Small-Scale Spatial Structure of Shallow Snowcovers

Abstract

The results of a field study of the small-scale spatial structure of the depth of shallow seasonal snowcovers in prairie and arctic environments are presented. It is shown that the spatial distribution of snow depth is fractal at small scales, becoming random at scales beyond some limiting length. This is due to the autocorrelation of depth at small sampling distances. The transition of fractal to random behaviour is indexed by a 'cutoff length', which is defined by the intersection of the 'fractal' slope and horizontal tangent of a logarithmic plot of the standard deviation of depth versus sampling distance.The magnitude of the cutoff length is related to the degree of macroscopic variability of the underlying topography. An increase in length due to the effects of macroscopic topographic variability on snowcover accumulation is confirmed by de-trending field measurements. The de-trended data shown a cutoff length for wheat stubble and fallow surfaces of approximately 30 m, which is consistent with the distance determined from measurements on 'flat' fields.The implications of the transition of snow depth from fractal to random structure on the scales of snow sampling and modelling are presented. The cutoff length may provide a statistic for stratifying shallow snowcovers, by linking snowcover properties to the underlying topography.

Multiscale spatial variability of lidar-derived and modeled snow depth on Hardangervidda, Norway

“…This result is consistent with observations from previous studies (e.g. Shook and Gray, 1996; Marchand and Killingtveit, 2005; Clark and others, 2011). Figure 3 illustrates the spatial variability of snow depth along a ∼500 m wide and 4000 m long transect.…”

Are flat‐field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale

“…The increase rate is largest at small distances and flattens at distances of about 100 m. This indicates that the largest variability of snow depths is present at short distances. This is in accordance with the findings of Shook and Gray (), Watson et al . () and Schirmer and Lehning () who reported the highest variability of snow depth at scales of metres to tens of metres.…”

Spatial Distribution and Scaling Properties of Lidar‐Derived Snow Depth in the Extratropical Andes

“…The results from transect analyses across all Andean sites confirm that the primary scale break L 1 is a measure of the average distance d between local peaks in snow depth, as reported by Trujillo et al (2007) for the Colorado Rocky Mountains, following earlier suggestions that the formation of snow drifts define subdomains of high spatial autocorrelation (Shook & Gray, 1996). Past studies have attributed the existence and location of local snow depth peaks to interactions between wind, snow, and land characteristics—e.g., terrain exposure, local irregularities, vegetation (when present)—whose relative importance varies depending on local site conditions (e.g., Schirmer & Lehning, 2011; Trujillo et al, 2007, 2009).…”