Application of LiDAR Differentiation and a Modified Savage–Hutter Model to Analyze Co-Seismic Landslides: A Case Study of the 2024 Noto Earthquake, Japan

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

See the Attacted 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Good

 

Author Response

Reviewer: General Comments 
This study innovatively combines LiDAR differencing techniques with a 
modified Savage-Hutter model to analyze the morphological characteristics and 
dynamic behaviors of co-seismic landslides triggered by the 2024 Noto Peninsula 
earthquake. The methodology is novel and holds practical value for hazard risk 
assessment. By integrating static morphological parameters (e.g., landslide 
dimensions) with dynamic granular flow mechanisms, the research reveals critical 
factors controlling the runout distances of landslides on low-angle slopes. The paper 
is well-structured and supported by substantial data, though some methodological 
details and discussion depth require further refinement.

Thank you for this kind introduction. Re-reading the text, numerous sentences were incomplete and needed a bit more "love", so corrections were made throughout the text to improve readability.

 
Reviewer: Literature Context and Scope Limitations 
While the authors claim to "bridge the gap between static analysis and dynamic 
modeling," it is recommended to enhance the introduction by: 
Comparing this methodology with traditional approaches (e.g., Newmark 
analysis, statistical models, or existing landslide simulation methods) to highlight its 
breakthroughs in addressing internal deformation or leveraging high-resolution 
LiDAR data. 

Answer: Indeed, the introductory part is too slim. The paper was first sent to "Drones" for a more technical approach, and it was transferred to Geoscience, for which some more context on landslides is indeed needed. The authors added these sections, one adding more of the relevant information on co-seismic landslide triggering and movement, and one part on the modelling approaches:

(L48 to L52) Co-seismic landslides present complex triggering mechanisms involving both dynamic loading and ground motion cycles [11,12], as the seismic acceleration increases shear stress along the potential sliding surface, while weakening the soil structure [13]. Furthermore, it modifies the traditional depth-dependence of soil shear-strength on slopes, introducing other spatial dependences like the distance to active faults [14]. Finally, there have also been reports that co-seismic landslides can travel longer distances [15] than rainfall landslides.

（L93 - L105）The Newmark displacement model dates back to 1965. It calculates the cumulative permanent displacement of a rigid sliding block. It represents the minimum acceleration required to overcome the static friction forces stopping the slopes from sliding. This method has since been extended for more complex representations of the sliding blocks and adapted to various types of landslides [37, 38, 39]. It became the link between the earlier static approaches like the limit equilibrium methods, and more advanced Finite Element and Finite Difference Methods [40], and Discrete Element Methods like Smooth Particle Models [41]. These methods are, however, well suited for a single landslide, but become very computationally intensive if one wants to apply the method to several landslides. Furthermore, the setup is more time-consuming and complicated than for the two first methods. Finally, probabilistic and GIS-based models (e.g. [42, 43]) are completing this toolbox, but they suffer from the lack of in-depth knowledge of the micro-conditions on each slope, so that significant errors always remain in the results.

Added references

Du, P.; Li, L.; Kopf, A.; Wang, D.; Chen, K.; Shi, H.; Wang, W.; Pan, X.; Hu, G.; Zhang, P. Earthquake-induced Submarine Landslides (EQISLs) and a comparison with their Terrestrial Counterparts: Insights from a New Database. Earth Sci Rev 2025, 104634, https://doi.org/10.1016/j.earscirev.2024.105021

Zhu, S.; Shi, Y.; Lu, M.; Xie, F.R. Dynamic mechanisms of earthquake-triggered landslides. Sci China Earth Sci 2013, 56, 1769-1779, https://doi.org/10.1007/s11430-013-4582-9

Fan, X.; Scaringi, G.; West, A.J.; Westen, C.J.; Tanyas, H.; Hovius, N.; Hales, T.C.; Korup, O.; Zhang, Q.; Evans, S.G.; Xu, C. Rain and small earthquakes maintain a slow-moving landslide in a persistent critical state. Nat Com 2020, 11, 780, https://doi.org/10.1038/s41467-020-14445-3

Meunier, P.; Uchida, T.; Hovius, N. Landslide patterns reveal the sources of large earthquakes. Earth Planet Sci Lett 2013, 363, 27-33, https://doi.org/10.1016/h.epsl.2012.12.018

Grant, R.R.; Culhane, N.K. Global Pattenrs of coseismic landslide runout mobility differ from aseismic landslide trends. Eng Geol 2025, 107824, https://doi.org/10.1016/j.enggeo.2024.107824

Moghaddam, H.; Darbani, M.S.; Sadrara, A.; Hajirasouliha, I. Recommendation of new design spectra for Iran using modified Newmark method. Soil Dyn Earthqu Eng 2024, 108332. https://doi.org/10.1016/j.soidyn.2023.108332

Xuan, G.; Yue, G.; Wang, Y.; Wang, D. An edge-based smoothed finite element method combined with Newmark method for time domain acoustic wave scattering problem. Eng Anal Bound Elem 2024, 158, 182-198, https:doi.org/10.1016/j.enganabound.2023.10.023

Ye, S.; Xue, T.; Wang, W. Multi-stage slope displacement analysis based on real-time dynamic Newmark slider method. Soil Dyn Earthqu Eng 2023, 108209, https://doi.org/10.1016/j.soildyn.2023.108209

Vanneschi, C.; Eyre, M.; Burda, J.; Zizka, L.; Francioni, M.; Cogga, J.S. Investigation of landslide failure mechanisms adjacent to lignite mining operations in North Bohemia (Czech Republic) through a limit equilibrium/finite element modelling approach. Geomorphology 2018, 320, 142-153. https://doi.oorg/10.1016/j.geomorph.2018.08.006

A, H.; Wang, H.; Xu, W.; Shi, H.; Hou, J. Numerical estimation of river blockage and the whole lifecycle of landslide-generated impulse waves in mountain reservoirs using a hybrid DEM-SPH and SWEs method. Eng Geol 2025, 108022, https://doi.org/10.1016/j.enggeo.2025.108022

Ali, Y.; Gugsa, T.H.; Raghuvanshi, T.K. GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia. Env Chall 2024, 100968, https://doi.org/10.1016/J.envc.2024.100968

Yang, S.; Tan, J.; Luo, D.; Wang, Y.; Guo, X.; Zhu, Q.; Ma, C.; Xiong, H. Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis. Comput Geosci 2024, 193, 105723, https://doi.org/10.1016/j.cageo.2024.105723

Reviewer: Acknowledging the limited sample size (13 landslides) and avoiding 
overgeneralization.

Answer: I agree with the reviewers. The authors should have mentioned that it is not an exhaustive statistical analysis, as there are only 13 landslides (although numerous studies also study landslides once at a time). The authors have added in the discussion lines 328 to 330 the following text, acknowledging the limited sample size:

(L328-330) The present research, however, is not without limitations. Indeed, the number of co-seismic landslides analyzed in the study is limited to 13, and further application of the methods are needed to increase the significance of the results.

Reviewer: The conclusions should be cautiously framed as a "case study
level methodological exploration" applicable to small regions with homogeneous 
geological conditions, with recommendations for future validation on larger datasets. 

Answer: Indeed, in the light of the 13 samples present in the study, the authors agree with this comment. The conclusion has been appended re-using the words of the reviewer lines 367- 368:

(L365-368...) In conclusion, this study demonstrates that LiDAR differentiation helped identify two distinct types of landslides: those with clear delimitation between deposit and erosion zones, and those with structures that are more complex. Although this is only an exploratory case study, the simulation further categorized landslides based on specific parameters...

Reviewer: Data Reliability Concerns 
The 4-year interval between pre-event (2020) and post-event (2024) LiDAR 
datasets may introduce errors from non-seismic factors (e.g., vegetation growth, 
erosion). The authors should: 
Validate data consistency by comparing stable areas (e.g., non-landslide regions) 
in pre- and post-event LiDAR data. 
Explain how interference factors (e.g., vegetation) were addressed to ensure the 
extracted deformations are reliable. 

Answer: The LiDAR was processed to extract the ground from the vegetation using a clothing method, in such a way that only the ground point are wrapped, like one would like a piece of cloth falling over an up side down topography. The authors added some explanation about the algorithm so that we know the deformation are reliable:

L178-181: For the present contribution, the authors extracted the ground values by classifying the point-cloud using the clothing algorithm of [39]. This algorithm provides an effective way to retrieve the bare ground by separating the vegetation and anything above ground level. 

Reviewer: Clarification on Methodology 
The term "Factor of Safety" is mentioned as derived from LiDAR data but lacks 
explanation. Since this parameter typically requires geotechnical properties (e.g., soil 
cohesion, friction angle), the authors should clarify whether it was approximated 
using geometric proxies (e.g., critical slope thresholds) or other methods. 

Answer: We can only agree with the reviewer, as this part was forgotten in the description of information retrieval/construction in the 　

L189-193: ”Furthermore, a simplified factor of safety was calculated at landslides’ locations before and after they occurred. Because the earthquake occurred in the midst of winter, the assumption was made that the groundwater did not play a major role, and the landslides were selected within a single geological formation and limited distance, so that the calculation was made from the critical slope thresholds.”

Reviewer: Parameter Justification in the Savage-Hutter Model 
The basis for assigning values to key parameters requires clarification: 
How was the friction angle ϕ determined? Was it inferred from local lithology? 
What justifies the selected range for the internal resistance coefficient μ (0.1–0.5)? If 
parameter sensitivity analysis or calibration was performed, briefly describe the 
process. 

Answer: This is also a very good point, and the authors realized that this was not appropriately explained in the methodology. The friction angle is first defined as 36 degrees, which is a "guessed value" from which an optimization algorithm is searching for the best value based on the concept that the material will start flowing as soon as a threshold angle of friction is reached. The algorithm is thus searching for the smallest phi value using the Nelder-Mead algorithm (implemented using the scipy library). 

The values of μ were tested from 0.1 to 0.9 originally (the higher values being truncated because it did not yield significant result. The choice is based on the following reflection. First, μ has a physical meaning, and this range corresponds to friction angles of approximately 5.7° to 26.6°, which is appropriate for weathered and residual soil encountered in landslides.

Yet, the reader will certainly tell us that fully weathered granite residual soils typically have friction angles in the range of 17° to 40° (μ = 0.31 to 0.84), which is slightly higher at rest. But during motion, the friction coefficient is often lower than static laboratory conditions due to: (a) Increased pore water pressure reducing effective stress; (b) Dynamic effects and material remobilization; (c) Material mixing with water and fine particles. If the values are back-calculated from existing real-life events, the values tend to be on the lower side. This is the rational behind the values choice.

Now, a broader set of values was checked, but values > 0.5 were not giving results that comes close to the observed landslides regardless of how phi was varying. 

For clarity, and without the details provided here for the reviewer, the algorithm was explained and added in the methodology (this was indeed insufficient):

The physical model was parameterized using the Nelder-Mead optimization algorithm, with a starting parameter at 34 degrees, which was the minimum angle of friction for the landslides not to move without seismic acceleration. From this value, angles were tested to reproduce the closest distance travelled by the landslides using the physical model. The termination criteria for the algorithm was set for 100 iterations with a solution expressed as the tangeant of the angle varying by less than 0.001. As the landslide is set in movement, and the shear strength reduced, it was assumed that the friction angle is reduced, therefore the algorithm investigated angles below 34 degrees. This iteration was repeated for a set of mu parameters, tested with sets of values of 0.1, 0.2, 0.3, 0.4 and 0.5, chosen based on trial and error, also using the Nelder-Mead algorithm. These values mark the ability of the flow to deform during flow, and thus comparing the shape of the landslide deposit generated at the same time-step, we can compare whether a landslide had to have the ability to “deform more than another” in order to reach its final position.

6、Language and Terminology 

The paper generally uses appropriate technical terms (e.g., "co-seismic 
landslides," "shallow flow model," "Coulomb friction"). However, the following 
issues need revision: Grammar/typos: Correct errors such as redundant phrasing (e.g., "landslides on 
steep slopes move against those on steep slopes are, are different"，Line 273) and 
improper prepositions (e.g., "regarding of" → "regardless of").

Indeed, those were not making much sense and were rewritten as follows: These landslides coincide with those occurring on lower-angle topographic slopes (< 4.5). It thus demonstrates that the mechanical properties of landslides occurring on steep slopes are different from those on shallow-angle slopes. (now lines 321-324).

 
Overly long sentences: Simplify complex sentences (e.g., in the Introduction) to 
improve readability. Terminology consistency: Avoid mixing Chinese and English terms (e.g., "Factor 
of Safety"). If used, define it upon first mention or replace it with a clearer descriptor 
(e.g., "stability threshold").

A thorough rewriting was performed. The dust needed to settle a bit on the manuscript for us to see its different original flaws. 

Finally, I would like to thank the reviewer for a thorough review, and his/her insightful comments, as the authors had missed reporting some important elements like the optimization algorithm and choice of test values.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

 The paper by Christopher Gomez * and  Danang Sri Hadmoko considers seismic landslides induced by the 2024 earthquake on the Noto peninsula, using the simplified Savage-Hutter model. Based on this assumption, the paper has several shortcomings:
1) Seismo-induced landslides are barely mentioned in the introduction, there is a very large bibliography that could be used to expand the chapter. 
2) More information is needed on the seismic characteristics of the area under study, as well as on the tectonic context in which the earthquake occurred, highlighting, for example, the distance from the epicentre of the landslide phenomena identified.
3) Define the types of landslides according to the Varnes classification. 4) Define the main and secondary effects that have affected the study area 5) Explain in detail the choice of the simplified method used.
6) Extend the discussion and conclusions by taking into account all the indications given. Some suggestions:

Ferrario, M. F. (2019). doi:10.1007/s11069-019-03718-w

Serva, L.,  et al. (2016). doi:10.1007/s00024-015-1177-8

 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

normal

Author Response

The authors thank the reviewer for the thorough reading and suggestions. 

Here are the changes made by the author (please note that the system decided to log me out without us having the time to save all we wrote, so that this is a short hand of it):

 

1) Seismo-induced landslides are barely mentioned in the introduction, there is a very large bibliography that could be used to expand the chapter. 

Indeed, there were certainly a need for more details. The references provided by the reviewer and some key papers, like those by Jibsons, were added at different locations in the introduction to provide a better account and introduction to coseismic landslides.

L34-35:

Co-seismic landslides have long been recognized an issue during earthquake [1], with the seismic intensity controlling the spatial distribution of landslides [2,3]. 

L49-61:

Co-seismic landslides present complex triggering mechanisms involving both dynamic loading and ground motion cycles [11,12], as the seismic acceleration increases shear stress along the potential sliding surface, while weakening the soil structure [13]. Furthermore, it modifies the traditional depth-dependence of soil shear-strength on slopes, introducing other spatial dependences like the distance to active faults [14]. Finally, there have also been reports that co-seismic landslides can travel longer distances [15] than rainfall landslides.

In turn, co-seismic landslides have profound impacts on the environments – EEEs or Earthquake Environmental Effects [16], which have been shown to repeat themselves at the same location over historical times (e.g. in Indonesia [17]), and which are being assessed in their complexity thanks to a new scale: the Environmental Seismic Intensity Scale – or ESI notably introduced by Serva et al. [16], which includes a broader range of information, including human experiences of earthquakes.

L101 - 117:

The Newmark displacement model dates back to 1965. It calculates the cumulative permanent displacement of a rigid sliding block. It represents the minimum acceleration required to overcome the static friction forces that stop the slopes from sliding. This method has since been extended for more complex representations of the sliding blocks and adapted to various types of landslides [37, 38, 39]. It became the link between the earlier static approaches like the limit equilibrium methods, and more advanced Finite Element and Finite Difference Methods [40], and Discrete Element Methods like Smooth Particle Models [41]. These methods are, however, well suited for a single landslide, but become very computationally intensive if one wants to apply the method to several landslides. Furthermore, the setup is more time-consuming and complicated than for the two first methods. Finally, probabilistic and GIS-based models (e.g. [42, 43]) are completing this toolbox, but they suffer from the lack of in-depth knowledge of the micro-conditions on each slope, so that significant errors always remain in the results. Furthermore, the complexity of relating landslides to earthquake characteristics has proven to not be a straightforward matter, as similar magnitude earthquakes within a single region have been shown to have very different effects (e.g. the Ms 6.6 Jinggu earthquake triggered 441 landslides, while the Ms 6.5 Ludian earthquake caused 10,559 landslides [44]).

2) More information is needed on the seismic characteristics of the area under study, as well as on the tectonic context in which the earthquake occurred, highlighting, for example, the distance from the epicentre of the landslide phenomena identified.

This section was modified and data on the distance to the epicentre was also added:

L124-138:

To reach this objective, the present research has investigated a set of shallow landslides (translational debris and earth slides) in a topographically and geologically homogeneous of the Noto Peninsula, Japan (Fig. 1), following the 2024/1/1 Noto Peninsula Earthquake. The earthquake interrupted the celebration of New Year’s day with a Mw 7.6 event at 16:10 local time that lasted for about 40 seconds, with the main-shock being triggered by a NE-SW trending thrust-fault [32]. The event has been shown to be the tail of an earthquake swarm that started in November 2020 [33], which ended by an unexpectedly strong event, or ‘dragon king’ event [34], and also followed a Mw 6.3 earthquake in 2023, the Oku-Noto Earthquake [35]. The epicentre of this earthquake was located to the North near the tip of the peninsula (Figure 1) and 11 km from the research location. The peninsula is characterized by a series of low-rising mountains mostly between 300 m and 500 m, plateaux at 80 m a.s.l. which, like the mountain ranges are deeply incised, with occasional wider valleys associated with fault-lines [36].

3) Define the types of landslides according to the Varnes classification.

L126, debris and earth slides was added defining the landslides from the Varnes classification.

4) Define the main and secondary effects (according to the ESI scale 2007, Michetti et al., 2007, Serva et al., 2007) that have affected the study area

Although this is a very interesting topic, the authors think that it is a set of elements that require a paper on its own, and may be seen as future research.

5) Explain in detail the choice of the simplified method used.

This is also a very good point, the methodological implementation of the statistical part that control the physical model was not explained. This paragraph was added to the methodology:

L239-251:

The physical model was parameterized using the Nelder-Mead optimization algorithm, with a starting parameter at 34 degrees, which was the minimum angle of friction for the landslides not to move without seismic acceleration. From this value, angles were tested to reproduce the closest distance travelled by the landslides using the physical model. The termination criteria for the algorithm was set for 100 iterations with a solution expressed as the tangeant of the angle varying by less than 0.001. As the landslide is set in movement, and the shear strength reduced, it was assumed that the friction angle is reduced, therefore the algorithm investigated angles below 34 degrees. This iteration was repeated for a set of mu parameters, tested with sets of values of 0.1, 0.2, 0.3, 0.4 and 0.5, chosen based on trial and error, also using the Nelder-Mead algorithm. These values mark the ability of the flow to deform during flow, and thus comparing the shape of the landslide deposit generated at the same time-step, we can compare whether a landslide had to have the ability to “deform more than another” in order to reach its final position.

6) Extend the discussion and conclusions by taking into account all the indications given. Some suggestions:

The discussion and conclusions were modified according to the different reviewers comments. Please see the paper for details, although based on 4, comparisons with EEE and ESI, were not added to the paper, as the authors do not believe that it is the focal point of the present contribution.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors,

Your data are quite interesting because you have LiDAR data before and after a strong earthquake. What was expected is a very good work. However, in much of written text there are serious problems. Before the paper be published please rewrite it with the help of an English native speaker. My judgment is major revision and resubmission. 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Written English is the major problem of this contribution. A thorough English editing is appropriate.

Author Response

To the reviewer:

Thank you for the thorough review. Extensive rewriting and extra information were added and this can be checked on the document with the material in different color. Please find answers to the points you raised in the pdf:

1. The document suggested to be added to the literature was added:

1. Koukouvelas, I.K.; Piper, D.J.W.; Katsonopoulou, D.; Kontopoulos, N.; Verroios, S.: Nikolakopous, K.; Zygouri, V. Earthquake-triggered landslides and mudflows: Was this the wave that engulfed Ancient Helike. Holocene 2020, 30 https://doi.org/10.1177/0959683620950389

2. L 88, the authors introduced another set of papers that state that the same "kind" of earthquake in almost the same location can generate different results (400 against 10,000 landslides), we use this as a springboard to further the suggestion that local soil characteristics may play an important role, and that we are now testing this hypothesis:

Furthermore, the complexity of relating landslides to earthquake characteristics has proven to not be a straightforward matter, as similar magnitude earthquakes within a single region have been shown to have very different effects (e.g. the Ms 6.6 Jinggu earthquake triggered 441 landslides, while the Ms 6.5 Ludian earthquake caused 10,559 landslides [44]).

Because scientists have proposed that local variability may thus be more of a control on landslides movement, the present work aims to contribute to this research gap by researching landslides’ parameters relevant to the in-motion mechanic using a dynamic model modified from the Savage-Hutter model calibrated against pre- and post-earthquake LiDAR topography, within a a small area to identify the role of local parameters in a seemingly homogeneous topography, geology and distance from the epicenter. (L118-123)

3. The reviewer suggested that "To reach this objective, the present research has investigated a set of shallow landslides (translational debris and earth slides) in a topographically and geologically homogeneous of the Noto Peninsula, Japan (Fig. 1), following the 2024/1/1 Noto Peninsula Earthquake. " be moved from the beginning of the field study to the end of the introduction. 

The authors have moved the text to the end of the introduction, instead of it being in the research location.

3. The reviewer helped modified two sentences at the beginning of materials and methods (which needed rewriting) and according to their request the other "hard to read" sentences were rephrased:

Arguably, one of the main difficulties when working with the soil parameters relevant to a co-seismic landslide is that pre-earthquake and post-earthquake soil conditions are not the same , either just after the earthquake or during the material flow. Indeed, as the P-waves are first sending compression and decompression waves that modify the soil structure (i.e. internal friction angle and moisture ratio, etc.), the conditions measured pre-earthquake are not the ones landslide researchers are interested in. Secondly, as liquefaction occurs during the flow, and because direct observations and measures are difficult to obtain, there is a need to integrate the dynamic components occurring on a slope to compare landslides.

In the present contribution, the methodology is twofolds. First, the authors analyzed the static slopes from LiDAR differentiation, and then using the geometry obtained from the LiDAR data, the authors ran a granular flow model to try identifying the variability of parameters necessary to reproduce the observed landslides. 

4. L201, the authors had mentioned a ratio without precising a ratio of what over what, and this has now been addressed:

[...] their ratio (h/L), [...]

5. The wet/dry problem was rephrased as:

The transition between wet and dry land during computation [...]

6. The result first paragraph was fully rewritten:

The 13 landslides of the survey area range in altitude between 249 m a.s.l. and 101 m a.s.l. at the landslides’ crown. Downstream, the landslides’ toes are ranging from 102 m a.s.l. to 22 m a.s.l. This variability reflects the position of the landslides in the watershed, as landslides 1,2,3,5 and 13 are on the upper slopes and the others extend down to the valley.

7. LiDAR DEM differentiation with DEM added as it is indeed more accurate.

8. The term pseudo was removed as it is the FoS explained in the introduction.

9. The result section that was vague and suggested to be rewritten as a table was presented as a table. (please see the new document)

10. Line 264, now 313: the formulation was changed to:  landslide 1 did not slide out of its original position, and mass transfer is not visible despite surface deformation ...

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The suggestions have been taken on board by the authors so that the work can be published in its current form. It is only suggested that Japan be added to the key notes.

Reviewer 3 Report

Comments and Suggestions for Authors

No more comments, the new version is improved and ready for publication.