the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Sep 2025
Spatial and temporal features of snow water equivalent across a headwater catchment in the Sierra Nevada, USA
Abstract. Advancements in remote sensing of snow (e.g., lidar) have allowed for the characterization of mountain snowpacks at higher spatial resolutions (< 10 m) and higher vertical accuracy (< 20 cm) than previously available, which can cover entire catchments repeatedly throughout the snow season. Here, we use distributed snow water equivalent (SWE) over the Tuolumne River Basin in California, USA, from lidar snow depths combined with energy/mass balance modeling of the Airborne Snow Observatory (ASO) program for the period 2013–2017 (48 flight-dates, 50-m resolution) to characterize the spatial and temporal variations in SWE distribution in this headwater catchment. Peak basin snow volume storage ranged from 142 M m3 (i.e., 106 m3) in 2015 to 1467 M m3 in 2017, covering one of the widest ranges in recorded history. Basin SWE empirical distributions vary between unimodal and bimodal distributions earlier in the season to decaying distributions later in the ablation season. Snow storage peaks at mid-elevations between 2750–3250 m, which is a consequence of increases in SWE with elevation and basin hypsometry. The date of peak SWE varies by several weeks across the watershed and between years, according to the combination of accumulation and melt patterns partially explained by elevation and aspect. This variation in peak SWE timing leads to underestimations if a single date is used to uniformly characterize the basin's peak SWE. These results illustrate how understanding SWE spatiotemporal dynamics can improve the understanding of where the snow is and when it melts, support satellite mission planning, and enhance ground survey design and planning.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3736', Anonymous Referee #1, 16 Sep 2025
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RC2: 'Comment on egusphere-2025-3736', Anonymous Referee #2, 15 Oct 2025
Review: Spatial and temporal features of snow water equivalent across headwater catchment in the Sierra Nevada, USA [egusphere-2025-3736]
This paper characterizes SWE in the Tuolumne River Basin, California, for WY 2013-2017 using ASO data (lidar + snow model). The analysis is somewhat new and is a small contribution to advancing current understanding. The analysis highlights the types of new knowledge that can be gained from spatially continuous SWE information.
The paper is well written. The results could be better synthesized to provide the reader with key takeaways and how they relate to paper aims and scope (see major comment 2), especially when the analysis is presented separately for each individual water year. I have three major comments that I’d like the authors to address before considering the paper for publication.
Major comments
- The paper would be strengthened by adding some precipitation data to support the SWE discussion, if possible. This analysis does not need to be exhaustive as the focus of the paper is to characterize the basin’s SWE. However, many of the explanations for the differing SWE distributions from year to year come down to precipitation, yet no direct evidence is given to support the claims.
- Provide statement(s) on the importance or value of the various analyses. In many instances I was looking for the ‘so what’ related to a specific result or analysis. For example, what is the value of knowing the empirical probability distribution in the context of water resources. What does a specific distribution mean and what are the implications of the distributions you discovered?
- Acknowledge the recent paper by Raleigh et al. (2025) on snow monitoring via strategic locations (as opposed to basin averaged SWE) and discuss to what extent your findings align or contradict their conclusions. Both works seem to suggest that a single basin-averaged survey is insufficient. Your work points to the need for multiple surveys whereas Raleigh et al. (2025) advocate for adding point measurements at ‘hot spots’.
Raleigh, M.S., Small, E.E., Bair, E.H. et al. Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping. Commun Earth Environ 6, 665 (2025). https://doi.org/10.1038/s43247-025-02660-z
Minor comments
L128-192: Please provide reference some ‘previous studies’ alluded to here.
Figure 2: Is it possible to add some sort of hypsometry of the part of the basin used for the 250m elevation bins and for those exact bins?
L229-230: (ii) This method could use a bit more explanation. i.e. is it a histogram of grid-averaged SWE for all grids in the basin as a function of total basin area? Also, were 6 dates selected because it corresponds to the minimum number of flights in a WY (2013)?
L250-254: (v) Unclear here and in the Results (Sect. 3.5) how ‘SWE at the timing of basin peak SWE’ is defined. Is it SWE from the survey closest to 1 April or the date with the highest basin-averaged SWE? Your text (Sect. 3.5) gives underestimation values for both. Which of these is shown in Figures 12-14?
L493-500: Are these two WYS ‘normal’ snow years and if so, how do their distributions compare to 2016?
Caption Figures 4 – 6: suggest ‘Histogram of SWE as a function of basin area for six flights dates in ... Total basin SWE storage and snow covered area noted in upper right’.
Sect 3.2: Suggest adding a sentence in the first paragraph similar to Sect. 3.3 that elaborates on what the SWE EPD shows and how that information is useful in the context of water resources.
Sect. 3.3 paragraph 1: Please provide references.
L318: suggest ‘total SWE storage’
Figures 7 & 8: Is there a way to show the hypsometry for the area between 2250 and 3500m? This would help the reader interpret the total snow mass in particular. Total basin hypsometry shown in Figure 1 but for different bin sizes than analysed here and I found it challenging to use that information to interpret the analysis in Sect. 3.3.
L354-255: This alludes to the strengths and weaknesses of the two types of data/measurements. See major comment #3.
Figures 9 through 14. Elevation distribution subplot – is the y-axis % the percentage of the basin area? Please clarify.
Citation: https://doi.org/10.5194/egusphere-2025-3736-RC2 -
RC3: 'Comment on egusphere-2025-3736', Anonymous Referee #3, 17 Nov 2025
Spatial and temporal features of snow water equivalent across a headwater catchment in the Sierra Nevada, USA
This paper uses combined lidar-derived snow depth with modeled snow densities at 50 m resolution to characterize spatial and temporal variability of SWE across the Tuolumne River Basin for water years 2013-2017. The analysis leverages a high volume of lidar flights (4 to 35 day flight intervals, 6-13 flights per year) and captures a period of extreme interannual variability. The use of this integrated dataset provides spatial and temporal coverage previously unavailable in the assessment of large snow-dominated headwater basins.
In my comments below, I summarize how this paper could be strengthened by clarifying the motivation and intended contributions, as well as more completely acknowledging the availability of high-resolution, continuous SWE products in the western US.
Major Comments
1. Clarity and structure of the storyline: The introduction lacks a clear and cohesive narrative that articulates the unique opportunities this integrated dataset presents. The limitations of April 1 SWE and snow depth are discussed, but the research questions and the framework of analysis are not clearly linked towards specific scientific or practical motivations. Because the April 1 SWE and snow depth limitations are well established in prior literature, the paper would benefit from identifying a targeted outcome or specific advancement that these analyses enable. Similarly, the broader impacts in the discussion focus on advocating for additional lidar flights around date of peak SWE. However, the paper emphasizes that such flights are already ongoing in this basin and others and that there are trade-offs of increasing flight volume. The novel contribution of this work is therefore unclear.
2. Constraints of lidar-based peak SWE: Timing of peak SWE is foundational in the analysis of this paper but is inherently biased by the timing and availability of ASO flights. Because lidar observations are not continuous, mean SWE curves and melt rates are also subject to the bias and uncertainty linked to limited flight dates. Please expand upon the constraints of having to rely on flight acquisition and the subsequent uncertainty of the resulting analysis. It may be important to constrast with continuous SWE datasets (see next comment), which have their own biases and uncertainties.
3. Continuous SWE datasets: Acknowledge the availability of continuous daily SWE products produced via data assimilation. The Western US snow reanalysis dataset by Fang et al. (2022) spans water years 1985-2021 at 500 m spatial resolution. Discussing their existence and potential utility would strengthen the contextualization of the results.
Fang, Y., Liu, Y. & Margulis, S.A. A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021. Sci Data 9, 677 (2022). https://doi.org/10.1038/s41597-022-01768-7
Minor Comments
Lines 51-53: I suggest swapping the Bohr and Aguado (2001) reference for Margulis et al. (2016), which is more relevant to the Sierra Nevada and the western US:
"However, the results showed that the assumption that 1 April SWE is representative of the peak SWE can lead to significant underestimation of basin-average peak SWE both on an average (21% across all basins) and on an interannual basis (up to 98% across all basin years)."
Margulis, S. A., Cortés, G., Girotto, M., & Durand, M. (2016). A Landsat-era Sierra Nevada snow reanalysis (1985–2015). Journal of Hydrometeorology, 17(4), 1203-1221.
Lines 60-63: The statement about April 1 SWE as a useful metric seems misplaced in a paragraph noting the shortcomings of April 1 SWE. The overall discussion surrounding the limitations of April 1 could be more concise to build a more targeted storyline.
Lines 125-127: Does this 2013-2017 window capture the effects of natural variability in the region on water resources?
Lines 128: Cite previous studies.
Line 157: Typo: “was generated”
Line 190: Is there a reference for these snow density results? If not, maybe just mention "not shown". Can you give a sense of the average % error that this corresponds to? E.g., <10% error?
Lines 208-210: This statement indicates uncertainty in peak snowpack storage dates that isn’t immediately acknowledged.
Line 245: It should be noted, here or in the Intro., that Margulis et al. (2016) characterized the differences in these two metrics across the Sierra ... b/c their approach was model-based, it would be less sensitive to errors introduced by "missing peaks" due to intermittent flight frequency, whereas your approach may be less likely to be affected by spatial model error due to the lidar constraint. In addition, wouldn’t peak SWE always be expected to occur on a day with snowfall that couldn’t be flown by lidar? So, in the best case scenario, aren’t lidar-based estimates of peak SWE always off by at least one day?
"The peak SWE can be characterized in different ways. We focused primarily on characterizing the climatology of the “pixel wise” peak SWE, which represents the maximum value of SWE at each 90-m pixel across the domain in a given WY. The pixel-wise peak SWE represents the maximum water available for partitioning into snowmelt, evaporation, and soil water storage, and has a corresponding spatially varying pixel-wise day of peak (DOP) at which the peak SWE occurs. The pixel-wise peak SWE provides a more complete characterization of the total maximum available water integrated over the full melt season since it takes into account the variability of accumulation and melt timing across the domain. Secondarily, we focused on characterizing the basin-average peak SWE for each basin in the Sierra Nevada range. These estimates correspond to a particular day of the water year (DOWY) where the basin-average SWE is at its maximum. This latter metric was compared to the pixel-wise peak SWE and the 1 April SWE, which is also often used as an index for use in water supply forecasts."
Lines 257-259: While a high volume of lidar flights, the limitations of a non-continuous dataset in the methods of analysis are not addressed.
Lines 269-273: Describes uncertainty in date of peak SWE relative to flight dates, but consequences are not discussed.
Figure 4: Suggest using yyyy-mmm-dd convention for clearer communication with international audience.
Lines 313-317: This feels more like Intro / Discussion material than Results.
Figure 8: Great figure!
Lines 605-607: The introduction discusses the limitations preventing spatially resolved peak SWE, and not necessarily that basin peak SWE is an assumption.
Lines 626-630: More lidar surveys are already being done in the basin, so what specifically does this paper contribute to decision making in snow-dominated basins?
Lines 658-661: Reiterating that more lidar surveys are already being endorsed and conducted in this region.
Lines 661-664: Tradeoffs addressed briefly and no other options explored.
Lines 676-679: This statement indicates that the study describes how these analyses can be used to analyze distributed SWE information throughout the accumulation season, but the dataset begins near or at peak accumulation (therefore missing the accumulation season).
Citation: https://doi.org/10.5194/egusphere-2025-3736-RC3
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