Comment on tc-2022-64

in two agro-forested and one They conduct variogram analyses on snow depth fields to find possible scale break lengths that define regions with self-similar behavior, and develop random forest models to characterize predictor importance. The results show scale breaks spanning 4-7 m in forested sites, and relatively longer values in field areas – up to 18 m in wind exposed fields – in agreement with previous studies. The results also show that wind-related forest edge descriptors mostly explain snow depth variability in agro-forested sites, while canopy characteristics (i.e., forest structure) are more important in the coniferous site.

The study is appropriate for The Cryosphere. The article is well written, but some parts describing the equations can be shortened. I think that the authors do a valuable contribution. Having tools to estimate DDFs is a good idea, and it can be useful for researchers working on the snow hydrology of poorly monitored regions. However, I think that the article needs to be improved before being suitable for publication. Please see my main comments.

Presentation and role of the datasets
Field dataset: The purpose of including the datasets from Brunnenkopfhütte and Naran stations is not clearly presented. The authors should mention in the Introduction what is the role of these datasets in their study. Are they used as validation, or test sites? Do the authors make tests at the catchment or point scales? Importantly, the use of the Naran dataset comes a surprise in the middle of the discussion section. Climate change dataset: Please provide more details about this dataset and add this analysis to the objectives of the study.

Discussion section
In this section, the authors continue their analysis and calculations, but they provide almost no comparisons with the results of other studies. The authors should discuss their results using the literature presented in the Introduction. Additionally, I recommend the inclusion of some other references regarding the spatial and temporal transferability of degree-day factors (or temperature factors) and melt parameters that, in my opinion, are missing (Ohmura, 2001;Carenzo et al., 2009;Gabbi et al., 2014) . The limitations of the approach proposed by the authors and the assumptions made through the article should be more discussed. For example, the authors validate their approach using only one monitoring station, can the authors include more data? There are certainly more datasets available for which DDFs have been derived. Otherwise, this is an important limitation of the study that should be discussed.

Conclusions and recommendations
As the aim of the study is to "quantify the effects of spatial, temporal, and climatic conditions on the DDFs" and the conclusion is that "DDF cannot be treated as a constant parameter", what are the recommendations of the authors to a researcher modelling the snow hydrology of poorly monitored catchments? Should that researcher use a range of parameters from your equations? How large should be the variability of DDFs in space and time? Different DDFs for each sub-catchment, slope or elevation band? How often should the DDFs change in time? Every week, month or season? I think that the article would benefit from such discussions and recommendations.

MINOR COMMENTS FOR THE AUTHORS
12-13: I would add "At mid-latitudes, seasonal snow …" because this seasonal pattern is not necessarily found on every snow and ice dominated mountain catchment (e.g. tropical glaciers).
13: I think that the concept of snowmelt runoff is wider than what the authors are describing. The authors are describing only the process of melt whereas snowmelt runoff include other processes controlling the movement of excess meltwater through a catchment. 21: is physically based -> is based 22: I don't think that the formulas are "approximate", they just have limitations and assumptions.
23: observed -> field-derived. DDFs cannot be measured in the field because they are not a physical quantity.
30: "albedo is likely to be higher", there are also other reasons, such as lower radiation and temperatures, aren't they?
35: It would be interesting to mention somewhere in the Introduction that researchers usually select DDFs values from other studies and that the spatial transferability is not always good [e.g. Carenzo et al., 2009;Wheler, 2009]. 300: What do the authors mean by "a probabilistic reasoning"? 344: I think a step or equation is missing here and it should be that relating RH and p0. Or how do the authors calculate pv? Also, are the authors assuming saturated conditions at the snow surface? 354/375: Sections 3.2.5 and 3.2.7 don't read as "Methods". They seem a review on the subject. As both terms (Q_G and DeltaQ) are neglected by the authors, I suggest the shortening of these sections and to move them to the beginning of Section 3.2 where a suitable justification to neglect them can be provided. 422: Delete "approximate". 431: higher altitudes, as well as dry climates. 504: As wind speed is highly variable in space and time, I don't think that the authors can refer to "typical values". It would be better to write something such as: "… can be roughly estimated based on the topographic and climate characteristics of the study site". 551: I think that this is the first time that the authors mention the goal of these dataset. Please see my main comments. 579: I believe that this is not clearly a discussion section because there are almost no comparisons against other studies (and almost no references). Instead, the authors present more results and analysis. Please my main comments. 592: This is the first time that the authors mention these data. Please properly introduce this site and the dataset in section 2. Also explain what is the purpose of including this dataset.
598: Please change the word "altitude" by "elevation" throughout the article. Altitude is the vertical distance between an object and the earth's surface.  Table   650: in Table   FIGURES Figure 1: I think that m (instead of cm) are enough for "High" and "Low" in the legend.  Figure 9 DDFs increase as the season progresses?