the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Aug 2024
Brief Communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow-ground interface temperature sensors
Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We train a random forest machine learning model to predict snow depth from variability in snow-ground interface temperature. The model performed well on Alaska’s Seward Peninsula where it was trained, and at pan-Arctic evaluation sites (RMSE 0.15 m). Small temperature sensors are cheap and easy-to-deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring to an extent previously infeasible.
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Status: open (until 03 Oct 2024)
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RC1: 'Comment on egusphere-2024-2249', Anonymous Referee #1, 17 Sep 2024
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Summary & General Comments:
This brief communication presents a novel approach to derive snow depth from low-cost temperature sensors deployed at the snow-ground interface using a random forest model. This method would hypothetically allow snow depth monitoring at far greater number of sites than currently available, a significant finding well within the remit of The Cryosphere journal. The manuscript is concise and well-written. I recommend this manuscript be published subject to minor revisions, as detailed below.
As an aside, I would be interested to see what you think the impact of snow stratigraphy may be on the depth estimates from your model (particularly when it comes to evaluating snow depth at sites beyond the Arctic, such as the New Mexico site), but I understand that you are unlikely to have this data for comparison.
Minor/Technical Comments:
This is a broad and minor stylistic comment, but I would remove the italics for above and below ground throughout.
Section 2.1: Could you add a photo of one of your DTPs to Fig S1? Please also give an indication of how deep into the soil these profilers go, and when they were deployed relative to the start of the snow season.
Line 46: Please give the precision of the snow depth estimates.
Line 47: Is the value of the closest temperature sensor used as the value for TSG, or is TSG estimated from the sensor temperature using another method (such as a linear extrapolation)?
Line 76-77: Does “shallow subsurface” refer to the 1 - 5 cm temperature measurements from the previous sentence? Consider rephrasing these two sentences for clarity.
Line 84: Consider adding vegetation type for all sites to table S3 and refer to this after the statement “Vegetation also varied across sites”. Vegetation for 2 sites is given in the following text but this info isn’t currently in the table, whereas vegetation for other sites is included in the sensor details column.
Lines 92-94: I am confused as to how you trained RF-Deep when you are unable to derive depth estimates for snowpacks deeper than the 1.77m length of the temperature probes. Please clarify what data was used to train the deep model.
Line 95: I would role this section into the previous one.
Line 115/Figure 1: My initial thought was that “temperature range” referred to the range of temperature measured along the whole depth of the DTP, not just as the snow:ground interface. Consider changing this to “daily TSG range” in both line 115 and the red y-axis for plots g - i. Additionally, the use of blue and green to distinguish between the two different models is not accessible to those with colour vision deficiencies. Please change one of these colours – something like blue and orange or green and purple would work.
Line 138: Could this poor performance for ephemeral snowpacks be improved by including more ephemeral snowpacks in the training dataset?
Line 149: The insulative capacity of some snowpacks has been shown to be reached at much shallower depths than 1 m (e.g., Slater et al, 2017), particularly in Arctic environments like where the original model was trained. Potentially reconsider the use of this 1m value.
Line 156/Figure 2: The figure caption refers to a colour bar for subplot f), when I think you mean the y-axis for subplot g). Please double check. Some units on the y axes are also needed. Please also clarify what the black lines refer to – measured snow depth? Also, as for the previous figure, the use of blue and green to distinguish between the two different models is not accessible to those with colour vision deficiencies. Please change one of these colours.
Figure S1: Please clarify what is meant by WY2023 and WY2022 in the figure caption. Can you also confirm that snow depth data shown in b) and d) is for a different year to the temperature data on which the snow depth model is based. Also see comments for Section 2.1 above.
References:
Slater, A.G., Lawrence, D.M. and Koven, C.D. (2017) ‘Process-level model evaluation: a snow and heat transfer metric’, The Cryosphere, 11 (2), 89–996. https://doi.org/10.5194/tc-11-989-2017.
Citation: https://doi.org/10.5194/egusphere-2024-2249-RC1
Data sets
iButton and Tinytag snow/ground interface temperature measurements at Teller 27 and Kougarok 64 from 2022-2023, Seward Peninsula, Alaska Katrina Bennett, Claire Bachand, Lauren Thomas, Eve Gasarch, Evan Thaler, and Ryan Crumley https://data.ess-dive.lbl.gov/view/doi:10.15485/2319246
iButton snow-ground interface temperature measurements in Los Alamos, New Mexico from 2023-2024 Lauren Thomas, Claire Bachand, and Sarah Maebius https://data.ess-dive.lbl.gov/view/doi:10.15485/2338028
Model code and software
Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico Claire Bachand, Chen Wang, Baptiste Dafflon, Lauren Thomas, Ian Shirley, Sarah Maebius, Colleen Iversen, and Katrina Bennett https://data.ess-dive.lbl.gov/view/doi:10.15485/2371854
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