the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Nov 2025
Field-validated imaging of decadal and seasonal changes in permafrost bedrock using quantitative electrical resistivity tomography (Zugspitze, Germany/Austria)
Abstract. Ongoing permafrost degradation in alpine regions requires monitoring methods that accurately decipher spatial and temporal dynamics. Electrical resistivity tomography (ERT) is widely applied in bedrock permafrost, yet its outputs are often interpreted only qualitatively. Quantitative evaluation of ERT results, however, is crucial for improving process understanding and enhancing predictions of permafrost-related slope instability. In this study, we present a 17-year monitoring of permafrost rock slopes on Mount Zugspitze (Germany/Austria) with monthly ERT campaigns. ERT data are combined with rock temperatures at four depths to establish field-based temperature–resistivity calibrations and to validate existing laboratory-derived relations. Both approaches agree well in the freezing range; however, field calibrations tend to yield higher resistivities at subzero temperatures and reveal substantial spatial heterogeneity. Incorporating reciprocal measurements refines the existing error model, increases image resolution, and improves the identification of subsurface features. Over ten years, the measured rock temperature increased by 1 °C, accompanied by a 25 % decrease in resistivity. The permanently frozen surface decreased by 40 %, with degradation rated up to −4.2 kΩmy−1. Extrapolating these trends would result in the loss of 65 % of permafrost within a decade. Thermal forcing controls the degradation; however, the observed conditions and projected increases in heatwaves suggest that newly unfrozen and connected fracture networks will enhance advective heat transfer. This is expected to accelerate permafrost thawing, thereby increasing the risk of slope instability. With these results, we demonstrate that ERT monitoring can yield high-quality quantitative insights into long-term permafrost evolution and effectively track bedrock permafrost degradation across both decadal and seasonal timescales.
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RC1: 'Comment on egusphere-2025-5552', Anonymous Referee #1, 04 Dec 2025
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AC1: 'Reply on RC1', Riccardo Scandroglio, 13 Feb 2026
Dear Reviewer,
Thank you very much for taking the time to review our manuscript entitled: “Field-validated imaging of decadal and seasonal changes in permafrost bedrock using quantitative electrical resistivity tomography (Zugspitze, Germany/Austria)”.
We greatly appreciate your constructive feedback and the detailed list of improvements provided. We will address all points raised in the revised version of the manuscript.
Best regards,
Riccardo Scandroglio, on behalf of all authors.
Citation: https://doi.org/10.5194/egusphere-2025-5552-AC1 -
AC2: 'Reply on RC1', Riccardo Scandroglio, 13 Feb 2026
Dear Reviewer,
Thank you very much for taking the time to review our manuscript entitled: “Field-validated imaging of decadal and seasonal changes in permafrost bedrock using quantitative electrical resistivity tomography (Zugspitze, Germany/Austria)”.
We greatly appreciate your constructive feedback and the detailed list of improvements provided. We will address all points raised in the revised version of the manuscript.
Best regards,
Riccardo Scandroglio, on behalf of all authors.
Citation: https://doi.org/10.5194/egusphere-2025-5552-AC2
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AC1: 'Reply on RC1', Riccardo Scandroglio, 13 Feb 2026
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RC2: 'Comment on egusphere-2025-5552', Anonymous Referee #2, 11 Feb 2026
Review of "Field-validated imaging of decadal and seasonal changes in permafrost bedrock using quantitative electrical resistivity tomography (Zugspitze, Germany/Austria)" by Scandroglio et al.
Summary and General Comments: The authors present an impressive and highly valuable 17-year dataset of monthly electrical resistivity tomography (ERT) measurements taken from the Kammstollen tunnel on Mount Zugspitze. The logistical effort required to maintain this monitoring in a high-alpine environment is commendable, and the dataset itself represents a significant contribution to the study of mountain permafrost dynamics.
However, despite the exceptional quality of the underlying data, the manuscript in its current form suffers from critical methodological omissions and a disjointed narrative structure. The paper reads as a collection of disparate analyses, ranging from updating inversion error models to forecasting permafrost degradation, without a clear, cohesive scientific message. Most concerningly, the methodological descriptions regarding ERT data processing, time-lapse inversion strategies, and spatial data extraction are entirely insufficient to reproduce or validate the presented results.
Because the core conclusions of the paper rely on these undocumented inversion and clustering steps, I recommend rejection at this time. I strongly encourage the authors to thoroughly revise their methodology and focus their narrative, as this dataset absolutely warrants publication once these issues are addressed.
Major Comments:
1. Lack of ERT Data Processing Details: For a manuscript whose primary findings rely almost entirely on ERT measurements, the description of the data processing is extremely brief. The authors state only that data were filtered using the variance of resistance to remove systematic errors. There is no information provided regarding data exclusion thresholds, contact resistance issues, or the percentage of data retained per dataset. These details are critical for assessing the quality of the raw data before inversion.
2. Missing Time-Lapse Inversion Strategy: The authors note that an updated version of CRTomo was used, but they completely omit their time-lapse inversion strategy. Time-lapse ERT is highly sensitive to the chosen regularization approach (e.g., independent inversions, difference inversion, or fully 4D spatio-temporal regularization). Without knowing how the temporal constraints were applied, it is impossible to determine whether the reported 25% decrease in resistivity is a robust physical trend or an artifact of the inversion process.
3. Ambiguity in the Temperature-Resistivity (T-rho) Comparison: The methodology linking ERT-derived resistivities to measured borehole temperatures is unclear. The text states: "Values from the side tunnel (Transect A-A') were used for the field calibration together with values of rho extracted from raw data, as normally done in laboratory calibrations".
- Does "raw data" refer to apparent resistivity or inverted cell resistivity?
- If inverted data were used, how were the cells extracted relative to the temperature sensors? Was a nearest-neighbour approach used, or an average of surrounding cells? The spatial scaling between a 1D point sensor and a 2D ERT grid should be explicitly defined.
4. K-means Clustering and Missing Uncertainty bounds: The authors apply a k-means clustering algorithm to segment the tomograms. However, there is no justification for why k-means was selected over other clustering methods. Furthermore, the authors acknowledge that "clustering is not unique, since multiple solutions are possible". Despite this non-uniqueness, the clustered results are used to generate highly specific, deterministic volume estimates (e.g., estimating 40,000 to 60,000 m³ of frozen rock, and projecting the loss of 39,000 m³ in the next decade). I find deriving these volumetric metrics without assigning any confidence intervals or uncertainty bounds quite dangerous.
5. Overemphasis on the Error Model: One of the main focal points of the paper is the application of an updated error model. While proper error weighting is important for image resolution, it distracts from the physical findings of the paper. A range of existing literature has already demonstrated the impact of error models on inversion results. Elevating this to a core research question fragments the narrative, especially given the lack of detail on the broader inversion strategy. This discussion would be better suited as a methodological footnote or moved to the supplementary material.
6. Unclear Manuscript Goals: The paper lacks a clear "red line." The introduction lists four distinct research questions, but the manuscript struggles to synthesize them into a unified conclusion. The authors should decide what the primary message of this paper is, is it a methodological paper about field calibration and error models, or is it a geomorphological paper about climate-driven permafrost degradation? Streamlining the narrative will greatly improve the manuscript's impact.
Although I'm quite critical on the paper in it's current state, I would like to emphasize that the underlying data set is very impressive and should be published. I hope that my comments are valuable in improving the paper.
You will find specific comments in the attached, annotated manuscript.
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AC3: 'Reply on RC2', Riccardo Scandroglio, 13 Feb 2026
Dear Reviewer,
Thank you very much for taking the time to review our manuscript entitled: “Field-validated imaging of decadal and seasonal changes in permafrost bedrock using quantitative electrical resistivity tomography (Zugspitze, Germany/Austria)”.
We genuinely appreciate your detailed and constructive feedback. We are confident that addressing your comments will significantly improve the manuscript's quality.
The core objective of this paper remains the detection and quantification of permafrost degradation. This study is tightly connected to the previous work of Krautblatter et al. (2010 – surely well known to the reviewer) for most methodological details, that are therefore not repeated here. Still, we agree that adding specific information regarding processing, inversion, and clustering will enhance the transparency and reproducibility of our study.
Major Comments:
1. Lack of ERT Data Processing Details: For a manuscript whose primary findings rely almost entirely on ERT measurements, the description of the data processing is extremely brief. The authors state only that data were filtered using the variance of resistance to remove systematic errors. There is no information provided regarding data exclusion thresholds, contact resistance issues, or the percentage of data retained per dataset. These details are critical for assessing the quality of the raw data before inversion.
Answer 1) We aimed at maintaining brief data processing, since most of the data processing is consistent with the previous study (Krautblatter et al, 2010), but we will be happy to improve this section. The required information (e.g. data exclusion threshold and contact resistances) can be easily provided. The percentage of retained data per dataset can be obtained from the lower graph in Fig. 1d, where we will provide a proper axis label.
2. Missing Time-Lapse Inversion Strategy: The authors note that an updated version of CRTomo was used, but they completely omit their time-lapse inversion strategy. Time-lapse ERT is highly sensitive to the chosen regularization approach (e.g., independent inversions, difference inversion, or fully 4D spatio-temporal regularization). Without knowing how the temporal constraints were applied, it is impossible to determine whether the reported 25% decrease in resistivity is a robust physical trend or an artifact of the inversion process.
Answer 2) Very good point, indeed the time-lapse strategy can have strong effects, as shown in Scandroglio et al, 2021. Similar to the previous point, we kept the strategy of Krautblatter et al (2010) and used independent inversions. Therefore, this information is missing but will be added in the revised manuscript.
3. Ambiguity in the Temperature-Resistivity (T-rho) Comparison: The methodology linking ERT-derived resistivities to measured borehole temperatures is unclear. The text states: "Values from the side tunnel (Transect A-A') were used for the field calibration together with values of rho extracted from raw data, as normally done in laboratory calibrations".
Does "raw data" refer to apparent resistivity or inverted cell resistivity?
If inverted data were used, how were the cells extracted relative to the temperature sensors? Was a nearest-neighbour approach used, or an average of surrounding cells? The spatial scaling between a 1D point sensor and a 2D ERT grid should be explicitly defined.
Answer 3) This technical information can be provided in a short additional paragraph.
4. K-means Clustering and Missing Uncertainty bounds: The authors apply a k-means clustering algorithm to segment the tomograms. However, there is no justification for why k-means was selected over other clustering methods. Furthermore, the authors acknowledge that "clustering is not unique, since multiple solutions are possible". Despite this non-uniqueness, the clustered results are used to generate highly specific, deterministic volume estimates (e.g., estimating 40,000 to 60,000 m³ of frozen rock, and projecting the loss of 39,000 m³ in the next decade). I find deriving these volumetric metrics without assigning any confidence intervals or uncertainty bounds quite dangerous.
Answer 4) The aim of this paper is not to discuss clustering strategies and different algorithms; therefore, a standard algorithm was selected (included in the Matlab basic packages). Confidence intervals are not computable with the “hard” k-means algorithms but could be obtained with other algorithms (e.g. “soft” k-means). An implementation seems easily feasible in a short time.
5. Overemphasis on the Error Model: One of the main focal points of the paper is the application of an updated error model. While proper error weighting is important for image resolution, it distracts from the physical findings of the paper. A range of existing literature has already demonstrated the impact of error models on inversion results. Elevating this to a core research question fragments the narrative, especially given the lack of detail on the broader inversion strategy. This discussion would be better suited as a methodological footnote or moved to the supplementary material.
Answer 5) Emphasis on the error model can be reduced or moved to supplementary material.
6. Unclear Manuscript Goals: The paper lacks a clear "red line." The introduction lists four distinct research questions, but the manuscript struggles to synthesize them into a unified conclusion. The authors should decide what the primary message of this paper is, is it a methodological paper about field calibration and error models, or is it a geomorphological paper about climate-driven permafrost degradation? Streamlining the narrative will greatly improve the manuscript's impact.
Answer 6) We believe that with the suggested improvements, the “red line” will be more visible. The focus is and will remain the climate-driven permafrost degradation, but – as the reviewer also underlined – it is also important to provide precise details on data analysis.
Based on these answers, we kindly ask the editor for the opportunity to submit a revised manuscript addressing all points raised. The suggested revisions can be implemented promptly, as many of these points have already been discussed and proved during our internal review phase.
Best regards,
Riccardo Scandroglio, on behalf of all authors.
Citation: https://doi.org/10.5194/egusphere-2025-5552-AC3
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