Density of Seasonal Snow in the Mountainous Environment of Five Slovak Ski Centers

Climate change affects snowpack properties indirectly through the greater need for artificial snow production for ski centers. The seasonal snowpacks at five ski centers in Central Slovakia were examined over the course of three winter seasons to identify and compare the seasonal development and inter-seasonal and spatial variability of depth average snow density of ski piste snow and uncompacted natural snow. The spatial variability in the ski piste snow density was analyzed in relation to the snow depth and snow lances at the Košútka ski center using GIS. A special snow tube for high-density snowpack sampling was developed (named the MM snow tube) and tested against the commonly used VS-43 snow tube. Measurements showed that the MM snow tube was constructed appropriately and had comparable precision. Significant differences in mean snow density were identified for the studied snow types. The similar rates of increase for the densities of the ski piste snow and uncompacted natural snow suggested that the key density differences stem from the artificial (machine-made) versus natural snow versus processes after and not densification due to snow grooming machines and skiers, which was relevant only for ski piste snow. The ski piste snow density increased on slope with decreasing snow depth (18 kg/m³ per each 10 cm), while snow depth decreased 2 cm per each meter from the center of snow lances. Mean three seasons maximal measured density of ski piste snow was 917 ± 58 kg/m³ the density of ice. This study increases the understanding of the snowpack development processes in a manipulated mountainous environment through examinations of temporal and spatial variability in snow densities and an investigation into the development of natural and ski piste snow densities over the winter season.

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What drives basin scale spatial variability of snowpack properties in northern Colorado?

The Cryosphere ◽

10.5194/tc-8-329-2014 ◽

2014 ◽

Vol 8 (2) ◽

pp. 329-344 ◽

Cited By ~ 24

Author(s):

G. A. Sexstone ◽

S. R. Fassnacht

Keyword(s):

Linear Regression ◽

Spatial Variability ◽

Multiple Linear Regression ◽

Snow Depth ◽

Density Model ◽

Snow Density ◽

Data Set ◽

Depth Measurement ◽

Basin Scale ◽

Snowpack Properties

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km2). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time-consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

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