| 26 Nov 2025
Spatial and temporal variability of taiga snow properties during melting period in Sodankylä, Finland
Abstract. This study investigates the temporal and spatial variability of physical snow properties during the melting period in taiga snowpack conditions in Sodankylä, northern Finland. Weekly snow pit measurements – including stratigraphy, temperature, and density – were conducted at four locations over an eight-week period from March to May in 2023 to assess landscape-scale variability. At three sites, the average spatial variability of automatically measured snow height over time was 7.4 cm (9.7 %). Density measurements using the snow water equivalent tube showed higher spatial variability compared to those made with a density cutter, and this variability increased during melt. Differences between measurement techniques exceeded differences between locations. Peaks in density profiles were mainly linked to melt-freeze crusts and ice layers. Depth hoar was consistently found in lower snowpack layers before melting, reaching a maximum relative height of 21.5 %. The appearance of melt-freeze crusts following short-term temperature shifts highlights the snowpack's sensitivity to daily thermal cycles. Initial wetness was observed mid-snowpack, suggesting that refreezing from cold nights operates top-down, not affecting the full depth. Moist and wet layers became more prevalent in the upper snowpack, while the wettest layers accumulated at the base rather than being evenly distributed.
General comments:
This paper reports snow physical properties obtained mainly from snow pit surveys conducted at four locations in the taiga region of Sodankylä, northern Finland, from March to May 2023. The observed snow parameters include snow height, density, layer stratigraphy, temperature, and wetness. The discussion focuses on vertical profiles, temporal and spatial variations, and differences in these snow properties between forested and open (non-canopy) conditions. In conclusion section, the general characteristics of the snowpack during the snowmelt season are summarized. The results are not particularly novel and the scientific impact is poor.
The only noteworthy finding is that the difference in observed snow water equivalent (SWE) between the two measurement techniques (snow water equivalent tube and snow density cutter) exceeded the differences among the observation locations. It is, however, unclear whether this result reflects low SWE variability among the sites, which were located close to each other, or large uncertainties inherent in both measurement techniques. This issue is also related to the insufficient assessment of measurement errors associated with the tools used to measure snow density in this study.
Although the research background is described, the research objective is not explicitly stated. If the objective of the study is assumed to be “to clarify the temporal and spatial variability of physical snow properties,” the paper lacks sufficient quantitative analysis and discussion to achieve this goal. In particular, the analysis of meteorological data obtained from the weather station and the theoretical discussion are insufficient.
Based on the above evaluation, this manuscript requires substantial revision. I recommend that the authors rewrite the manuscript with a clear objective and corresponding presentation of the results, incorporating the following improvements, including quantitative analyses and theoretical discussions.
Improvement suggestions:
- Assess the measurement errors of SWE associated with snow density observations.
- Utilize automatic weather station data at the observation sites to analyze the surface energy (heat) budget. If possible, calculate snow layer structures using a numerical model, such as the SNOWPACK model (Lehning et al., 1999) and Crocus snowpack model (the latest version: Lafaysse et al., 2025), and compare the results with the observations.
- Quantitatively demonstrate the influence of canopy and ground characteristics on the snow physical properties examined in this study, including snow density profiles and the timing of snowmelt.
Specific comments:
L20-21: "Initial wetness was observed mid-snowpack, suggesting that refreezing from cold nights operates top down, not affecting the full depth." Is this only due to the gravitational settling of meltwater? Further theoretical discussion is needed regarding the presence of capillary barriers (Hirashima et al., 2017) related the snow layer stratigraphy obtained from snow pit observations.
L21-22: "Moist and wet layers became more prevalent in the upper snowpack, while the wettest layers accumulated at the base rather than being evenly distributed." Is the former due to solar heating? More quantitative and theoretical discussion is needed. Regarding the latter, please refer to the previous comment.
p.5, Figure 1a: Please add a scale to indicate the distance.
L134-135: "Qualitatively, the spatial variability of the manually measured snow heights seems to be even lower than the spatial variability of the automatically determined snow heights." Is it reasonable to discuss spatial variation based on differences in manually measured snow depth at only two locations, and then compare this to automated measurements?
L139 "3.2 Density section" It is necessary to explain the differences between the two snow density measurement methods and their possible reasons. Whether it is meaningful to discuss the differences between spatial variation and methodology will depend on the validity of those reasons.
L154-155 'Fig. 6' Typo of Fig. 5.
L164: "This layer is observable at all locations, sometimes more pronounced, sometimes weaker." It is difficult to identify the ice layers at the airport in Figure 5.
L179-182: "Across all locations, the temperature profiles ..." The temperature profile figure is not shown. Additionally, it would be helpful to include a figure showing the temporal variation of snow surface temperature, which can be calculated from the upward longwave radiation observed by the automatic weather station.
L222: "Similar observations were made by Hannula et al. (2016)." What is the similar observations? Please indicate the physical parameter or phenomenon.
L232-233: "On the other hand, using the density cutter can systematically overestimate the density, as some areas can be sampled several times, especially in the case of layers with high densities, where accurate sampling is harder." While measuring the snow density of an ice layer is certainly difficult, I don't understand why some areas were sampled several times.
L244-245: "Overall, a systematic error of 13% using the density cutter and 9% for the tube density measurement is considered, following the results from Kaasik et al. (2023) and Proksch et al. (2016)." This description contradicts the earlier descriptions (L238-242). Isn't the density cutter more accurate, then?
L248: "The temperature profiles of the snow pits showed the expected patterns, particularly" The explanation appears to be based on the assumption that a temperature profile figure is shown.
L2668-269: "Another source of error results from the lowest layers, which are often interspersed with vegetation, such as lichens." If this paper contains any new findings, they would likely concern the influence of ground vegetation, the effects of forests blocking snowfall and solar radiation, or the downward longwave radiation emitted from forests during the snowmelt season. Unfortunately, however, no quantitative discussion of these points is provided.
References
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations, Cold Reg. Sci. Tech., 30, 145–157, https://doi.org/10.1016/S0165-232X(99)00022-1,1999.
Hirashima, H., Avanzi, F., and Yamaguchi, S.: Liquid water infiltration into a layered snowpack: evaluation of a 3-D water transport model with laboratory experiments, Hydrol. Earth Syst. Sci., 21, 5503–5515, https://doi.org/10.5194/hess-21-5503-2017, 2017.
Lafaysse, M., Dumont, M., De Fleurian, B., Fructus, M., Nheili, R., Viallon-Galinier, L., Baron, M., Boone, A., Bouchet, A., Brondex, J., Carmagnola, C., Cluzet, B., Fourteau, K., Haddjeri, A., Hagenmuller, P., Mazzotti, G., Minvielle, M., Morin, S., Quéno, L., Roussel, L., Spandre, P., Tuzet, F., and Vionnet, V.: Version 3.0 of the Crocus snowpack model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4540, 2025.