the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Characterization of Non-Gaussianity in the Snow Distributions of Various Landscapes
Abstract. Seasonal snowpack is an important predictor of available water resources in the following spring and early summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy, the Kullback–Leibler divergence from Gaussian distribution, for field-observed snow depth data on the North Slope, Alaska, and three representative SWE distributions in the western US from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks in slope and other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western US if the land is completely covered by snow. The wind-induced snowdrift effect can be potentially partitioned from the observed snow spatial distribution guided by its Gaussianity.
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RC1: 'Comment on egusphere-2024-395', Anonymous Referee #1, 12 Jun 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-395/egusphere-2024-395-RC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-395', Anonymous Referee #2, 22 Jul 2024
Review of the paper “Characterization of Non-Gaussianity in the Snow Distributions of Various Landscapes” by Ohara et al.
The topic of this paper is interesting. Representing the spatial variability of snow in modeling has been a longstanding challenge, with various approaches proposed by different researchers. However, none of these approaches has proven superior to the others. This paper provides a good test of the idea of using negentropy to evaluate the non-Gaussianity of snow. I recommend accepting the paper with minor revisions. Here are some comments from my perspective.
General comments:
- From the snow depth survey using GPR in Inigok (Figure 4), this study mentions that 'the snowdrift due to steep terrain is considered a major source of non-Gaussianity'. We know that the terrain over the Tuolumne River and East River Watersheds varies dramatically, and I would expect strong non-Gaussianity from these watersheds. However, the computed negentropy for fully snow-covered cells in these watersheds was quite small. Could the authors explain why this is different from the conclusion drawn from Figure 4?
- Based on the calculated negentropy, this paper mentions that 'Most of the fully snow-covered areas fell into the category almost Gaussian.' I am curious if this is a conditional conclusion since the paper lacks information on the sensitivity of this index to the spatial scale. For example, the paper uses a 30-meter moving window and a 1500-meter moving window for different datasets. Would such inconsistency be a concern in drawing the conclusion?"
- I wonder if this paper can include a paragraph in the discussion section to explicitly mention the advantages of using negentropy in describing snow distribution. Otherwise, there are other simple statistical metrics, such as skewness and kurtosis, that can identify non-Gaussianity straightforwardly.
Specific comments:
Line 107, Need to explain what is px.
Citation: https://doi.org/10.5194/egusphere-2024-395-RC2
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