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

Abstract

We characterize elevational gradients, probability distributions, and scaling patterns of lidar-derived snow depth at the hillslope scale along the extratropical Andes. Specifically, we analyze snow depth maps acquired near the date of maximum accumulation in 2018 at three experimental sites: (i) the Tascadero catchment (31.26 degrees S, 3,270-3,790 m), (ii) the Las Bayas catchment (33.31 degrees S, 3,218-4,022 m); and (iii) the Valle Hermoso (VH) catchment (36.91 degrees S, 1,449-2,563 m). We examine two subdomains in the latter site: one with (VH West) and one without (VH East) shrub cover. The comparison across sites reveals that elevational gradients are site-dependent, and that the gamma and normal distributions are more robust than the lognormal function to characterize the spatial variability of snow depth. Multiscale behavior in snow depth is obtained in all sites, with up to three fractal regimes, and the magnitude of primary scale breaks is found to be related to the mean separation distance between local snow depth peaks. The differences in snow depth fractal parameters between VH West-the only vegetated subdomain-and the remaining sites suggest that local topographic and land cover properties are dominant controls on the spatial structure of snow, rather than average hydroclimatic conditions. Overall, the results presented here provide, for the first time, insights into the spatial structure of snow depth along the extratropical Andes Cordillera, showing notable similarities with other mountain regions in the Northern Hemisphere and providing guidance for future snow studies.