Threshold wind speed for snow movement is one of the most important parameters describing the wind‐transport snow process. A majority of previous studies used empirical and constant threshold wind speeds while the variations of atmosphere condition and snow age seem to greatly affect the snow settlement process. This study tested the hypothesis that the threshold wind speed for snow transport increases as deposition time passed since last snowfall by introducing a new formula of the threshold wind speed for snow movement. It was theoretically derived based on the sintering process modeling and the moment balance of a snow particle. Through this formula, the influences of snow particle size, temperature, and deposition time on the threshold wind speed were explicitly taken into consideration. However, two empirical parameters in the sintering process modeling remained in the new formula, and they were determined by a calibration‐validation procedure using the field observed snow flux data. The snow flux and meteorological data collected at the ISAW stations (http://www.iav.ch) including large number of snow transport events during five winter seasons were used to test this formula. It was shown that the new formula qualitatively described the threshold wind speed required for the incipient motion of snow under various conditions in the natural environment of the Alps.

It is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large-scale hydrological and land surface models. High-resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability.In this study, snow depth was measured by airborne Lidar at 0.5-m resolution over two mountainous areas in south-western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1-km × 1-km. Also, nine representative grids of 1-km × 1-km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1-km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non-Gaussian distribution, largely due to the no-snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth. KEYWORDS fractal dimension, no-snow areas effect, probability density function, snow, subgrid variability, variogram

Snowdrift, which results from deposition of wind transported snow, has been primarily estimated empirically rather than using physically-based modelling since the snow redistribution process is extremely complex. This study demonstrates a practical predictive model for snow redistribution based on the Linear Particle Distribution equation, which consists of snow surface diffusion, snow surface advection, and snow surface erosion components. Here, we focus on numerical model development and implementation for two-dimensional natural terrains at meter-scale resolutions with and without perforated snow fences, which has been difficult to model in a twodimensional field. First, a selected numerical scheme was implemented in the Snow Movement Over Open Terrain for Hydrology model platform and tested by the exact solutions under a few well-defined boundary conditions. Then, to simulate snowdrifts around the snow detention structures in the middle of the computational domain, an equivalent solid snow fence concept was introduced and tested. The model was applied to several terrains in the Laramie Range, Wyoming, and at two sites on the North Slope of Alaska, where wind-induced snow redistribution plays a major role.Data from Airborne Light Detection and Ranging, Ground Penetrating Radar, and Unmanned Aerial Vehicle photogrammetry were used to calibrate and validate the model. The numerical snow redistribution model effectively reproduces the observed snowdrift distributions when snow densification and snowmelt effects were minimal.The model applications illustrated that the diffusion effect generally dominated snow redistribution with limited contributions of advection and erosion effects for abrupt terrain transition and perforated object, respectively.