the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow mechanical properties variability at the slope scale, implication for snow mechanical modeling
Francis Gauthier
Alexandre Langlois
Abstract. Snow avalanches represent a natural hazard for infrastructures and backcountry recreationists. Risk assessment of avalanche danger is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to the decision-making and route finding in avalanche terrain for mountain users. Snow cover models simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical given that slope stability and the possible size of an avalanche are governed by such scale. In order to better understand and predict the spatial variability at the slope scale, this work explores existing linkages between snow mechanical properties and microtopographic indicators. First, we compared covariance models and scaling properties. Then, we predicted snow mechanical properties, including point snow stability, using GAM spatial models (Generalized Additives Models) with microtopographic indicators as covariates. Snow mechanical properties such as snow density, elastic modulus, shear modulus and snow microstructural strength were measured at multiple locations over several studied slopes using a high-resolution snow penetrometer (SMP), in Rogers Pass, British-Columbia, and Mt Albert, Québec. Point snow stability such as the skier crack length, critical propagation crack length and a skier stability index were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators such as the topographic position index (TPI), vegetation height and proximity, Up-wind slope index (wind exposed/sheltered area) and potential radiation index were derived from Unmanned Aerial Vehicles (UAV) surveys with sub-meter resolution. We computed the variogram and log-log variogram of snow mechanical properties. The comparison showed some similarities in correlation distances fractal dimensions between the slab depth and slab snow density and also between the weak layer microstructural strength and the stability metrics. GAM models suggested several significant covariates such as TPI, VRM, Winstral index, vegetation height and distance to vegetation. The point snow stability maps generated represents good teaching material in avalanche skill training and awareness course. The difference in spatial pattern between the slab and the weak layer should be considered in snow mechanical modeling.
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Francis Meloche et al.
Status: open (until 21 Oct 2023)
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RC1: 'Comment on egusphere-2023-1586', Anonymous Referee #1, 18 Aug 2023
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General comments
This paper explores slope-scale patterns in snowpack stability. Four field surveys were conducted at different locations where snowpack properties were measured with a snow microprenetrometer and terrain properties were measured with a UAV. Three snowpack properties (slab depth, slab density, weak layer strength) and three stability indices (skier crack length, critical crack length, skier stability index) were derived from the SMP measurements, and their spatial patterns were explored with variogram analyses. Trrrain properties (slope angle, convexity, etc.) were used to fit regression models to predict stability patterns across the slopes and explore which terrain factors were most influential. The results suggest slab properties were more variable than weak layer properties and recommend ways slab variability could be accounted for in mechanical models of avalanche release.
The study is well designed, relevant, and interesting; however, I think its presentation needs to be improved before publication in the Cryosphere. Some of the methods and concepts are not described in sufficient detail, the use of terminology and symbols should be more consistent and organized, and the overall contribution and relevance of the study should be clarified.
Specific comments
- Novelty of research methods. Line 105 states “no studies have linked snow stability and mechanical properties with microtopography indicators in spatial modeling”, but I would argue that Reuter et al. (2016) perform a similar study where SMP data was used to spatially predict a failure initialization criteria and critical crack length based on terrain and snowpack data. While the specific properties and terrain predictors differ, as do the type of regression models, the methods are conceptually quite similar. Sect 4.3 of that study specifically discusses spatial prediction of stability indices. I think the similarities and differences between this study and previous studies needs to be clearer in the Introduction (several distinctions are made throughout the section, but not presented in a complete succinct way that links to their objective), and any relevant comparisons with past studies should be added to the Discussion.
- Incomplete methods. Methods section 2.5 does not describe how the covariates were derived or how the GAM models were fit to the data in enough detail to reproduce the study. The technical comments below list some specific examples.
- Description of terrain variables. The microtopographic indicators (covariates) are not sufficiently described. The topographic position index and vector ruggedness measure are not common terms used to describe avalanche terrain and should be described with plain language interpretations. It’s difficult to interpret why these were significant explanatory variables without understanding what they represent. Similarly, some of the other terrain variables are not described in enough detail to understand how they were derived or how to interpret them (e.g., wind-exposure index).
- Relating results to terrain/snowpack influences. A strength of this study is that it was conducted at multiple sites with different terrain and snowpack characteristics. I think the results could be more impactful if the influence of these characteristics were discussed in more detail. For example, what were the main differences between the wind-exposed versus forested slopes and persistent versus non-persistent weak layer grains? Understanding how these factors influence slope-scale variability would be directly relevant to avalanche risk management.
- Consistency and organization of terms and symbols. In general, there were quite a few places where consistent and complete use of terminology and symbols needs to be improved. Many examples are provided below.
Technical comments
Abstract/Introduction
- Line 4: True in some contexts, but “can simulate with good accuracy” is better.
- Line 11: These were not “measured” on the slopes but estimated from SMP measurements.
- Lines 8-19: Some of these sentences are a little vague “models suggested significant covariates”) and would benefit with being a little more specific about what types of variables were included in various parts of the study (e.g., “covariance models and scaling properties”) and some plain language interpretations (e.g., what does it mean that “GAM models suggest significant covariates”?).
- Line 19: Winstral index as not defined in the abstract, so perhaps use wind-exposure index.
- Lines 26-27: Perhaps more general triggers such as “person” instead of “skier” and “stresses from snowfall or warming” instead of just “new snowfall”.
- Line 30: The conceptual model decomposes hazard into 4, not 2, factors (problem type, location, size, likelihood).
- Line 44: Is there a word like “depth” missing in “spatial pattern of snow”?
- Line 48: Can you describe what is meant by “roughness” in a way that links the concept to avalanche release? The interpretation of the fractal distances is unclear in the results.
- Line 52: Start new paragraph here?
- Line 110: Can you briefly describe this “knockdown effect”?
Methods
- Line 127: “receives” instead of “received”.
- Lines 131-136: Please provide consistent details for each site. For example, the text for the site in Quebec does not name it Arete de Roc or provide the abbreviation AR used later in the manuscript, no slope angle is provided for JBC, and shouldn’t “the other site” in line 131 be “the first site”?
- Fig 1: Very nice images to illustrate the study sites. Please add the word “survey” prior to green and red in the caption for consistency.
- Line 165: Provide a bit more detail about the weak layer criteria. It sounds like one weak layer was identified for each survey, was this the uppermost result in a compression test of any fracture character, the uppermost result with a sudden fracture character, an expert interpretation of the primary layer of concern, or something else?
- Line 167: Please clarify if the winter imagery was collected on the same day as the survey.
- Line 181: I would consider layer depth, thickness, and density to be structural rather than mechanical properties.
- Line 183: Missing “density” between slab and rho.
- Line 187: Out of curiosity, does this method of averaging the density of each slab layer account for the varying thicknesses of these layers so that it would be conceptually the same as a bulk density measurement made with a sampling tube, or is this a more abstract slab density?
- Line 191: State “… shear strength of the weak layer…” so it is clear this is in reference to how you will derive tau_p.
- Line 194: Macroscale strength is not defined or explained anywhere, so the justification for this assumption is unclear.
- Fig 2: This figure is helpful but could potentially be simplified with a bit less text (e.g., green boxes) and more consistent formatting (has a mix of serif and sans serif fonts and sizes, bold and non-bold font, why is some text red?).
- Line 201: You could consider just saying “the SPI is the ratio of two lengths” rather than “defined by”.
- Line 207: It’s not clear to me what “the surface beneath the skier” refers to in the definition of alpha.
- Eq 6: Missing right bracket at the end of the numerator.
- Lines 262-264: This sentence is confusing and perhaps belongs later in this section. Aren’t the microtopographic indicators defined by more than the second order derivates as listed in Table 1? And it’s not clear how these moving windows are applied or relevant to the analysis.
- Sect 2.4: The fitting of spherical and gaussian variogram models should be described here since they are discussed in the results. Also, the results suggest you pick the best fitting model.
- Sect 2.5.1 and Table 1: Some of the microtopographic indicators could be defined more clearly. Specifically, TPI and VRM should have plain language descriptions because they are not everyday terms used to characterize avalanche terrain with intuitive meanings. How should canopy height be interpreted if you masked areas with vegetation? How are the concepts of “potential of incoming solar radiation” and “Winstral index” quantified? How was prevailing wind direction determined? What is meant by moving windows represented with two values such as 5/15 and 25/50?
- Line 284: The symbol Sx has already been used to describe a slab layer (line 177).
- Sect 2.5.2: This section is not clear what data is used to fit GAM models. My interpretation is that Y is the 6 properties previously analyzed and the X are the ~13 covariates listed in Table 1. I also assume the model was fit (and cross-validated) using data from the 60-80 SMP profile locations, but this is not stated. While the concepts behind the statistical modelling are explained well, it should be clearer and more explicit how they were applied to this data.
- Eq 12: The asterisk for multiplication is not necessary.
Results
- Fig 3: It would help if the 4 surveys were presented in a consistent order throughout the paper (methods, table 2, figures, etc.). The y-axis is not labelled.
- Table 2: Based on the methods, 3 x 2 = 6 compression tests were done with each survey, so why is only a single test reported. Since the tests were performed following Canadian Avalanche Association (2016), they should also be reported following those standards: “CTM 15 (RP) down 25”. How was ac_PST derived from PST test results? These don’t seem like cut lengths from a 100 cm long column. The mix of words and symbols in the column headings is confusing, I suggest using words. Units can be specified in the column headings. Consider separate columns for slab depth and density. Dates should probably be in YYYY-mm-dd format.
- Line 311: Are the lengths reported for each weak layer the (average) observed grain size with a crystal screen and loupe or the thicknesses derived from SMP measurements?
- Line 315: What is meant by the slab is made up of one layer? Doesn’t the SMP identify very thin layers?
- Line 340: “slab thickness” used here but referred to as “slab depth” in other parts of the manuscript. Check manuscript for consistency.
- Line 340: Is there any relevant interpretation to gaussian versus spherical variogram models?
- Fig 3: Interesting that AR had some longer correlation lengths given it sounds like it was the most wind exposed site.
- Line 353: “surface roughness” could be misinterpreted to mean the physical texture of the snow surface, which is why I think the interpretation of fractal distances needs to be explained. What does a value of 2.7 mean?
- Line 360: Please be more specific about what variable or property the “variance” refers to.
- Fig 6/7: Please explain the grey vegetation in the caption. Consider presenting the RMSE and MAE as rounded values with units to improve interpretability. The prefix “CV” is unnecessary. In general, these are very interesting figures and I agree could be valuable teaching material.
- Line 368: “same” or “similar” variation?
- Line 370: This sentence is confusing and partly contradictory.
- Line 378: This could be the start of a new subsection on microtopographic indicators.
- Table 2 and 3 are not cited in the text. The asterisks next to covariates are not defined, but I assume refer to significance levels.
- Table 2/3: Interesting that the wind exposure index Sx was more frequent for the models at the Fidelity sites than the AR site which was apparently more wind exposed. This result could be better understood of the derivation of Sx was explained better.
- Fig 8: What is meant by “pondered” in the caption. Consider vertical gridlines to make it easier to align the labels with the upper chart.
Discussion
- Line 388: Again, “variance” of what variables?
- Line 395: Should this be “< 0.5”?
- Line 401: Consider “slope angle” instead of just “slope”.
- Lines 402-406: These interpretations of TPD and VRM are difficult to understand when these variables have not been described in plain language.
- Lines 408-434: These seem to be new results presented in the Discussion section, which is unconventional. Also, the relevance of these comparisons could be introduced initially (instead of lines 435-445) so it is clearer why estimating density and strength from slab depth/thickness is helpful for mechanical models.
- Results were not compared with the similar studies such as Reuter et al. (2016).
- Fig 9: It’s odd to present new datasets in the caption of a discussion figure (EP20, EP19). Also, caption should have plain text names for all symbols presented. The 2 subfigures should be labelled and cited as 9a and 9b. Consider using different colours for the McClung and Bazant curves, it initially appears they are from the same study.
Citation: https://doi.org/10.5194/egusphere-2023-1586-RC1
Francis Meloche et al.
Francis Meloche et al.
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