Dear Authors - while major improvements have been made in your revised manuscript, the view of Reviewer 2 and myself is that while the material is worthy of publication, the presentation falls below the standard required for publication. I invite you to respond to Reviewer 2 comments below and look forward to receiving your final paper.

Best regards, Ross Brown

Reviewer 2 comments on revised m/s:

This paper is much improved from the original version, but still needs improvement before I think it should be accepted for publication. The main improvement is to make the results, and their meaning clearer, and to support use the results to support the conclusions more fully.

One thing that will greatly help with clarity is for the authors to review and revise the nomenclature that is being used throughout the text, defining clearly what terms mean, and what is actually being measured. Choose names carefully, then stick to the choice! Consider the use of precipitation, accumulation, snow depth and distribution. Precipitation varies with elevation (orographic lifting). It may also vary with slope and aspect due to squalls and storm tracks moving through basins, but slope and aspect can also effect the snow distribution (as would wind) through direct post-deposition effects, and indirect effects through controls on the vegetation. Depth as revealed by lidar….which includes canopy interception as well as post-deposition processes like avalanching and differential melt….is the product of two very different physical processes. And there is actually a third set of processes that alter depth: snow settlement. The regression equations proposed in the text includes both sets of processes, which is fine, but needs to be stated clearly. Also, while settlement does not affect depth, it does affect SWE. It is completely possible that altitude/slope/aspect driven differences in settlement produces different depths without differences in SWE, another point that deserves discussion.

Similar nomenclature issues exist in the paper for the terms related to canopy openings (also called open areas) and under canopy areas, and to the various lidar-derived models (ground DEM, snow depth, canopy etc.) Many of these seem to get called different things at different points in the paper. One simple thing that would help (suggested in my previous review) would be a simple sketch of a tree canopy, a shrub canopy (less than 2 m high) and the various lidar-derived surfaces, marked on the sketch.

A second major problem is that the paper does not do a good job of circling back to the stated objectives and three study questions (new) listed page 5. In fact, I would suggest the questions are slightly off target. I think this study really addresses 1) whether using lidar can improve our understanding of the orographic increase in snow depth with height in the Sierras (Yes, because we get so many more data), 2) whether the resolution at which the lidar is used matters (perhaps, but see below), and 3) whether including slope, aspect and some measure of canopy conditions can improve regressions used to predict snow depth (Yes, nicely shown in the paper). Issue 2) remains in doubt and is not addressed well in the paper. The authors show that in order to increase lidar snow depth mapping toward 95% coverage, pixels must be increased to about 5-m, and that doing so tends to favor a bias (though towards + or – is not made clear, and why). But whether the increase in areal coverage leads to a commensurate decrease in actual depth accuracy is either buried away in the text where I missed it, or not addressed. This is an important and practical outcome of the study: it should be addressed more clearly and comprehensively.

Minor Points

I was disappointed that the manuscript at this stage was not more error-free. I would expect it to be so before re-submission. One trivial but indicative point concerning this is the use of the abbreviation Lidar, which in various places also shows up as LiDAR. A recent paper on lidar suggests, just as radar, the all-lower case version is starting to be preferred in the literature. But the point is mistakes like using several versions should be absent by now.

Abstract: Line 13: delete “snow-on and snow-off”

Abstract: Lines 25-28 are very awkward.

Introduction, Line 38: “…precipitation and snow distribution . . . “

Introduction, Line 45: Surely some knew and published that snow below the canopy is shallower than in a clearer before 2013? In my paper on tree wells (Arctic and Alpine Research, 1992, Vol. 24, pp. 145-152) I cite several papers dating back to 1939 on this topic.

Line 54: Why not cite that Sierra snow fuels a multi-billion dollar agra-business? That’s a good reason for the study.

Line 69: New paragraph starting with “An orographic-lift effect. . .

Line 73: Predictors: this in the context of the paper are elevation, slope aspect and canopy character, but this gets back to my major comment on being clearer on how the snow depth arises from variations in precipitation, as well as post-depositional processes of redistribution.

Lines 92: This study doesn’t explain or increase our understanding of why the depth varies, but rather how it varies. See above how the questions might be revised. Currently, questions 2 and 3 overlap considerably.

Lines 119-120: Poor and confusing sentence.

Lines 138-139: I have no idea what this sentence means.

Equation 1: Specify that north is 0/360°, and that it is measured in degrees.

Line 152: Too many sentences start with “And. . .” Its OK a few times, but I think it has been over used.

Lines 155-156: A good example of varying nomenclature: under-canopy. Also why 2 m? Never discussed.

Section 2.4: The reason to define and compute penetration fraction is to be able to examine the impact of canopy on the regression. Fine, but that is never stated.

Lines 172 ans 177: Repeats. Be more careful!

Line 179: Why 2 m?

Line 188: Is a “survey area” each basin?

Line 207: This needs to go sooner. The whole point of penetration fraction (pf) is to have a useable physiographic variable. To be really acceptable, you would need to show quantitatively that pf is related closely to some physical measure of canopy, and that seems not to have been done, so we have to take this on faith.

Lines 226-227: This seems to warrant more comment. Don’t you think it somewhat surprising that these four separate field areas produced such a nicely clustered non-linear function? Why might this be the case?

Figures 7a and 7b vs. 5a: Personally, I would lead as Figure 5 with what is currently Figures 7a and b. It is almost the same data, and it is more interesting with the data points actually plotted and coded by aspect. You could readily delete 5a.

Line 230: Here you state the depth is linear with elevation in clearings and under the canopy, but Fig. 5a only shows data from clearings. This is another good reason to replace 5a with 7a and b. Also, I hardly would call the depth-elevation function linear. It can be fit with a line, but it has a strong curve to it.

Table 1 needs to have the mean elevation, elevation range, and canopy cover added to it. Table 2 could be deleted if room becomes a problem. We need to readily understand how these basins differed in elevation, area, and so on.

Lines 235-250: I still would like to see the elevation-depth regression for each area as an equation, before adding in the other explanatory variables.

Lines 251 to 267: By their nature, residuals have sign, but we usually mean that an increase in residual can be either positive or negative, while a decrease means the residual absolute value gets smaller.

Lines 264-265: The relative strength of the regression equation coefficients is masked here because the input values differ in scale: elevation ranges from 1500 to 3000, while penetration fraction ranges from 0 to 1. But if you normalized each explanatory variable from zero to 1, then the coefficient magnitudes would suggest the strength of that term.

Line 282: Important and not shown or demonstrated (see above).

Conclusions: Good and clear!

Figure 6: Steeper slopes have less snow than the elevation model predicts. North slopes have more; south slopes less. What does panel c show?

Dear Authors - the paper is getting closer but there are still phrases that are difficult to understand and the abstract and conclusions need more work to present the study findings in a succinct manner. For example, neither the abstract nor the conclusions indicate what new significant findings were obtained by this study.

I suggest you include the answers to your three questions in the abstract and conclusions. Note that as posed, your Question 2 is difficult to understand.

1. What new information about orographic effects on precipitation versus accumulation is provided by Lidar data?

2. Is it possible to have snow-depth measurements in forested mountain terrain from all pixels on a fine sampling resolution (1- to 5 m) using Lidar data? If not, how does the percentage of pixels measured change with the sampling resolution. [Not clear what you mean by a "measured" pixel]

3. Third, what is the importance of slope, aspect and canopy penetration fraction on snow accumulation, relative to elevation; and are effects consistent across sites?

In addition to the points noted below, I strongly recommend you engage a technical editor to verify your manuscript before resubmitting it. Can you also include a version with the edits accepted for easier reading.

Best regards, Ross Brown

Lines 17-18: It seems there is a key word missing in the sentence. "...the percent of pixels with [valid?] snow-depth measurements is..."

Lines 20-29: The results presented in these lines are not clear and quantitative. I suggest you simplify this to something like "Elevation was the dominant physiographic variable explaining snow depth variability over the study regions (xx % of variability) followed by slope (xx%), aspect (xx%) and canopy penetration fraction (xx%). However, the relative importance of the latter three variables was observed to vary with elevation and canopy-cover."

Lines 232-234: Not clear. Suggest "A multivariate linear-regression model was also applied to quantify the influence of the various physiographic variables on the snowpack distribution."

line 372: Not clear. Suggest rewording "Based on the scatterplots in shown in Figures 6a and 6b color coded by northness,..."

lines 391-395: This paragraph is confusing and does not provide a clear conclusion. I suggest you cut this.

lines 399-401: Suggest you provide some more quantitative estimates of the relative importance of the physiographic variables e.g. were the correlations statistically significant? The conclusions section needs to answer your three questions and indicate what new findings were obtained.

I suggest you include an acknowledgement to your reviewers. If it wasn't for Reviewer 2 this paper would never have made it this far!

Figure 2: Good but could do a better job of conveying the sampling issue e.g. open snow, snow in gaps, and snow under vegetation.

Dear Dr. Zheng - we are almost there! Your abstract is still rather difficult to follow in places so I suggest you look at my suggested revision below. It would also be a good thing to finish the abstract with a sentence highlighting the importance of the paper's findings/methodology.

I think it is vitally important your paper has a readable abstract as this is what most people will look at through search engines.

Best regards, Ross B

Suggested revised abstract:

Airborne light detection and ranging (Lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak snow accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65-90% to 99% as the sampling resolution of the Lidar point cloud was increased from 1 to 5 m. However, this coarser resolution risks undersampling the under-canopy snow relative to snow in open areas, and was estimated to result in at least a 10-cm overestimate of snow depth over the main snow accumulation region between 2000-3000 m. Analysis of the 1-m gridded data showed consistent patterns across the four sites dominated by orographic effects on precipitation. Elevation explained 43% of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14% over the elevation range 1500-3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under canopy areas increased with elevation in the rain-snow transition zone (1500-1800 m), and was about 35±10 cm above 1800 m. [Add final statement about the novelty, importance etc of the findings]