Rockwall permafrost dynamics evidenced by Automated Electrical Resistivity Tomography at Aiguille du Midi (3842 m a.s.l., French Alps)

Abdulsamad, Feras; Bock, Josué; Magnin, Florence; Malet, Emmanuel; Revil, André; Ben-Asher, Matan; Richard, Jessy; Duvillard, Pierre-Allain; Karaoulis, Marios; Condom, Thomas; Ravanel, Ludovic; Deline, Philip

doi:https://doi.org/10.5194/egusphere-2025-637

Preprints

https://doi.org/10.5194/egusphere-2025-637

Preprints

04 Mar 2025

| 04 Mar 2025

Rockwall permafrost dynamics evidenced by Automated Electrical Resistivity Tomography at Aiguille du Midi (3842 m a.s.l., French Alps)

Feras Abdulsamad, Josué Bock, Florence Magnin, Emmanuel Malet, André Revil, Matan Ben-Asher, Jessy Richard, Pierre-Allain Duvillard, Marios Karaoulis, Thomas Condom, Ludovic Ravanel, and Philip Deline

Abstract. Permafrost warming significantly affects the stability of rockwalls in high altitude regions. Subsurface monitoring of permafrost is essential to assess the resulting potential geohazards. This study investigates permafrost dynamics at Aiguille du Midi (3842 m a.s.l., French Alps) using an Automated-Electrical Resistivity Tomography (A-ERT) approach, conducted over a period of four years (2020–2023). A total of three geophysical profiles have been installed on three sides of the Aiguille du Midi. An autonomous acquisition system for permanent resistivity monitoring and remote data acquisition is implemented. A time-lapse inversion technique is employed to get time lapse variations of the electrical resistivity of Aiguille du Midi at different time scales. In addition to the field measurements, laboratory measurements of electrical resistivity are conducted on one water-saturated granite sample in both unfrozen and frozen conditions to evaluate the temperature-dependency of resistivity. Temperature information about the thermal state of permafrost is available from three shallow boreholes drilled in 2009, used to validate our interpretation. A-ERT showed significant variations in the active layer thickness across different rock faces, along with a slight decrease in the resistivity of permafrost, indicating its warming over time. Our findings indicate that the temperature dependence of resistivity in field conditions (open system) is slightly less pronounced than in controlled laboratory experiments (closed system). Using a petrophysical model connecting temperature to resistivity, the temperature distribution within the frozen zone can be estimated from the resistivity measurements (during summer and autumn) with an accuracy of ~±1 °C. This research underscores the efficacy of ERT as a promising, non-invasive tool for monitoring permafrost dynamics in Alpine environments. It highlights the need for further methodological refinement to better resolve subsurface properties, potentially using induced polarization data.

Received: 11 Feb 2025 – Discussion started: 04 Mar 2025

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RC1:
'Comment on egusphere-2025-637', Anonymous Referee #1, 16 Apr 2025
The manuscript titled "Rockwall permafrost dynamics evidenced by Automated Electrical Resistivity Tomography at Aiguille du Midi (3842 m asl, French Alps)" presents repeated ERT measurements in a high-alpine environment, complemented by laboratory experiments and a comparison of measured borehole temperatures with ERT-derived temperature estimates. The dataset is not entirely unique in terms of elevation, as it represents only a partially higher-altitude setting with conditions comparable to other study sites and publications. While we acknowledge the effort involved in obtaining and processing this dataset, we recommend major revisions. Specifically, the manuscript would benefit from a clearer articulation of its novelty and research objectives. At present, the focus and unique contribution of the study remain somewhat unclear. The identified research gap – namely, that A-ERT at high altitudes has not yet been tested for long-term permafrost monitoring – is relatively weak. As a result, the study’s aim appears vague, and the added value or benefit of the findings remains insufficiently presented and discussed. Furthermore, we encourage a more transparent presentation of the dataset, particularly regarding its temporal and spatial coverage (see specific comments below).
Main concerns
Novelty and previous studies: The methods applied in the A-ERT monitoring (e.g., Keuschnig et al., 2017; Mollaret et al., 2019), the inversion routines, and the laboratory calibration approaches (Krautblatter et al., 2010; Magnin et al., 2015; Scandroglio et al., 2021; Etzelmüller et al., 2022, Offer et al., 2025) are well-established. Several parts of the lab analysis and interpretation closely resemble those of previous studies. We recommend reconsidering the novelty of your study – possibly by applying new or more advanced analyses – and restructuring the manuscript accordingly. The stated goals (1–3) at the end of the introduction have been substantially addressed in prior work (e.g., Keuschnig et al., 2017; Mollaret et al., 2019, Scandroglio et al., 2024, Offer et al., 2025). Clarify what differentiates your approach or findings from those.

Abstract (Lines 44–47): The abstract should clearly state the main goal of the study. As currently written, it reiterates that ERT is effective in permafrost studies – something that is already well-known (Herring et al., 2023). The methodological innovations or scientific contributions are not emphasized enough. Please revise this to highlight what is new or different in your approach or findings.

Time period and spatial coverage of A-ERT monitoring: The abstract states that monitoring occurred from 2020–2023. However, there are no measurements before June 2020, and between June 2020 and September 2021, ERT acquisitions were only conducted occasionally. Furthermore, significant data gaps occurred from summer 2022 onward on the NW-facing profile, and from summer 2023 on, due to cable failures. These sparse measurements (occasional repetition and cable defects) do not constitute continuous monitoring. Please clarify the actual continuous data availability in the entire manuscript (maximum of 2 years instead of 4 years). Moreover, the east profile, although included in the inversion models, is not adequately analyzed, particularly in terms of contact resistance, average apparent resistivity, and its temporal evolution. Why not analyze this profile more comprehensively? You mentioned problems with data gaps, however, data sets of the E profile are included in the inversions. Furthermore, the interpretation relies on a limited number of selected tomographies, which raises the question of whether A-ERT truly offers advantages over occasional repeated campaigns – given this, the conclusions appear overstated. With reference to the selected data presented in Table 1, it remains unclear how representative these data are. We recommend clarifying the criteria for their selection and discussing to what extent this choice may influence the overall findings and interpretations.

Laboratory calibrations: We disagree with the statement that the temperature–resistivity dependency is less pronounced in field conditions. Rather, the temperature–resistivity relation remains consistent, but it is often influenced by discontinuities, not captured in lab samples. The manuscript presents calibration data from only one sample (and one electrode array!) taken directly from the study site, while the second sample originates from the lower Cosmiques ridge and may not be fully representative. We strongly recommend additional tests using multiple samples and/or multiple array configurations on each sample to characterize variability and quantify uncertainty. Prior studies (Krautblatter et al., 2010; Magnin et al., 2015; Scandroglio et al., 2021; Offer et al., 2025) have demonstrated the considerable range in temperature–resistivity relationships between different rock samples and even within individual electrode arrays. Your main interest lies between the temperature range from -5 to +5°C, but you only show five observations for each sample with only one point on the freezing path. Please address this in your further laboratory testing. Furthermore, in Line 540, it is stated that resistivity in frozen conditions is primarily dependent on temperature, while other parameters are assumed to be constant. We would like to clarify that resistivity in freezing rock is primarily controlled by the remaining unfrozen pore water content, which in turn strongly influenced the pore water salinity. Porosity, by contrast, remains constant in both frozen and unfrozen states and should not be considered a temperature-dependent variable in this context. Finally, we question the decision to perform the calibration tests using vacuum saturation with degassed water. Why was this approach chosen over using snowmelt water from the field site and applying atmospheric pressure conditions? The latter would be more realistic for field conditions.

Data Processing and Inversion Parameters: Please clarify the following points regarding your data processing:
How are outliers defined (Line 288)?

Why was a linear error model with 5% chosen? What is the absolute error?

Which inversion parameters were applied, especially lambda, paraDepth, quality, and z-weighting?

Include chi² values for all inversion results, as the model mesh appears very fine, with little growing with depth, raising concerns about overfitting of the data. Fig. 13 (P1) shows that you already obtain eight resistivity values for a depth<2m, which seems to be inappropriate for an electrode spacing of 5m. We would highly recommend adapting the mesh.

In addition, we advise against using measured apparent resistivity directly for trend analysis. The instrument may apply incorrect geometric factors, which could distort your results. Reanalyze the data using resistance or properly computed apparent resistivity (e.g., using pyGIMLi) for greater reliability.

Figures – numbers, quality and color schemes: The manuscript includes a large number of figures, which makes it difficult to follow the overall narrative. We suggest reducing the number of figures in the main text – moving less essential ones to the appendix or supplementary material – and clearly emphasizing the main findings of each figure, either through improved visual presentation or more informative captions. Nearly all figures are not colorblind-friendly, particularly those using red and green together. Please revise all color schemes accordingly. Additionally, the choice of colors should support the interpretation of the data more effectively. For instance, in Figure 4, consider using clearly distinguishable colors to differentiate between excluded and retained datapoints.

Figure 5: The figure is difficult to interpret. The bars are barely readable, and the intended comparison is unclear. Are you examining interannual changes or comparing specific time periods? The same question arises for Figure 9-12 – what is the focus of the study? In the current layout, it is complicated to distinguish between interannual and pluriannual changes.

Figure 6: Rather than indicating the number of electrodes, specify the depth of investigation. It would be more appropriate to present inverted resistivity values here. The connecting lines between data points suggest a linear trend, which may misrepresent the actual physical behavior – particularly during the transition between unfrozen and frozen conditions. We therefore suggest removing these trend lines to avoid a misleading interpretation.

Figure 7: This figure compares resistivity across multiple profiles. However, a direct comparison is problematic due to differences in profile orientation—P1 is oriented perpendicular to the surface, whereas the others are nearly horizontal. In addition, it would be helpful to indicate the expected permafrost extent and the corresponding threshold resistivity values (subfigure). We also recommend adjusting the color scale: resistivity values as high as 500.000 Ωm are not physically explainable and should be limited. The grey lines representing the infrastructure are barely visible.

Figure 9-10: It appears that different meshes were used for the inversion routines and the subsequent calculation of resistivity change ratios. This inconsistency should be addressed. We also recommend using consistent x- and y-axis limits in both figures.

Figure 11: The red arrows overlaid on the tomograms obscure important details. Additionally, mark the locations of observed drainage paths in Figure 12 for better correlation between figures.

Borehole validation: In the discussion, it is stated that borehole temperature data were used to validate both the interpretation and the estimated temperatures (Lines 486–487). The borehole temperature data of the different boreholes are not included; however, the manuscript would greatly benefit from a visual representation of borehole temperatures over time, ideally aligned with the ERT measurement periods and on the north/ south-facing slope. This could include a figure showing active layer thickness evolution over the years.

You presented two calibration curves from the laboratory testing – please clarify which one (and why) was used to estimate temperature. In Figure 14, estimated temperatures below 2 meters are missing, although Figure 13 shows that 13 resistivity values were extracted from profile P1. Why are these data points excluded? Also, consider adjusting the x-axis of Figure 14; the current range (-10°C to +10°C) is unnecessarily broad.

In Line 553, you mention that individually adapted inversion parameters can improve temperature estimation – why does not show a comparison to demonstrate this? Lastly, when comparing laboratory and field-derived resistivity–temperature relationships, please account for differences in the penetration depth of the current signal.

Hydrological dynamics: To confidently interpret water pressures, appropriate measurements must be presented, as in Offer et al. (2025) or Scandroglio et al. (2025). Water pressure is influenced not only by the water table elevation but can also result from frozen clefts or permafrost-related constraints. Given that hydrological dynamics are listed among the study’s main objectives, their treatment in the manuscript is limited. Either revise the stated objectives or expand the analysis and discussion of these processes.

Conclusion: (L630-631) We believe it is important to clarify in the manuscript that the measured resistivity signals on the north- and south-facing sides are similar, and that the main difference lies in your interpretation – namely, attributing high resistivity to surface drying in one case and to the presence of permafrost in the other.

Additionally, can it truly be claimed that subsurface temperature can be "accurately" (L632) derived from ERT measurements using the applied petrophysical models? While the manuscript suggests a potential precision of 1°C, Figure 15 reveals discrepancies of up to ~5°C between laboratory-based and field-derived estimates. Even a precision of 1°C would be substantial in the context of permafrost studies, where internal temperatures often lie just a few degrees below freezing (e.g., Noetzli et al., 2024), and small changes can have major implications for stability and long-term thermal evolution.

What about depths from 0-4 m (L634-635) – which are probably most relevant when assessing the progressive deepening of the active layer?

Technical corrections:
-Fig 1a: the colors representing the mean annual temperature are not readable in the figure
-L26: permafrost “rocks”…
-L32: to get “temporal” variations
-L51: degradation of permafrost(?)
-L53: affected by (rather use another verb, as it is a repetition with the next sentence)
-L55: Offer et al. 2025 is not the appropriate study for rockfall monitoring, use Hartmeyer et al. 2020 instead
-L59: Mamot et al. 2018 would be good to include here
-L85: the wording “in the last few years” is not in accordance with the used reference of the year 2010. This is now more than 15 years ago and not only a few years…
- L173: title of the section à “Electrical resistivity-temperature relationship” (in the section you don’t show conductivity results)
-L203: what is the snow melt water conductivity of the field site?
-Fig. 4: electrode number
-L293: three times “data” in a short sentence is a bit overwhelming
-Table 1: no necessary information à consider to put it in the appendix
-L314-317: please reformulate– it is unclear what you want to express
-L356-357: please reformulate – it is unclear what you want to express

Etzelmüller, B., Czekirda, J., Magnin, F., Duvillard, P.-A., Ravanel, L., Malet, E., Aspaas, A., Kristensen, L., Skrede, I., Majala, G. D., Jacobs, B., Leinauer, J., Hauck, C., Hilbich, C., Böhme, M., Hermanns, R., Eriksen, H. Ø., Lauknes, T. R., Krautblatter, M., and Westermann, S.: Permafrost in monitored unstable rock slopes in Norway – new insights from temperature and surface velocity measurements, geophysical surveying, and ground temperature modelling, Earth Surf. Dynam., 10, 97–129, https://doi.org/10.5194/esurf-10-97-2022, 2022.
Hartmeyer, I., Delleske, R., Keuschnig, M., Krautblatter, M., Lang, A., Schrott, L., and Otto, J.-C.: Current glacier recession causes significant rockfall increase: the immediate paraglacial response of deglaciating cirque walls, Earth Surf. Dynam., 8, 729–751, https://doi.org/10.5194/esurf-8-729-2020, 2020.
Herring, T., Lewkowicz, A. G., Hauck, C., Hilbich, C., Mollaret, C., Oldenborger, G. A., Uhlemann, S., Farzamian, M., Calmels, F., and Scandroglio, R.: Best practices for using electrical resistivity tomography to investigate permafrost, Permafrost Periglac., 34, 494–512, https://doi.org/10.1002/ppp.2207, 2023.
Keuschnig, M., Krautblatter, M., Hartmeyer, I., Fuss, C., and Schrott, L.: Automated electrical resistivity tomography testing for early Warning in unstable permafrost rock walls around alpine infrastructure, Permafrost Periglac., 28, 158–171, https://doi.org/10.1002/ppp.1916, 2017.
Krautblatter, M., Verleysdonk, S., Flores-Orozco, A., and Kemna, A.: Temperature-calibrated imaging of seasonal changes in permafrost rock walls by quantitative electrical resistivity tomography (Zugspitze, German/Austrian Alps), J. Geophys. Res., 115, F02003, https://doi.org/10.1029/2008JF001209, 2010
Magnin, F., Krautblatter, M., Deline, P., Ravanel, L., Malet, E., and Bevington, A.: Determination of warm, sensitive permafrost areas in near–vertical rockwalls and evaluation of distributed models by electrical resistivity tomography, J. Geophys. Res.-Earth, 120, 745–762, https://doi.org/10.1002/2014JF003351, 2015.
Mollaret, C., Hilbich, C., Pellet, C., Flores-Orozco, A., Delaloye, R., and Hauck, C.: Mountain permafrost degradation documented through a network of permanent electrical resistivity tomography sites, The Cryosphere, 13, 2557–2578, https://doi.org/10.5194/tc-13-2557-2019, 2019.
Noetzli, J., Isaksen, K., Barnett, J. et al. Enhanced warming of European mountain permafrost in the early 21st century. Nat Commun 15, 10508 (2024). https://doi.org/10.1038/s41467-024-54831-9.
Offer, M., Weber, S., Krautblatter, M., Hartmeyer, I., and Keuschnig, M.: Pressurised water flow in fractured permafrost rocks revealed by borehole temperature, electrical resistivity tomography, and piezometric pressure, The Cryosphere, 19, 485–506, https://doi.org/10.5194/tc-19-485 2025,  2025.
Scandroglio, R., Draebing, D., Offer, M., and Krautblatter, M.: 4D quantification of alpine permafrost degradation in steep rock walls using a laboratory–calibrated electrical resistivity tomography approach, Near Surface Geophys., 19, 241–260, https://doi.org/10.1002/nsg.12149, 2021.
Scandroglio, R., Weber, S., Rehm, T., and Krautblatter, M.: Decadal in situ hydrological observations and empirical modeling of pressure head in a high-alpine, fractured calcareous rock slope, Earth Surf. Dynam., 13, 295–314, https://doi.org/10.5194/esurf-13-295-2025,  2025.
Citation: https://doi.org/10.5194/egusphere-2025-637-RC1
- AC2: 'Reply on RC1', Feras Abdulsamad, 18 Jul 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-637/egusphere-2025-637-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-637-AC2
RC2:
'Comment on egusphere-2025-637', Anonymous Referee #2, 23 Apr 2025

In the attachment, you will find all my detailed comments.
All the best and good luck.

Citation: https://doi.org/10.5194/egusphere-2025-637-RC2
- AC1: 'Reply on RC2', Feras Abdulsamad, 18 Jul 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-637/egusphere-2025-637-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-637-AC1
EC1:
'Editor comment on egusphere-2025-637', Teddi Herring, 01 May 2025

Please note that some of the comments from Reviewer 2 are not in accordance with The Cryosphere’s guidelines of constructive criticism within a review. Feedback is expected to be constructive and professional.

Citation: https://doi.org/10.5194/egusphere-2025-637-EC1
- AC3: 'Reply on EC1', Feras Abdulsamad, 18 Jul 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-637/egusphere-2025-637-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-637-AC3

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Short summary

Permafrost dynamics at Aiguille du Midi in the French Alps was investigated using Automated Electrical Resistivity Tomography (A-ERT) during four years. A-ERT reveals seasonal and multi-year permafrost changes. Temperatures estimated using resistivity measurements provide a good agreement with measured temperature in borehole in frozen zone. Variations in active layer thickness across different faces were observed, along with a slight decrease in permafrost resistivity suggesting warming.


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