Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modeling approach is proposed to simulate the temporal and spatial evolution of high mountain snowpacks using the Canadian Hydrological Model (CHM), a multi-scale, spatially distributed modelling framework. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing snow redistribution and sublimation, avalanching, forest canopy interception and sublimation and snowpack melt. Short-term, km-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM, and were downscaled to the unstructured mesh scale using process-based procedures. In particular, a new wind downscaling strategy combines meso-scale HRDPS outputs and micro-scale pre-computed wind fields to allow for blowing snow calculations. HRDPS-CHM was applied to simulate snow conditions down to 50-m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (~1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne Light Detection and Ranging (LiDAR) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both blowing snow and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of wind-blown snow on leeward slopes and associated snow-cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture leeside flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

Abstract Horizontal and altitudinal redistribution of snow by wind transport and avalanches can be important controls on small- and large-scale snow accumulation patterns that control meltwater supply in alpine environments. Redistribution processes control the spatial variability of snow accumulation, which not only controls meltwater supply, but also regulates snowmelt timing, duration, and rates, as well as snow-covered area depletion and the variable contributing area for meltwater runoff generation. However, most hydrological models and land surface schemes do not consider snow redistribution processes, and those that do are difficult to verify without spatially distributed snow depth measurements. These are rarely available in both high resolution and covering large scales. As an increased number of hydrological models include snow redistribution processes there is a need for additional snowcover metrics to verify snow redistribution schemes over large areas using readily available data. This study develops novel high-resolution (20 m), snowcover indices from remotely sensed imagery (Landsat-8 and Sentinel-2) to evaluate snow redistribution models over alpine areas without in-situ or airborne snow observations. A snowcover absence (SA) index, calculated from snow-free areas during the winter, identifies areas of wind erosion or avalanche source areas. A snowcover persistence (SP) index, calculated from snow-covered areas during the summer, identifies snow deposition in drifts and avalanche deposits. The snowcover indices captured the relative differences in surface observations of snow presence and absence between exposed and sheltered sites on an intensely instrumented ridge in the Canadian Rockies Hydrological Observatory. Within the Tuolumne River Basin in central California (1100 km2), the SP index captured roughly half of the spatial variability (R2 = 0.49 to 0.56) in peak SWE as estimated from airborne LiDAR-derived snow depths. At the individual mountain ridge scale (~800 m), variability in both ablation and snow redistribution controlled the SP patterns over 7979 ridges. Differences in shortwave irradiance explained 76% of the SP variance across ridges, but could not explain smaller-scale (~100 m) SP peaks that are associated with snowdrifts and avalanche deposits. The snowcover indices can be used to evaluate snow redistribution models of the finer scale impacts of snow redistribution by wind and gravity as long as the larger scale influences of spatially variable solar irradiance effects are also simulated.