This study directly addresses what I would call the measurement and estimation problem: using 36 snow and repeat airborne lidar across the Wind River Range to show that widely used snow density and SWE datasets systematically underrepresent heterogeneity. A key finding is that complete profiling of a 5.9 m drift in a nivation hollow yields a bulk density of 585 kg/m³ — exceeding prior empirical bulk snow density model predictions by 14–35%. At the watershed scale, three near-real-time gridded SWE datasets underestimate total SWE storage in a heavily drifted 1 km² glacial cirque basin by 65–73%, despite underestimating watershed-average SWE by only 2–17%.

The same research group recently published two other papers dealing with snow heterogeneity in the Wind River Range, Wyoming. Compared to these papers, this study shows:

1. Density profiling of extreme drifts. The study shows results of a 5.9 m drift, where the measured bulk density is 585 kg/m³. It is shown that widely used snow density models fail badly such deep snow.
2. Benchmarking operational gridded SWE products. This study directly evaluates the performance of existing near-real-time SWE products and shows the heterogeneity problem propagates into operational datasets. It is shown that that watershed-average SWE can look well-estimated even while the spatial distribution — and especially the high-SWE tail — is dramatically wrong.
3. SNOTEL sampling bias. The study notes that all 88 SNOTEL sites in Wyoming are located in forested areas, structurally excluding them from observing alpine snowpack heterogeneity.

After reading the large preprint (42 pages without references) I was disappointed as there is unfortunately only little new scientific insight in this study. Based on the above three points, only the first is new, i.e. showing the density distribution in such a deep snow drift and providing an explanation for the vertical and lateral differences within the nivation hollow. Unfortunately, it is only one profile from one winter.

However, already the finding that empirical models of snow density underestimate the measured bulk density is not surprising, because, as the authors themselves write: “Many of the models tested are being used beyond their intended scope.”

Similarly, regarding the second of the above points. It is not surprising that most of current available products fail to reflect the spatial heterogeneity, because, as the authors themselves write “Gridded SWE products that do not explicitly consider snow transport processes are unlikely to capture the spatial variability”. However, they were trained to reflect watershed-average, where they perform mostly quite well.

The third of the above points is connected to the second point. The finding that SNOTEL sites are not representative on the watershed scale is not only valid for Wyoming. It is valid also for other regions and other in-situ networks and has been demonstrated already many times (e.g. doi.org/10.1002/hyp.6128, doi.org/10.1002/hyp.9355)

Given this fact (just one single new scientific finding), I believe that the study, in its current form, is not worthy of publication in TC.  If at all, I strongly suggest changing the focus and the title so that the weight is on the density profile in the nivation hollow. I do not like the current title for two reasons: First, not only deep snow drifts are missing in current approaches, but probably also the opposite like ridges with bare ground. Second, it is not all new that most current approaches underestimate the heterogeneity of snow in mountainous terrain (e.g. doi.org/10.5194/hess-19-1339-2015)

Finally, the author totally misses mentioning the current literature, which tries to tackle the missing heterogeneity problem. Here is just a small set of examples…

doi.org/10.5194/tc-15-743-2021

doi.org/10.5194/tc-18-3533-2024

doi.org/10.3389/feart.2024.1393260

doi.org/10.3389/feart.2023.1308269

doi.org/10.1002/2016WR019872

doi.org/10.5194/tc-18-4315-2024

Here, some input to individual lines in the manuscript:

L21: Accumulation areas of typical mountain glaciers are often at locations which profit from snow drift. Mass balance measurements before the melt season frequently show densities around 600 kg/m3 (e.g. doi.org/10.5194/tc-13-3413-2019)

L40: Pfohl et al. in review: Could not be checked.

L291: There are more newer, more enhanced models available, which were developed based on the same data set (doi.org/10.1016/j.coldregions.2025.104435, doi.org/10.5194/hess-25-1165-2021).

L376-389: Belongs to introduction

L455: Fig.4: snow stratigraphy and snowpack temperatures would help a lot to explain density trends.

L501: “we there”?

L503: “lower density”. I strongly recommend to consistently use “bulk density” and reserve “mean density” for the average of bulk densities from different profiles.

L543: Fig: 6: snow stratigraphy would help a lot to explain density trends.

L545: “the other tree snow pits”. I strongly recommend introducing a table providing an indicator, snow depth, elevation, aspect, density, density-trend for each snow pit and provide the indicator in the figures.

L557: Snowpack temperatures: I wonder if it was always measured in the shade when shoveling took several hours?

L562: “The coldest temperatures in the Downs Mountain summit drift are near the ground surface”. That might have been due to permafrost?

L573: “the temperature profile is reversed compared to what we would expect for depth hoar formation”. Not necessarily, as the depth hoar formation typically happens during a period of several days where the temperature measurement is only valid at the time of the measurement.

L600: That also depends on the aspect of the forest (doi.org/10.5194/hess-30-1691-2026)

L764: “avalanche debris is the densest snow within the high-elevation Downs Mountain vicinity”. Just one sample?

L768: “Since snow near the surface of the drift exceeds the vertically integrated mean density”. This can easily be caused by wind- or melt-crust.

L791: “Tabler (2003) emphasizes the pressure of overlying snow”. Tabler developed is method for snow drifts along road fences and was never thinking about special cases like nivation hollows.

L799: “For the first time, our study quantifies the watershed-scale impact of ignoring wind-packing effects on snow density.” Snow density heterogeneity?

L814: The best model shows only an underestimation of 3%! It is in sect. 3.5 and not 3.4!

L882: Current progress in forest-snow is missing: doi.org/10.5194/hess-24-2545-2020, doi.org/10.5194/hess-27-2099-2023 or doi.org/10.1029/2020WR029064

L890: “overrepresentation of in-situ stations in forested regions”. This might be true for the SNOTEL network, but it is not true for other mountainous snow networks. Also, the opposite has been observed (doi.org/10.1002/hyp.10295).

L915: “colocation issues between the snow pillow (which measures SWE) and the snow depth sensor (which measures snow depth) could contribute to inconsistent density calculations”. Please provide any literature for this claim.

L938: “the two deepest snow pits (4.0 and 5.9 m) have the third- and fourth-highest representativeness scores.” Not shown - please provide table/figure.

L1022: “Airborne lidar surveys provide the key to accurate quantification of snow depth heterogeneity”. Right, but please also mention that they are expensive and therefore rarely available in other mountain regions.

The manuscript addresses spatial variability in snow properties, particularly snow density, which has been relatively understudied in mountainous environments. Most existing measurements are conducted at lower elevations, where snow characteristics differ substantially; therefore, such observations are not representative of high-altitude conditions. At higher elevations, snow density is typically modeled, but these models often fail to capture highest densities.

In situ observations from wind-drifted locations with several meters of snow accumulation in high mountains are very rare, mainly due to difficult access and the effort required for excavation and measurement of deep snowpacks. In this context, the study presents a unique dataset, including an exceptionally deep snow pit (5.9 m), which reveals very high snow density, as well as another 4 m deep pit with higher-than-average density compared to surrounding sites.

The study is further strengthened by the inclusion of a spatial snow depth survey using airborne lidar, allowing analysis at larger scales in addition to point-based measurements. The manuscript includes also comparisons with three other snow products. The model with 36 snow pits and lidar data demonstrates that other models without lidar data and SWE products underestimate SWE by approximately 2–17% at the watershed scale. Wind speed is simulated to identify wind-drift areas and incorporated into the analysis, showing that snow density is higher in these areas, which contributes to the underestimation of SWE at the watershed scale. Areas influenced by avalanches show particularly high densities, which is consistent with expectations. Overall, the watershed-scale comparisons with models and existing products are a valuable aspect of the study.

The figures are generally clear and of good quality, effectively supporting the presented results.

Some additional comments and suggestions:

• It is unfortunate that only one very deep snow pit is presented. However, I acknowledge the difficulty of such measurements, and even a single profile is valuable. Since the deep pit was already excavated, it would be beneficial to include multiple detailed density profiles from this location.
• It would be interesting to include a comparison with SWE tube measurements. Based on my experience, a soil plug is not always necessary when using a Federal sampler, as dense snow can remain in the tube without it. If future measurements are conducted, SWE tube sampling at snow pit sites could help confirm ground contact.
• The text is very long could be shortened for better focus
• Figure 1 would benefit from a scale bar
• Please clarify whether the density cutter was used vertically or horizontally. Also, how many profiles were measured?
• The accuracy and type of scale used for density measurements should be reported.
• The opening of the Results section contains material that could be moved to the Introduction.
• Line 501: revise “Consequentially, we there” by removing “we”