Estimating Reactivation Times and Velocities of Slow-Moving Landslides via PS-InSAR and Their Relationship with Precipitation in Central Italy

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

General Comments:
I am not a fan of acronyms when not needed. Can the authors reduce them? e.g. S1 is sentinel 1 up to line 153 where it becomes a sub-segment.. perhaps it can remain Sentinel 1 throughout the text. TP(s) is turning point(s) but there is no need to keep it short (in some parts of the manuscript you call it TP while somewhere else you call it turning points)..in general: please reduce acronyms. When used in figures acronyms can be clarified in the caption. An example is given in figure 7, what are the meanings of DIR and NDRI? Check also other figures.

I wonder if covering a vast area such the one modeled in this work, with data from only four weather stations surrounding the core of the study area has effects on the reliability of the discussed correlations. Perhaps the authors can discuss a little about possible effects of more weather stations on their estimates. Alternatively they can add more wheater stations (if available).

Are the AOI polygons the portions of the four administrative regions delimited by black dashed and continuous lines in fig. 1B? If so, the ratio between the area of each AOI polygon and the number of weather stations covering the AOI is not the same across the four regions (Marche has a wider polygon covered by one station if compared to the others). Do this disproportion carry effects on the reliability of your estimates? If so, I guess these are more evident on periferal areas of AOI, the more distant from the weather station. Please briefly discuss this in chapter 4.2

From figure 8 it looks that each long-lasting (roughly I would say > 3 months) slope change of cumulated precipitations is matched by a similar change of slope of TP trend (red lines). I wonder if authors can address this feature with a quantitative analysis (see comment in line 221-235).

Minor comments:
INTRO:
Line 46-51: perhaps here the authors can shortly and simply define what a turning point is and what it represents for the evolution of a landslide, I feel this would be of help for the reader not familiar with the method or the terminology.
Line 55: Perhaps authors can mention other In-SAR Applications to this seismic sequence: e.g:

Carboni, F., Porreca, M., Valerio, E. et al. Surface ruptures and off-fault deformation of the October 2016 central Italy earthquakes from DInSAR data. Sci Rep 12, 3172 (2022). https://doi.org/10.1038/s41598-022-07068-9

Brozzetti F, Mondini AC, Pauselli C, Mancinelli P, Cirillo D, Guzzetti F, Lavecchia G. Mainshock Anticipated by Intra-Sequence Ground Deformations: Insights from Multiscale Field and SAR Interferometric Measurements. Geosciences. 2020; 10(5):186. https://doi.org/10.3390/geosciences10050186

DATA AND METHODS
line 89: that were struck
Lie 93: "The present research contributes to this framework" please rephrase, maybe: "The present research investigates the morphoevolution..."
Line 105-107: Maybe some references to catalogues and/or papers are useful to the reader not familiar with the area
Line 115: were these 255 landslides chosen by the authors? were taken from a catalogue? do these represent all the mapped landslides on study area? Please clarify why this choice and from where you took the data.
Caption of figure 1: "calcareous admarly.." perhaps is marly?
Lines 132-141: I like these bullet points on S1 and CSK satellites, please include some references about the data you mention here
Line 153: ..two pieces of what? I guess of the segment.. please clarify
Line 169: remove double the
line 183: please remove superiority, replace it with advantages

General about chapter 2.3: Why is the STPD revisited here? has it been modified from the original? From what is included between lines 153 and 188 I cannot understand if you are revisiting a published method with some tuning or are applying the original method.. please clarify the eventual differences or remove revisited..

RESULTS
Line 209: Criteria? Where are described such criteria? do you refer to lines 178-181? please clarify or recall the chapter in which you presented and discussed such criteria

Figure 6: what blue and red columns represent?
Line 215: it is unclear where these polygons are located in relation to weather stations and how big these are (square km?). Can you show them on maps with dedicate colours? I do not understand if in figure 1b these polygons are the large areas delimited by black dashed and continuous lines or what else. If this is the case, I would not say that the four AOI polygons are small (caption of figure 1) nor they are near the local weather stations (line 215).. See general point N 3.

Line 221-235: Rather than the qualitative description of figure 8 like the one proposed in these lines, can the author quantitatively relate the change of gradient of cumulated precipitation trend with the change of sign and intensity (mm/year) of the turning point? In other words: is there a relation (linear?) of some sort between the cumulated precipitation temporal trend and red lines in figure 8? If so, is this the same across all the AOI - i.e. across the entire study area?

DISCUSSION
Line 247: are all the "many researchers" included in the only paper you mention or there are other papers to mention? Either rephrase or include other references.
Line 250: "... and the limitations of this study"
Line 267-269: please rephrase to clarify what you mean
Line 272: please change spells (periods, intervals, seasons...)
Line 295-297: please add some references
Line 319: please clarify what QGIS is (an open source GIS software) and provide a reference.

Comments on the Quality of English Language

Grammar looks mostly OK to me but in some cases the sentence can be re-arranged or shortened to improve readability and clarity.

Author Response

Authors’ Responses to Reviewer 1

Dear reviewer,

We would like to thank you very much for your time and positive feedback and insightful comments that helped us improve the presentation of our manuscript further. Please see below our responses where the changes are highlighted in the revised version.

General Comments:

I am not a fan of acronyms when not needed. Can the authors reduce them? e.g. S1 is sentinel 1 up to line 153 where it becomes a sub-segment.. perhaps it can remain Sentinel 1 throughout the text. TP(s) is turning point(s) but there is no need to keep it short (in some parts of the manuscript you call it TP while somewhere else you call it turning points)..in general: please reduce acronyms. When used in figures acronyms can be clarified in the caption. An example is given in figure 7, what are the meanings of DIR and NDRI? Check also other figures.

Authors’ Response: Agreed. We have replaced TP with turning point, and S1 with Sentinel-1. We have also removed some of other acronyms and defined the rest the first time they appeared and in the caption.

I wonder if covering a vast area such the one modeled in this work, with data from only four weather stations surrounding the core of the study area has effects on the reliability of the discussed correlations. Perhaps the authors can discuss a little about possible effects of more weather stations on their estimates. Alternatively they can add more wheater stations (if available).

Authors’ Response: Thank you for your insightful comment. In Introduction we added:

“Unfortunately, there is a limited number of weather stations in Central Apennines to conduct a rigorous correlation analysis between precipitation and displacement in time and space. Fortunately, the satellite-based global precipitation measurement (GPM) images provide a good approximation for precipitation measurements, although they have a low spatial resolution. The GPM images are employed by many researchers for climate studies and environmental monitoring.”

We added the description of GPM in the dataset section. We added a new Section 3.3. The STPD results of GPM time series, and we added new Figures 9,10,11,12 and also added some dedicated paragraphs in the Discussion section.

Are the AOI polygons the portions of the four administrative regions delimited by black dashed and continuous lines in fig. 1B? If so, the ratio between the area of each AOI polygon and the number of weather stations covering the AOI is not the same across the four regions (Marche has a wider polygon covered by one station if compared to the others). Do this disproportion carry effects on the reliability of your estimates? If so, I guess these are more evident on periferal areas of AOI, the more distant from the weather station. Please briefly discuss this in chapter 4.2

Authors’ Response: Thank you for your insightful comment. Figure 1(b) and its legend are updated. Each pair of ascending and descending time series is shown by a blue star, and the weather stations are shown by red squares in Figure 1(b). The reliability and uncertainty analysis are discussed in the new Section 3.3 and Discussion Sections 4.1 and 4.2.

 

From figure 8 it looks that each long-lasting (roughly I would say > 3 months) slope change of cumulated precipitations is matched by a similar change of slope of TP trend (red lines). I wonder if authors can address this feature with a quantitative analysis (see comment in line 221-235).

Authors’ Response: Thank you for your insightful comment. Through processing monthly GPM time series at spatial resolution of 10 km from 2017 to 2023, we tried to address your comment in the new Section 3.3.

Minor comments:

INTRO:

Line 46-51: perhaps here the authors can shortly and simply define what a turning point is and what it represents for the evolution of a landslide, I feel this would be of help for the reader not familiar with the method or the terminology.

Authors’ Response: Thank you for your insightful comment. We added:

“A turning point in a time series corresponds to a time when the gradient changes. In slow-moving landslide studies, the velocity of ground motion is generally considered constant over a long period until it changes due to potential triggers, such as changes in precipitation patterns.”

Line 55: Perhaps authors can mention other In-SAR Applications to this seismic sequence: e.g.:

Carboni, F., Porreca, M., Valerio, E. et al. Surface ruptures and off-fault deformation of the October 2016 central Italy earthquakes from DInSAR data. Sci Rep 12, 3172 (2022). https://doi.org/10.1038/s41598-022-07068-9

Brozzetti F, Mondini AC, Pauselli C, Mancinelli P, Cirillo D, Guzzetti F, Lavecchia G. Mainshock Anticipated by Intra-Sequence Ground Deformations: Insights from Multiscale Field and SAR Interferometric Measurements. Geosciences. 2020; 10(5):186. https://doi.org/10.3390/geosciences10050186

Authors’ Response: Thank you for your insightful comment. We reviewed your suggested references and found them very relevant and included them in the Introduction for In-SAR applications as well as a few more references.

DATA AND METHODS

line 89: that were struck

Authors’ Response: Done! Thank you!

Line 93: "The present research contributes to this framework" please rephrase, maybe: "The present research investigates the morphoevolution..."

Authors’ Response: Done! Thank you!

Line 105-107: Maybe some references to catalogues and/or papers are useful to the reader not familiar with the area.

Authors’ Response: We added two new relevant references. Thank you!

Line 115: were these 255 landslides chosen by the authors? were taken from a catalogue? do these represent all the mapped landslides on study area? Please clarify why this choice and from where you took the data.

Authors’ Response: Thank you for your insightful comment. In section 2.1, we added:

“The boundary of the study region is defined by Autorità di Bacino Distrettuale dell’Appennino Centrale (ABDAC), shown in Figure 1(a). The current research carefully examines a subset of 255 landslide bodies, the areas of interest (AOI), interacting with urban planning within the study region, Figure 1(b). These landslides were selected based on the intersection of the hydrogeological asset plan (https://aubac.it/piani-di-bacino/piani-di-assetto-idrogeologico, accessed on August 1, 2024) catalogue and AOI defined by ABDAC’s project for assisting post-seismic reconstruction activities. While the hydrogeological asset plans catalogue contains a comprehensive inventory of mapped landslides in the region, the present study focuses specifically on those that have a direct impact on urban areas and infrastructure within the scope of ABDAC’s project.”

Caption of figure 1: "calcareous admarly.." perhaps is marly?

Authors’ Response: Agreed. Done! Thank you!

Lines 132-141: I like these bullet points on S1 and CSK satellites, please include some references about the data you mention here.

Authors’ Response: Agreed. We added four relevant references. Thank you!

Line 153: ..two pieces of what? I guess of the segment.. please clarify

Authors’ Response: Done. Thank you! It is “two linear pieces”. Segment is part of time series, but we mean the part of fitted linear trend.

Line 169: remove double the

Authors’ Response: Done. Thank you!

line 183: please remove superiority, replace it with advantages

Authors’ Response: Done. Thank you!

General about chapter 2.3: Why is the STPD revisited here? has it been modified from the original? From what is included between lines 153 and 188 I cannot understand if you are revisiting a published method with some tuning or are applying the original method.. please clarify the eventual differences or remove revisited..

Authors’ Response: Done. Thank you! At the beginning of Section 2.3, we added:

“This section reviews the already established STPD utilized in this study for estimating trend turning points and their directions.”

RESULTS

Line 209: Criteria? Where are described such criteria? do you refer to lines 178-181? please clarify or recall the chapter in which you presented and discussed such criteria

Authors’ Response: Done. Thank you!  The conditions (criteria) are: The thresholds are |NDRI| < 0.3, |DIR| > 0.2 and only non-increasing or non-decreasing time series. We referred to the caption of Table 1.

Figure 6: what blue and red columns represent?

Authors’ Response: Done. Thank you!  We modified the caption: “….ascending (in red) and descending (in blue) PS-InSAR time series…”

Line 215: it is unclear where these polygons are located in relation to weather stations and how big these are (square km?). Can you show them on maps with dedicate colours? I do not understand if in figure 1b these polygons are the large areas delimited by black dashed and continuous lines or what else. If this is the case, I would not say that the four AOI polygons are small (caption of figure 1) nor they are near the local weather stations (line 215).. See general point N 3.

Authors’ Response: Thank you for your insightful comment. Figure 1(b) and its legend are updated. Each pair of ascending and descending time series is shown by a blue star, and the weather stations are shown by red squares in Figure 1(b). The reliability and uncertainty analysis are discussed in the new Section 3.3 and Discussion Sections 4.1 and 4.2.

Line 221-235: Rather than the qualitative description of figure 8 like the one proposed in these lines, can the author quantitatively relate the change of gradient of cumulated precipitation trend with the change of sign and intensity (mm/year) of the turning point? In other words: is there a relation (linear?) of some sort between the cumulated precipitation temporal trend and red lines in figure 8? If so, is this the same across all the AOI - i.e. across the entire study area?

Authors’ Response: Thank you for your insightful comment. To address your comment, we analyzed GPM (calibrated satellite-based monthly precipitation measurements with spatial resolution of 10 km) for the entire region and tried to describe quantitively the relationship between precipitation pattern change and displacement turning points in new Section 3.3 and the discussion section. We also updated the Abstract and Conclusions sections to reflect the analysis of additional datasets.

DISCUSSION

Line 247: are all the "many researchers" included in the only paper you mention or there are other papers to mention? Either rephrase or include other references.

Authors’ Response: Agreed. We added two more relevant references. Thank you!

Line 250: "... and the limitations of this study"

Authors’ Response: Done. Thank you!

Line 267-269: please rephrase to clarify what you mean

Authors’ Response: Thank you! We rephrased it as “…Luppichini et al. (2023) demonstrated that global warning has caused a decline in precipitation amount mainly in the dry season followed by intense and heavy precipitation events in north-central Italy.”

Line 272: please change spells (periods, intervals, seasons...)

Authors’ Response: Thank you! We replaced “spells” with “periods”.

Line 295-297: please add some references.

Authors’ Response: We added four relevant references. Thank you!

Line 319: please clarify what QGIS is (an open source GIS software) and provide a reference.

Authors’ Response: Thank you! We added: “…(an open-source quantum geographic information system software [80,81])”.

 

We thank you again very much for your time and efforts for providing such insightful and detailed comments that helped us significantly improve the presentation of our manuscript.

 

Best regards,

Ebrahim Ghaderpour, PhD

On behalf of all co-authors

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Overall, this article seems well written and detailed work.
1.Can the findings be generalized to other regions or other types of landslides? Authors should discuss the generality and applicable conditions of their methods.
2. Enrich the conclusion section by placing more emphasis on the main findings and contributions of the study and by clearly indicating the novelty of the study and its addition to the existing body of knowledge.
3. Does the non-increasing and non-decreasing assumption mentioned in the paper apply to all cases? The authors should further discuss the limitations of this assumption and its impact on the results.
4. In section 2.3, it is recommended that the authors compare the results of the STPD method with those of other existing methods or techniques to demonstrate their strengths and potential weaknesses.
5. To ensure the data's representativeness, the authors should consider its spatial distribution and the extent to which it captures variability across the entire study area.
6. Further discuss the correspondence of the turning point test results to the actual geological conditions, and clarify how to ensure the accuracy of geological interpretations of these results.

Comments on the Quality of English Language

Overall, this article seems well written and detailed work. However, there are some grammar issues, such as Line 75, 

Author Response

Authors’ Responses to Reviewer 2

Overall, this article seems well written and detailed work.

Dear reviewer,

We would like to thank you very much for your time and positive feedback and insightful comments that helped us improve the presentation of our manuscript further. Please see below our responses where the changes are highlighted in the revised version.

1.Can the findings be generalized to other regions or other types of landslides? Authors should discuss the generality and applicable conditions of their methods.

Authors’ Response: Thank you for your insightful comment. At the end of the discussion section, we added the following paragraph:

“Lastly, depending on the type of time series and noises involved, the parameters of STPD, such as window and step sizes and the minimum distance between turning points, must be tuned to optimize the model’s performance. In the present study, like the earlier study by the authors [22] through an extensive simulation, a five-year-long window size and a one-year-long step size with a minimum of one-year difference between turning points are identified to be optimal for the purpose of detecting slow-moving landslides that occur slowly in nature. Therefore, users need to be cautious when applying STPD to other applications as tuning parameters are very important.”

We also analyzed GPM (calibrated satellite-based monthly precipitation measurements with spatial resolution of 10 km) for the entire region and tried to describe quantitively the relationship between precipitation pattern changes and displacement turning points in new Section 3.3 and the discussion section. We also updated the Abstract and Conclusions sections to reflect the analysis of additional datasets.

2. Enrich the conclusion section by placing more emphasis on the main findings and contributions of the study and by clearly indicating the novelty of the study and its addition to the existing body of knowledge.

Authors’ Response: Thank you for your insightful comment. The Conclusion section is rewritten as follows:

“This research presents a multifaceted approach for analyzing slow-moving deformation in the landslide-prone areas of Central Apennines, Italy, utilizing PS-InSAR time series data from CSK and Sentinel-1 satellites. The study’s primary contribution lies in its innovative application of STPD for the precise detection of dates of significant velocity changes in landslide displacement. This application enabled the identification of trend changes to facilitate the generation of spatial maps illustrating both the timing and magnitude of these changes and providing a comprehensive view of landslide dynamics across the study area. A key advancement of this research is the application of STPD to investigate the interconnection between precipitation patterns and slow-moving landslides, establishing a validated approach to scrutinizing trend changes due to intense rainfall. This analysis revealed significant correlations that have been challenging to quantify in previous studies. Our findings uncovered that extreme precipitation events, particularly when followed by extended dry periods, play a crucial role in reactivating slow-moving landslides within the area of interest. This discovery contributes substantially to the understanding of the complex interplay between climate variability and landslide activity. Furthermore, this research identified a trend of gradual warming and increasingly dry seasons, expected to escalate the frequency of extreme precipitation events. This observation highlights the potential for increased landslide reactivation in Central Italy, emphasizing the urgent need for adaptive strategies in landslide risk management to mitigate socioeconomic risks and damages in the face of current and future climate change. By combining satellite-based displacement data with climate analysis, this study provides an innovative and holistic understanding of landslide behavior over time and space, representing a significant advancement in landslide monitoring and prediction methodologies in the context of changing climate patterns. Future research should build upon these findings to develop more accurate predictive models and risk assessment tools, incorporating the temporal and spatial patterns of landslide activity identified in this study. The methodologies developed here have the potential for application in other landslide-prone regions globally, contributing to improved landslide risk management strategies worldwide and enhancing resilience in vulnerable regions. Some of the main findings of the present research are summarized below.

• Relatively more turning points in the PS-InSAR time series inside the landslide-prone polygons were detected during the summers of 2019 and 2020 for Marche and Lazio sub-regions, respectively.
• More than 80% of detected turning points in the PS-InSAR time series had a direction between −4 to 4 mm/year.
• Ground-based and satellite-based (GPM) monthly precipitation time series generally had a strong correlation (r ≥ 0.7) with similar turning points and directions.
• The coastal sub-regions of Marche and Abruzzo were drier than Umbria and Lazio sub-regions with an insignificant precipitation rate during 2017–2022.
• Most of the turning points in the accumulated GPM precipitation time series were during the summers of 2017 and 2020 with positive directions, potentially reactivating many slow-moving landslides across the affected areas.”

3. Does the non-increasing and non-decreasing assumption mentioned in the paper apply to all cases? The authors should further discuss the limitations of this assumption and its impact on the results.

Authors’ Response: Thank you for your insightful comment. To address your comment, we modified the first paragraph in Section 4.2 as:

“The non-increasing or non-decreasing assumption of trend component in this research (see Figure 2) limits the results to the places where the ground is always moving toward the same downslope direction with respect to satellite line-of-sight. Urgilez Vinueza et al. [62] pointed out that this assumption is reasonable as there is not a clear geophysical justification on how the sign of line-of-sight direction can switch from negative to positive or vice versa. They assumed that switching gradient from positive to negative or vice versa could be due to sensor or phase unwrapping errors which could be plausible for steep slopes. In regions with relatively flat terrain, changing sign in displacement gradients could be due to land subsidence/uplift, correlated with groundwater level fluctuation (e.g., in industrial regions [23]). Table 1 showed that applying this assumption and enforcing the thresholds for NDRI and DIR eliminated about 60% of PS; however, the remaining PS-InSAR time series demonstrated pretty good results, many of which are randomly examined by visual interpretation and QGIS software (an open-source quantum geographic information system software [80,81]) and from the field knowledge. Loosening the conditions potentially brings in time series with more uncertain turning points that may be due to measurement errors, phase unwrapping, atmospheric noise, etc.”

4. In section 2.3, it is recommended that the authors compare the results of the STPD method with those of other existing methods or techniques to demonstrate their strengths and potential weaknesses.

Authors’ Response: Thank you for your insightful comment. We added in Section 2.3:

“The advantages of STPD over other popular methods, such as Pettitt’s and running slope difference in terms of root mean square error has also been demonstrated in [22]. Through an extensive simulation experiment, Ghaderpour et al. [22] also showed that the overall accuracy of STPD for detecting turning points was approximately 83% while Pettitt’s overall accuracy for detecting a change point was 65%.”

In addition, in new Section 3.3, we made a comparison between station-based, and satellite-based turning point by STPD.

5. To ensure the data's representativeness, the authors should consider its spatial distribution and the extent to which it captures variability across the entire study area.

Authors’ Response: Thank you for your insightful comment. We also analyzed GPM (calibrated satellite-based monthly precipitation measurements with spatial resolution of 10 km) for the entire region and tried to describe quantitively the relationship between precipitation pattern changes and displacement turning points in new Section 3.3 and the discussion section. We also added more details in Abstract, Discussion and Conclusions. We also added some details in the Dataset section.

6. Further discuss the correspondence of the turning point test results to the actual geological conditions, and clarify how to ensure the accuracy of geological interpretations of these results.

Authors’ Response: Thank you for your insightful comment. We added a few sentences in the discussion section 4.1 to address your comment.

 

We thank you again very much for your time and efforts for providing such insightful and detailed comments that helped us significantly improve the presentation of our manuscript.

 

Best regards,

Ebrahim Ghaderpour, PhD

On behalf of all co-authors      

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The revised paper has been significantly improved with the inclusion of more detailed data and methodological descriptions, which has enhanced the reader's comprehension of the research process. The utilization of charts and maps for presenting study results has effectively conveyed the correlation between trend changes and precipitation patterns in an intuitive manner. The delineation of the study area has been clarified, thereby facilitating a better understanding of the research's specific scope. Furthermore, the integration of Global Precipitation Measurement (GPM) data has notably fortified the analysis concerning the relationship between precipitation pattern variations and landslide activities.
Minor Comments:
1. Whether the paper adequately elaborates on the background and importance of the research, especially in the context of the current climate change and frequent geological disasters
2. The manuscript should clarify whether the study's results are applicable universally or if they are confined to specific regions or conditions. If the findings are context-specific, this limitation should be explicitly stated.
3. If possible, sensitivity analysis using higher resolution precipitation data is recommended to assess the impact of resolution changes on the results.
4. The manuscript must adhere to the appropriate formatting guidelines for keywords, ensuring they are relevant, concise, and accurately reflect the content of the paper.
5. The placement of figures and images within the manuscript should be meticulously reviewed to ensure they are seamlessly integrated into the text. This will ensure that the visual elements enhance the narrative and contribute to a coherent understanding of the research.

Author Response

Authors’ Responses to Reviewer 2

Dear reviewer,

We would like to thank you very much for your time and positive feedback and insightful comments that helped us improve the presentation of our manuscript further. Please see below our responses where the changes are highlighted in the revised version.

The revised paper has been significantly improved with the inclusion of more detailed data and methodological descriptions, which has enhanced the reader's comprehension of the research process. The utilization of charts and maps for presenting study results has effectively conveyed the correlation between trend changes and precipitation patterns in an intuitive manner. The delineation of the study area has been clarified, thereby facilitating a better understanding of the research's specific scope. Furthermore, the integration of Global Precipitation Measurement (GPM) data has notably fortified the analysis concerning the relationship between precipitation pattern variations and landslide activities.

Minor Comments:

1. Whether the paper adequately elaborates on the background and importance of the research, especially in the context of the current climate change and frequent geological disasters.

Authors’ Response: Thank you for your insightful comment. Following your suggestion, in the first paragraph of Introduction, we added the following sentences and added two more related articles on impact of climate change on slow-moving landslide reactivation (the focus of this study):

“For example, Fiolleau et al. (2023) showed that reactivation of slow-moving landslides occurred during an intense rainfall event after a 7-month drought in their study region. Narcisi et al. (2024) demonstrated the impact of temperature and precipitation trend changes on slow-moving landslides using in-situ and remote sensing instruments in Piemonte region, Italy.”

2. The manuscript should clarify whether the study's results are applicable universally or if they are confined to specific regions or conditions. If the findings are context-specific, this limitation should be explicitly stated.

Authors’ Response: Thank you for your insightful comment. Following your suggestion, we have added the following sentences in the discussion section.

“The STPD utilized herein can be applied to other regions across the world for studying the relationships between precipitation and ground deformation trend changes. Intense rainfalls after dry periods appear to be triggering landslides in the affected regions in Central Italy, confirming the earlier results for province of Frosinone and Central Europe (Tichavsky et al. 2019, Ghaderpour et al. 2024). Apart from precipitation, earthquakes, volcanic activities, snowmelt, water-induced soil erosion, changes in groundwater, and human-caused disturbances are also other potential triggering factors of landslides that need to be investigated. Therefore, although generalization of the results presented herein to other regions may sound logical, other factors triggering landslides must also be studied carefully  alongside precipitation.”

3. If possible, sensitivity analysis using higher resolution precipitation data is recommended to assess the impact of resolution changes on the results.

Authors’ Response: Thank you for your insightful comment. Unfortunately, high-resolution station-based measurements (within 1 km away from the PS-InSAR time series) were not available to us for performing a more rigorous sensitivity analysis. However, as a general picture, we have incorporated satellite-based GPM precipitation measurements at about 10 km resolution as a proxy for estimating precipitation trends across the region. We have also shown the general trends of GPM time series match the trends of the four station-based precipitation time series (as illustrated in Figures 8,9,10).

4. The manuscript must adhere to the appropriate formatting guidelines for keywords, ensuring they are relevant, concise, and accurately reflect the content of the paper.

Authors’ Response: Thank you for your insightful comment. We have checked the keywords and included “Central Italy”, “Global Precipitation Measurement (GPM)”, and “Slow-Moving Landslides” as other keywords. The names of COSMO-SkyMed, Sentinel-1, PS-InSAR, STPD, and GPM are important to be mentioned as the keywords for a more effective visibility in online search platforms, such as Scopus and Web of Science.

5. The placement of figures and images within the manuscript should be meticulously reviewed to ensure they are seamlessly integrated into the text. This will ensure that the visual elements enhance the narrative and contribute to a coherent understanding of the research.

Authors’ Response: Thank you for your insightful comment. Following your suggestion, we have repositioned the figures to appear right after their citation in the text.

We thank you again very much for your time and efforts for providing such insightful and detailed comments that helped us significantly improve the presentation of our manuscript.

 

Best regards,

Ebrahim Ghaderpour, PhD

On behalf of all co-authors      

Author Response File: Author Response.pdf