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.