal horizons" could be more specific (e.g., decadal to centennial scales).
- Line 433: Provide the full name of "SHAP" upon its first occurrence.
Citation: https://doi.org/10.5194/egusphere-2025-5005-RC2 - AC3: 'Reply on RC2', Zhuotong Nan, 27 Feb 2026
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Jiwei Xu
Fujun Niu
Yaonan Zhang
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2340 KB) - Metadata XML
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have archived your code on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, the current situation with your manuscript is irregular. Please, publish your code in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Also, you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the information of the new repositories.
Finally, I must note that if you do not fix this problem, we cannot continue with the peer-review process or accept your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor