the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
25 Aug 2021
25 Aug 2021
Towards accurate quantification of ice content in permafrost of the Central Andes – Part II: an upscaling strategy of geophysical measurements to the catchment scale at two study sites
- 1University of Fribourg, Department of Geosciences, Switzerland
- 2BGC Engineering Inc., Canada
- 1University of Fribourg, Department of Geosciences, Switzerland
- 2BGC Engineering Inc., Canada
Abstract. With ongoing climate change, there is a pressing need to better understand how much water is stored as ground ice in areas with extensive permafrost occurrence and how the regional water balance may alter in response to the potential generation of melt water from permafrost degradation. However, field-based data on permafrost in remote and mountainous areas such as the South-American Andes is scarce and most current ground ice estimates are based on broadly generalised assumptions such as volume-area scaling and mean ground ice content estimates of rock glaciers. In addition, ground ice contents in permafrost areas outside of rock glaciers are usually not considered, resulting in a significant uncertainty regarding the volume of ground ice in the Andes, and its hydrological role. In part I of this contribution, Hilbich et al. (submitted) present an extensive geophysical data set based on Electrical Resistivity Tomography (ERT) and Refraction Seismic Tomography (RST) surveys to detect and quantify ground ice of different landforms and surface types in several study regions in the semi-arid Andes of Chile and Argentina with the aim to contribute to the reduction of this data scarcity. In part II we focus on the development of a methodology for the upscaling of geophysical-based ground ice quantification to an entire catchment to estimate the total ground ice volume (and its estimated water equivalent) in the study areas. In addition to the geophysical data, the upscaling approach is based on a permafrost distribution model and classifications of surface and landform types. Where available, ERT and RST measurements were quantitatively combined to estimate the volumetric ground ice content using petrophysical relationships within the Four Phase Model (Hauck et al., 2011). In addition to introducing our upscaling methodology, we demonstrate that the estimation of large-scale ground ice volumes can be improved by including (i) non-rock glacier permafrost occurrences, and (ii) field evidence through a large number of geophysical surveys and ground truthing information. The results of our study indicate, that (i) conventional ground ice estimates for rock-glacier dominated catchments without in-situ data may significantly overestimate ground ice contents, and (ii) substantial volumes of ground ice may also be present in catchments where rock glaciers are lacking.
Tamara Mathys et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2021-251', Anonymous Referee #1, 12 Oct 2021
General comments:
The manuscript submitted by Mathys et al. describes a new method to extrapolate the quantification of ice contents in permafrost areas based on another paper submitted. By the combination of multiple observations, modelling tools, remote sensing data, they evaluated the ice contents in study sites in the Central Andes. This quantification is helpful for the scientific community to better understand hydrological processes occurring in permafrost-affected catchments.
I think the paper is suitable for publication in The Cryosphere with the condition that the paper explaining the methodology is published as this manuscript fully depends on Hiblich et al. paper. Moreover, I do have some specific comments that may improve the quality of the manuscript.
Specific comments:Figure 2: It is hard to link the figure with what is described in the text. I would recommend adding some information on the figure to help the reader follow the different steps.
L143-145: I suggest to split the sentence into two separate sentences.
In this way, I found multiple long sentences in the manuscript and splitting them would help the reader.
L223-225: “Figure 3, 1a,b” and “Figure 3, 2a,b” are hard to understand. Please clarify.
Figure 5: I do not see where the text refer to Figure 5. If it is not cited, it should be moved to the Appendix section or removed. Moreover, the figures and the tables within Figure 5 are hard to read.
L253-255: This should go in the Discussion section.
L312-313: Parentheses are doubled. Please correct.
L332-335: I would suggest to move this sentence to the discussion section.
L335-338: These results are already mentioned in the previous paragraph.
Figure 7: It is not clear that “rock glacier dominated catchment” cover the four groups above.
Figure 8: The large uncertainties in the calculations of ice content revealed by this figure are not enough discussed in the discussion section. I would recommend to add information regarding this uncertainty so the reader can understand how the method presented in this paper improves the quantification of ice contents in permafrost areas.
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RC2: 'Comment on tc-2021-251', Anonymous Referee #2, 20 Nov 2021
Review: Towards accurate quantification of ice content in permafrost of the Central Andes - Part II: an upscaling strategy of geophysical measurements to the catchment scale at two study sites
This paper aims at developing a methodology to scale local, geophysics-derived estimates of ground ice content to a subcatchment scale. The study is part of a project that uses geophysical data to estimate ground-ice in an area of the Andes. This work is currently under review as the first part of this study.
Similar upscaling attempts have been shown to be successful, but were mostly applied to high-latitude environments, whereas this study is considering a high mountain array. Hence, the authors make use of geomorphological data and field observations.
As the authors demonstrate, estimating ground ice content of high altitude, headwater environments is important to assess groundwater resources further downstream, yet a quantitative estimation of this parameter is difficult. Here, the authors build on geophysical data, presented as Part I of this study, to estimate the ground ice content throughout a wider area. By using various input parameters to classify their sites, the authors are able to provide quantitative estimates of ground ice content. While the approach is very interesting, the classification, which forms an integral part of the study, seems poorly constrained, and mostly qualitative. The authors repeat much detail of the geophysical characterization (which is fair, given that this is the most important data set), there is very little detail on the actual classification. No maps are shown that show the other input parameters, such as slope angle, aspect, geomorphology, or the estimated soil parameters, including locations of soil probing, making it impossible to follow or understand how class parameters vary and how they were decided on. Similarly, it is not clear how the parameters that are critical for the ice content calculations (thickness, area, ice content) were upscaled, or determined, particularly for areas without geophysical data. It would be great to also see those as maps.
These limitations of the current manuscript makes it difficult to understand what the benefit of the approach is. Comparing Figures 7 and 8, the shown difference between the geophysical based estimate and the empirical approach, could well fall within the uncertainties introduced by using different classifications. Given the strong reliance on field observations, it is also questionable whether similar approaches could be used more widely to estimate ground ice contents.
Next to those rather major comments, please find below some more minor comments:
Line 14-15: I don't think that an abstract should contain references, and I wonder whether the detail on the geophysics is actually needed here, as this paper focuses on the upscaling, not the geophysics.
Line 89: In a previous sentence you mention that line locations were planned based on "safety within the mines". Does this mean that the chosen sites are active mining sites, and hence not in their natural state? That would make upscaling to natural systems impossible. According to Fig. 1, sub area 1 seems to be located within active mining, whereas others seem outside. I think some more detail is needed here on what the impact of mining on the chosen sites is to justify that mining has no impact on the results.
Line 126: “comparable near-surface substrate [...]” This is a critical assumption for the upscaling, yet the authors do not provide information on the geology and the variability of subsurface properties.
Line 154: Potential incoming solar radiation: How and based on what did you calculate that?
Line 155: Equation for estimating permafrost occurrence: It would be good to show a figure that shows the data and model fit, and also details the parameters of the model.
Line 172-173: what do you mean by "high bedrock slopes"?
Line 186-187: On what data is this threshold based on?
Line 197-199: Although you describe the input parameters, there is no clear methodology described here on how you define the classes. This needs more detail and justification.
Line 202: Given that soil properties will also impact on the ground temperature distributions, shouldn't the soil stratigraphy be an input to the classification?
Line 295 - 298: Given that the scope of your work is upscaling, why do you distinguish areas where geophysical data has been acquired and areas where this has not been done?
Line 320 - 321: It would be great to see the estimated ground ice content as a map.
Discussion: The discussion on the geophysical results should be mentioned, but not in that much detail, as it should be part of Part 1 of this study. The uncertainty in the classification is of greater importance.
Line 390: I don't think that this is necessarily an image classification problem. But you could use machine learning to exploit relationships between surface and subsurface parameters.
Figure 4: You prescribe ice-poor bedrock of D02 with an ice content of 4%, and bedrock of A15, which is overlain by ice-rich material with an ice content of 0%. How did you define that? Similar for Fig. 5, where the 4PM model indicates higher ice contents. Does the bedrock geology play a role in your definition of the ice content. If so, shouldn't this be an input to the classification?
Tamara Mathys et al.
Tamara Mathys et al.
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