ALBERT

Description: High resolution snow depth maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km 2 ) in the Pyrenees were used to assess small scale variability of the snowpack at the catchment and sub-grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49 and 99 × 99 m), and 8 different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and snow depth, topographic variables, and small scale variability in topographic variables. The results showed that there was marked variability in snow depth, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small scale variability, but this increased sharply with the onset of melting. The coefficient of variation in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. Snow depth best explained the spatial distribution of sub-grid variability. Topographic variables including slope, wind sheltering, sub-grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the coefficient of variation of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small scale variability of the snow depth for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using snow depth and other predictors to design optimized surveys for acquiring distributed snow depth data. This article is protected by copyright. All rights reserved.