the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Changing Snow Water Storage in Natural Snow Reservoirs
Christina Marie Aragon
David Foster Hill
Abstract. This work defines a new snow metric, snow water storage (SwS), which is the integrated area under the snow water equivalent (SWE) curve. Other widely-used snow metrics capture snow variables at a single point in time (e.g. maximum SWE) or describe temporal snow qualities (e.g. length of snow season), SwS can be applied at numerous spatial and temporal scales. The flexibility in the SwS metric allows us to characterize the natural reservoir function of snowpacks and quantify how this function has changed in recent decades. In this study, changes in the SwS metric are evaluated at point, gridded and aggregated scales across the conterminous United States (hereafter US). There is special focus on 16 mountainous EPA Level III Ecoregions (ER3s), which play an inordinate role in US annual SwS (SwSA). An average of 72 % of the annual SwSA in the US is held in the 16 mountain ER3s, despite these ER3s only covering 16 % of the US land area. SwSA and monthly SwS (SwSM) have changed significantly across the US since 1982 at point, gridded and ER3 scales. This change is spatially variable across the US with more spatially widespread significant decreases in SwSA than increases. The greatest SwSM loss occurs early in the snow snow season, particularly in November. All but two ER3 mountain ranges have decreasing trends in SwSA and there has been a 22 % decline in SwSA across all mountain ER3s. Unsurprisingly, the highest elevations are responsible for the greatest SwS in all mountain ranges, though the elevations that have lost or gained SwS over the 39 years of study are variable across mountain ranges. Comparisons of the percent change in SwS to other snow metrics reveals that change in the SWE curve has not been shape-preserving - instead, the SWE curve has been flattening. As we move into a future of increased climate variability and increased variability in mountain snowpacks, spatially and temporally flexible snow metrics such as SwS may become more valuable.
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Christina Marie Aragon and David Foster Hill
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-596', Anonymous Referee #1, 26 Jun 2023
The authors of “Changing Snow Water Storage in Natural Snow Reservoirs” (Aragon and Hill) present a new metric, Snow Water Storage (SwS) to evaluate the snowpack in the mountainous United States (U.S.) throughout the length of the snow season in meter-days. Unlike traditional metrics used to quantify and characterize the snowpack at a single point in time (e.g., April 1 or peak SWE), the SwS captures the area under the SWE curve to illustrate differences and changes in snowpack accumulation and ablation seasons as well, better illustrating the nature of the complete snow season (in a given area). While the metric has great and complementary utility in quantifying the snowpack (on a monthly, annual, or by elevation bin scale), the manuscript would benefit from further depicting the SwS using actual examples across the U.S. in raw units (meter-days as opposed to predominantly reported % changes). Hypothetical examples of the SwS and changes in the SwS are presented in Figure 1 of the manuscript, yet observed and modeled changes in SwS are reported as only % changes. In order to contextualize these changes and further emphasize the added utility of this metric, the readership needs to learn how the SWE curve has changed in various parts of the U.S. to understand why the SwS has increased or decreased, and how the SwS thus provides more/added information compared to other metrics. Translating what is presented in Figure 1 to the real/raw observed and modeled data which are used in the presented work is a critical missing component to this work and would add more intuition around the new metric. Toward the tail end of the discussion, readers learn that “the conceptual SWE curve has been flattening over the 39-year period of record,” which is the first mention of how the SWE curve has changed (not just monthly or annual SwS % changes), by way of the SwS evaluation, and thus provides valuable, new information (but also leaves the reader questioning, for example, how is this different from a lower April 1 SWE value? What more does this tell us about the changing snow season?). These questions need to be directly addressed (and seemingly can be, by way of the information gained from SwS). Since the metric leans on the important of temporal changes in the snow season – in addition to changes in magnitude, and thus a novel combination of snowpack characteristics – the changes in SWE curve shape need to be reported throughout the manuscript when % changes are stated (with complementary figures, ideally). This will greatly assist the readership reach the intended conclusions made in the manuscript. The manuscript would benefit from further elaborating on other, recent metrics aimed at quantifying snow water storage (e.g., Hale et al., 2023; Immerzeel et al., 2020) and being more specific in naming changes seen within individual ER3s (instead of “only one” or “four” ER3s, the authors should state the specific areas of reference), such that comparisons can be further made between the SwS and observed changes in other metrics.
Specific and technical comments are provided in the supplementary document attached.
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RC2: 'Comment on egusphere-2023-596', Anonymous Referee #2, 27 Aug 2023
General comments：
The study defines a new snow metric, snow water storage (SwS), which is the integrated area under the snow water equivalent (SWE) curve and analyzed the SwS change in America during 1982-2020. It is a good idea to define a new metric (SwS) to express amount of snow water over a time period (water year, a given month, etc.) of interest, and the manuscript is well organized.
Specific comments：
(1) The SwS is from SWE integrated over time and SWE itself is the amount of snow accumulated rather than an increase at the corresponding time. The physical meaning of this metric (SwS) doesn’t seem to be clear. It seems easier to understand if the SWS is divided by the corresponding time, that is, the average SWE over a period. Moreover, some studies have analyzed the change of mean SWE in some regions over corresponding time periods e.g., Pulliainen et al (2020). Why did you define new metric of SwS instead of average SWE?
(2) The snow water storage (SwS) indicated snow mass or average SWE in some studies like Pulliainen et al (2020), Kwon et al 2016,2017, Hale et al 2023, which indicated different meaning in your study. Perhaps you should make a distinction, such as highlighting the meaning in the introduction, or changing the name of your metric.
Technical corrections:
Line 11: ‘snow snow season’ might be ‘snow season’.
Line3.1: Might ‘SwS trend’ be ‘SwS change trend’? as well as in Line 176, 204 and other places in the manuscript.
Line 257: ‘The was an 18%’?
Line 257-259: This sentence does not correspond to Table 3.
Line 259: Why do not picture?
Line 278: There are double ‘that’.
Line 280: ‘are know known’ might be ‘are known’.
Line 311: Harpold et al. (2012) might be (Harpold et al., 2012).
It might be better to understand if Figure 4 has same extent and scale with Figure 5 and 6.
Line 182 and 183: There are Northern and Middle Rockies、Southern Rockies and in the Cascades. Can you show their locations in Figure2 although I saw there are mountain names in Table 2 in the later section. The mountains names indicated by numbers should be noted in Figure2 or front of the manuscript.
References:
Hale, K. E., Jennings, K. S., Musselman, K. N., Livneh, B., & Molotch, N. P. (2023). Recent decreases in snow water storage in western North America. Communications Earth & Environment, 4(1).
Kwon, Y., Yang, Z. L., Zhao, L., Hoar, T. J., Toure, A. M., & Rodell, M. (2016). Estimating snow water storage in North America using CLM4, DART, and snow radiance data assimilation. Journal of Hydrometeorology, 17(11), 2853-2874.
Kwon, Y., Yang, Z. L., Hoar, T. J., & Toure, A. M. (2017). Improving the radiance assimilation performance in estimating snow water storage across snow and land-cover types in North America. Journal of Hydrometeorology, 18(3), 651-668.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., ... & Norberg, J. (2020). Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581(7808), 294-298.
Citation: https://doi.org/10.5194/egusphere-2023-596-RC2
Christina Marie Aragon and David Foster Hill
Christina Marie Aragon and David Foster Hill
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