Preprints
https://doi.org/10.5194/egusphere-2024-395
https://doi.org/10.5194/egusphere-2024-395
12 Apr 2024
 | 12 Apr 2024
Status: this preprint is open for discussion.

Characterization of Non-Gaussianity in the Snow Distributions of Various Landscapes

Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão

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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Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão

Status: open (until 11 Jun 2024)

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Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão

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Short summary
Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in-situ observations and remote sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. It was found non-Gaussianity of snow distribution is a signature of wind redistribution effect. Generally, seasonal snowpack can be well approximated by Gaussian distribution for fully snow-covered area.