Development of Potential Slip Surface Identification Model for Active Deep-Seated Landslide Sites: A Case Study in Taiwan

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.     The introduction section of the article is too lengthy and could be made more concise.

2.     A technical roadmap could be added to the methodology section to help readers quickly understand the experimental steps.

3.     Some sentences in the article are too long and could be shortened.

4.     This study builds a model based on hydrogeological data from 24 landslide sites in Taiwan. While these data are representative, whether they can be generalized to other geological environments still requires further discussion and validation. It is recommended to expand future testing to landslide cases in different regions to ensure the model's broad applicability.

5.     Several charts are referenced in the article, but the explanation for certain figures (e.g., Figures 6 and 7) is somewhat brief. It is suggested to add more discussion in the chart descriptions, especially regarding data trends and practical significance, to help readers better understand the relationship between predicted sliding depth and hydraulic conductivity in the model.

Author Response

Please see the attached file.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

 

The manuscript deals with the study on the prediction of potential slip surfaces in landslide-prone areas in Taiwan. The research has practical implications for disaster prevention and management. The methodology involving principal component analysis (PCA) and regression analysis to develop the Hydraulic Conductivity Potential Index (HCPI) is commendable. The topic fits with the scope of Geoscience, and it could be interesting for part of its readership.

 

Detail comments:

1.     About Research Design: The research design is well-structured, and the use of PCA to quantify the hydraulic properties of disturbed rock formations is appropriate. However, the manuscript will be better if authors discuss on the selection criteria for the six geological factors (RQD, DI, GCD, LPI, FA, and FD), from line 375 to line 380, and their representativeness in different geological settings.

 

2.     Data Analysis: The statistical methods used are suitable for the research questions. The authors should ensure that the assumptions of the statistical tests are met and provide details on data normality, homoscedasticity, and multicollinearity checks. Additional information on the robustness of the model with respect to these assumptions would be valuable.

 

3.     Validity of Results: line 561 to line 585, The results indicate a strong correlation between HCPI and hydraulic conductivity, which supports the model's effectiveness. The authors could consider the further validation measures, such as cross-validation or testing on an independent dataset, to strengthen the reliability of the model.

 

4.     Depth of Discussion: The discussion is logically structured and interprets the results well. It would be enhanced by a more thorough exploration of the model's limitations and its applicability to different geological conditions and landslide types. I suggest that author should discuss in detail the possible limitations of the model at the end of the manuscript, including its applicability under different geological conditions.

 

5.     Other details in the manuscript:

 

5.1   Line 721 to line 760, The conclusion section should summarize the key findings and suggest directions for future research, including further validation and refinement of the model. I suggest that author should simplify this part.

5.2   Fig. 3 and Fig. 4. The font of the horizontal and vertical axes is inconsistent. Actually, figures and illustrations are crucial for understanding the research outcomes. The authors should ensure that all figures and tables are clear, accurately represent the data, and are consistent with the text. Please check all the figures in your manuscript. And, fig.2 and fig. 7, authors should ensure that all figures and illustrations are clear, accurately represent the data.

5.3   Although the article addresses multicollinearity, the author could discuss more details on statistical data such as tolerance and Variance Inflation Factor (VIF) values, which are crucial for assessing the robustness of the model.

Comments on the Quality of English Language

Minor editing of English language required

Author Response

Please see the attached file.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper proposes a multiple regression equation for predicting the hydraulic conductivity at specific depths based on bedrock conditions obtained from in situ hydrogeological surveys in landslide-prone sites in Taiwan. The authors present a novel analytical method for identifying potential slip surface depths by analyzing changes in permeability coefficients in conjunction with geological (stratigraphic) characteristics. This method shows promise for application in deep-seated landslide hazard risk assessments, as it can help predict potential slip surfaces before a landslide occurs.

However, some sections require revision. Please refer to the attached report for detailed suggestions.

Comments for author File: Comments.pdf

Author Response

Please see the attached file.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The title is well structured but a bit generic, leading to the lack of some details, such as the reference to the "hydraulic conductivity-based model" and the "insights from case studies," which would further enhance the importance of the results obtained.

The abstract reflects what the paper discusses. It provides a concise overview of the key topics covered. In particular, the abstract effectively captures the main themes and scope of the paper without delving into specifics that are not addressed in the body of the work.

The interpretations and conclusions presented in the paper are partly justified by the data, but have some shortcomings. Although the study provides a solid analysis of the hydraulic conductivity profiles and proposes a new model (HCPI) for the identification of the slip surfaces, the lack of a detailed quantitative comparison with other existing methods and the absence of an error propagation analysis leave room for doubts on the robustness of the conclusions. Furthermore, the data analysis could be improved with a greater attention to the variability and uncertainty of the input parameters.

In the paper, the images have specific limitations that reduce their effectiveness in communicating the results of the study. Problems include scales and legends, which are not always complete or well defined; this limits the understanding of the meaning of the data represented and complicates the comparison between different landslide sites. The addition of detailed annotations and more explanatory captions would help readers to better connect the data to the analyses and conclusions discussed in the text.

Even if the paper seems quite interesting it need more detailed quantitative comparison and a greater attention to the variability and uncertainty of the input parameters, it could capture further aspects that would further enhance the results and conclusions. In particular:

-          The paper lacks a detailed quantitative comparison between the proposed model (HCPI) and existing methods for the assessment of slip surfaces, such as traditional limit equilibrium methods (LEM) or numerical models (e.g., FEM, FDM). Although the HCPI model seems to offer a new approach to landslide assessment, clear metrics are not presented to demonstrate its performance compared to established approaches. It would be necessary to include a comparison based on quantitative parameters, such as the accuracy of slip depth predictions, computation times, sensitivity to input data variations and reliability of results in different geological contexts.

-          The paper does not address an adequate sensitivity analysis, a fundamental element to evaluate the impact of individual geological parameters on the output of the HCPI model. In a complex context such as landslide modeling, input parameters, such as RQD, FD and LPI, can differently influence the final result of the model. Without a sensitivity analysis, it is not possible to accurately determine which variables have a greater weight on the HCPI calculation and, consequently, which parameters need to be measured more accurately in the field.

-          The paper does not thoroughly evaluate the uncertainty associated with the geological parameters used in the HCPI model, an aspect that is considered fundamental for a correct application and interpretation of the results. Parameters such as Rock Quality Designation (RQD) and Fracture Density (FD) are subject to inevitable variations due to both the heterogeneous nature of the geological materials and the measurement techniques adopted. The lack of an error propagation analysis in the presented model represents a significant gap, since it does not allow to quantify the impact that these uncertainties could have on the variability of the calculated HCPI index.

Author Response

Please see the attached file.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The author has made some revisions to the manuscript in response to the commments, but some of the figures  should be revised to meet the requirement of the journal. Such as Figure 3, Figure 4. In addition, the font size in figure7 and figure 8 should also be consistent.

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Please see the attached file.

Author Response File: Author Response.pdf