the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Aug 2024
Large-scale assessment of rainfall-induced landslide hazard based on hydrometeorological information: application to Partenio Massif (Italy)
Abstract. The definition of reliable tools for rainfall-induced landslide hazard assessment is often limited by the lack of long records of occurred landslides and relevant hydrometeorological variables. This is the case of the mountainous areas of Southern Apennines of Campania (Italy), diffusely covered by loose pyroclastic deposits laying upon limestone bedrock, and frequently subjected to rainfall-triggered shallow landslides. To get around this issue, a 500-year long synthetic dataset of the response to precipitation of a typical slope of the area has been generated, by means of a physically based model previously validated through experimental data. The obtained dataset, containing hourly values of soil moisture and suction, and of water level in an ephemeral aquifer developing in the uppermost fractured bedrock, has been used to assess slope stability through the calculation of the factor of safety. Based on the synthetic data, empirical thresholds for the prediction of landslide occurrence have been defined, either meteorological (i.e., based on rainfall intensity and duration) or hydrometeorological (i.e., coupling rainfall depth with antecedent root-zone soil moisture or aquifer water level). The results show that, where meteorological forcing and slope characteristics are perfectly known, hydrometeorological thresholds outperform the meteorological ones, and that a 3D threshold based on root-zone soil moisture, aquifer level, and rainfall depth, provides nearly unerring landslide predictions. The use of two antecedent hydrologic variables also allows identifying two different landslide triggering mechanisms, respectively typical of the beginning and of the end of the rainy season.
To extend the application to large areas, the uncertainties linked to the spatial variability on slope geomorphologic characteristics and hydrometeorological variables were considered as random errors. Hence, foreseeing the application to the north-facing side of Partenio Massif (about 80 km2), the synthetic dataset has been perturbed, superimposing Normal-distributed random fluctuations to the calculated values of the factor of safety, and to the hydrometeorological variables used as landslide predictors. Although the uncertainty reduces the predictive skill of all the thresholds, the hydrometeorological ones show more robustness, with small numbers of both missed and false alarms. This result is confirmed by the application of the obtained thresholds to available data of landslides, rainfall and root-zone soil moisture for the period 2002–2020 in the area.
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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
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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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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2329', Anonymous Referee #1, 18 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2329/egusphere-2024-2329-RC1-supplement.pdf
- AC2: 'Reply on RC1', Pasquale Marino, 18 Jan 2025
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RC2: 'Comment on egusphere-2024-2329', Anonymous Referee #2, 09 Dec 2024
I read with interest the paper by Daniel Camilo Roman Quintero and co-authors. Their research in physically-based modelling of shallow landslides is welcome in the literature and it fits well within the scope of NHESS. They propose an approach to deal with synthetic long record of landslide occurrences and hydrometeorological conditions based on a region of Italy. Their research allow to define empirical rainfall thresholds associated with landslide occurrence in the context of landslide early warning systems. Their approach is aimed are being used on large area contexts. I also like the fact that they compare their modelling outputs to actual landslide occurrences.
While I have limited comments over the modelling approach - the author demonstrate a strong understanding of hillslope response to rainfall and soil moisture - my main concern is, overall, that a discussion is truly missed. The authors proposed a combined results-discussion section, but apart from a link to observed landslide occurrences in the region, there is, as also pointed out by the other reviewer, not real reflection of their research with respect to the method approach, the applicability, the early warning context (to name but a few points that could be discussed) and the associated state of the art literature. To me, it makes little sense to have a manuscript without a proper discussion.
Added to the comments of the first reviewer, I have listed below other comments which I hope would be helpful to improved the manuscript:
The definition of “large area” is somehow unclear. 80km² for the case-study test zone is not a very large zone when compared to many landslide data-driven susceptibility assessments. There is maybe some way to better define/constrained this scale of analysis context.
The end of the abstract is very case-study specific. One would welcome an ending with a more general/broader statement.
Lines 33-34. To be accurate, the issue of human influence on landslides is not only in urban areas. Note also that there are some recent work (and maybe more relevant) that allow to support such a broad statement. For example, Ozturk et al. (2022) https://www.nature.com/articles/d41586-022-02141-9
Line 35. Predicting the occurrence of landslide is also relevant when outside cities and/or when the landscape is not disturbed by human activities. Landslides are above all natural processes, and this is what the authors want to model here. The focus on urban areas from the start of the introduction is somehow misleading.
Line 44: ..” depends not only on”.. is a strange formulation. After the not only, we would expect “but also” somehow. This sentence must be rephrased.
Lines 75-82: it is strange to have such an emphasis on the study area in the middle of the state of the art.
Lines 95-125: this is a very long part of the introduction to explain the goal of the research and provide a supposedly short overview of what has been done. In my opinion too many details are provided here; it sounds more like an extensive abstract.
Several times, the emphasis is put in LEWS. Although rainfall threshold determination is a key aspect of LEWS, their study can also be relevant to other hazard assessment needs. This is something that could be nuanced.
The title refers to large areas. In the introduction “wide areas” is several time used. Beyond this lack of consistency, one would appreciate a definition of what “large” actually means (size, spatial resolution, etc.)
Figure 1. Some local names in the maps are not readable. What is the background information of the map? In such a figure one would expect a visualization of the topography to better understand, for example, the slope context.
Line 135. Local names are being used for specific location. However, without a map to local them, such information is not relevant to a broad audience. .
Line 138: “a huge debris avalanche”. What do you mean by “huge”. I would suggest not to use such a subjective wording. Here is a reference that could help:
McColl, S. T., & Cook, S. J. (2024). A universal size classification system for landslides. Landslides, 21(1), 111-120.
Line 221. Replace “associated to” by “associated with”
Line 245: early warning system. Remover capital letter
Line 265. “normal…” remove capital letter
Lines 257-266: There seem to be some repetition here to what is being said earlier.
Line 269: remove the two “,’
Figure 4 (and in other places). Is the use of decimal values in the percentage valid? Does it make sense?
Table 2. What is “s.l.m.”?
Line 229. The work of Brocca et al 2021) is mentioned as a research carried out on a relatively small area (less than 100 km²). How can we say it is a small area while, here, one speak about lar scale for an area of 80 km². As said earlier, one need a more robust definition of the scale aspect of this research.
Line 368. Remove capital letter in “Normally…”
Table 4. “large scale” instead of “Large scale”
Lines 364-370. Some of this information is connected to the description of the study area. It comes a bit as a surprise that this is provided here.
Figure 6. Make sure that all symbols used in the figure are explained in the caption. Same comment for figure 7 and others
Line 385. Dataset instead of data set.
Line 410. Such isolated sentence must be removed (or attached to a main paragraph).
Section 3.1. this is purely results, no discussion here. In sections 3.2 and 3.3 reference is only made to work that identified landslides in the region.
Line 430. 175 failures in 500 years. However, in line 376, 20 events. This is unclear.
Citation: https://doi.org/10.5194/egusphere-2024-2329-RC2 - AC1: 'Reply on RC2', Pasquale Marino, 18 Jan 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2329', Anonymous Referee #1, 18 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2329/egusphere-2024-2329-RC1-supplement.pdf
- AC2: 'Reply on RC1', Pasquale Marino, 18 Jan 2025
-
RC2: 'Comment on egusphere-2024-2329', Anonymous Referee #2, 09 Dec 2024
I read with interest the paper by Daniel Camilo Roman Quintero and co-authors. Their research in physically-based modelling of shallow landslides is welcome in the literature and it fits well within the scope of NHESS. They propose an approach to deal with synthetic long record of landslide occurrences and hydrometeorological conditions based on a region of Italy. Their research allow to define empirical rainfall thresholds associated with landslide occurrence in the context of landslide early warning systems. Their approach is aimed are being used on large area contexts. I also like the fact that they compare their modelling outputs to actual landslide occurrences.
While I have limited comments over the modelling approach - the author demonstrate a strong understanding of hillslope response to rainfall and soil moisture - my main concern is, overall, that a discussion is truly missed. The authors proposed a combined results-discussion section, but apart from a link to observed landslide occurrences in the region, there is, as also pointed out by the other reviewer, not real reflection of their research with respect to the method approach, the applicability, the early warning context (to name but a few points that could be discussed) and the associated state of the art literature. To me, it makes little sense to have a manuscript without a proper discussion.
Added to the comments of the first reviewer, I have listed below other comments which I hope would be helpful to improved the manuscript:
The definition of “large area” is somehow unclear. 80km² for the case-study test zone is not a very large zone when compared to many landslide data-driven susceptibility assessments. There is maybe some way to better define/constrained this scale of analysis context.
The end of the abstract is very case-study specific. One would welcome an ending with a more general/broader statement.
Lines 33-34. To be accurate, the issue of human influence on landslides is not only in urban areas. Note also that there are some recent work (and maybe more relevant) that allow to support such a broad statement. For example, Ozturk et al. (2022) https://www.nature.com/articles/d41586-022-02141-9
Line 35. Predicting the occurrence of landslide is also relevant when outside cities and/or when the landscape is not disturbed by human activities. Landslides are above all natural processes, and this is what the authors want to model here. The focus on urban areas from the start of the introduction is somehow misleading.
Line 44: ..” depends not only on”.. is a strange formulation. After the not only, we would expect “but also” somehow. This sentence must be rephrased.
Lines 75-82: it is strange to have such an emphasis on the study area in the middle of the state of the art.
Lines 95-125: this is a very long part of the introduction to explain the goal of the research and provide a supposedly short overview of what has been done. In my opinion too many details are provided here; it sounds more like an extensive abstract.
Several times, the emphasis is put in LEWS. Although rainfall threshold determination is a key aspect of LEWS, their study can also be relevant to other hazard assessment needs. This is something that could be nuanced.
The title refers to large areas. In the introduction “wide areas” is several time used. Beyond this lack of consistency, one would appreciate a definition of what “large” actually means (size, spatial resolution, etc.)
Figure 1. Some local names in the maps are not readable. What is the background information of the map? In such a figure one would expect a visualization of the topography to better understand, for example, the slope context.
Line 135. Local names are being used for specific location. However, without a map to local them, such information is not relevant to a broad audience. .
Line 138: “a huge debris avalanche”. What do you mean by “huge”. I would suggest not to use such a subjective wording. Here is a reference that could help:
McColl, S. T., & Cook, S. J. (2024). A universal size classification system for landslides. Landslides, 21(1), 111-120.
Line 221. Replace “associated to” by “associated with”
Line 245: early warning system. Remover capital letter
Line 265. “normal…” remove capital letter
Lines 257-266: There seem to be some repetition here to what is being said earlier.
Line 269: remove the two “,’
Figure 4 (and in other places). Is the use of decimal values in the percentage valid? Does it make sense?
Table 2. What is “s.l.m.”?
Line 229. The work of Brocca et al 2021) is mentioned as a research carried out on a relatively small area (less than 100 km²). How can we say it is a small area while, here, one speak about lar scale for an area of 80 km². As said earlier, one need a more robust definition of the scale aspect of this research.
Line 368. Remove capital letter in “Normally…”
Table 4. “large scale” instead of “Large scale”
Lines 364-370. Some of this information is connected to the description of the study area. It comes a bit as a surprise that this is provided here.
Figure 6. Make sure that all symbols used in the figure are explained in the caption. Same comment for figure 7 and others
Line 385. Dataset instead of data set.
Line 410. Such isolated sentence must be removed (or attached to a main paragraph).
Section 3.1. this is purely results, no discussion here. In sections 3.2 and 3.3 reference is only made to work that identified landslides in the region.
Line 430. 175 failures in 500 years. However, in line 376, 20 events. This is unclear.
Citation: https://doi.org/10.5194/egusphere-2024-2329-RC2 - AC1: 'Reply on RC2', Pasquale Marino, 18 Jan 2025
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- 1
Daniel Camilo Roman Quintero
Pasquale Marino
Abdullah Abdullah
Giovanni Francesco Santonastaso
Roberto Greco
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3102 KB) - Metadata XML