Quanitfying snow depth distributions and spatial variability in complex mountain terrain

Abstract

The spatial variability of snow depth is a major source of uncertainty in avalanche and hydrologic forecasting. Identification of spatial and temporal patterns in snow depth is further complicated by the interactions of complex mountain topography and localized micro-meteorology. Recent studies have dramatically improved our understanding of snow depth spatial variability by utilizing increasingly accessible remote sensing technologies such as satellite imagery, terrestrial laser scanning, airborne laser scanning and uninhabited aerial systems (UAS) to map spatially continuous snow depths over a variety of spatiotemporal scales. However, much of this work focuses on relatively low-relief topographies or limited temporal frequencies. Our research presents a thorough evaluation of the evolution of snow depth spatial variability at the slope scale in steep complex mountain terrain (45.834 N, -110.935 E) using analysis from UAS imagery. We apply 13 spatially complete UAS-derived snow depth datasets collected throughout the course of the 2019/2020 winter to analyze spatial and temporal patterns of snow depth and snow depth change variability. Our results show greater spatial variability in steep complex mountain terrain than an adjacent mountain meadow both in the seasonal context and during individual meteorological periods. We analyze 2 cm horizontal resolution snow depth models by (i) comparing spatial patterns with coincident meteorological data, (ii) analysis of the temporal elevation specific patterns of snow depth, and (iii) a comprehensive multi-scalar evaluation of spatial variability. We quantify the unique spatial signature of four specific events: a major snow accumulation, a natural avalanche, a calm period, and a significant wind event. We find a non-linear relationship between elevation and snow depth, with upper elevations proving to be the most variable. We also verify that significant storm events result in the largest snow depth change variability throughout our study area, as compared to other meteorological events. The synthesis of these findings illustrate the dynamic spatial and temporal snow depth distribution patterns observed in complex mountain terrain during the course of a winter season. These findings are relevant to avalanche forecasters and researchers, snow hydrologists and local water resource managers, and downstream communities dependent on snow as a hydrologic reservoir.