Multi-scale snowdrift-permitting modelling of mountain snowpack

Abstract

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.

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

“…Relevant drivers of snowpack and glacier evolution that are not yet included comprise internal snowpack energetics and the full energy balance, longwave losses, turbulent heat fluxes, sublimation, and canopy-snow interactions. While the recent literature has shown that the added value of complex, physics-based snow models over more parsimonious alternatives for variables that are relevant to hydrology may sometimes be elusive (Rutter et al, 2009;Magnusson et al, 2015;Zaramella et al, 2019;Girons Lopez et al, 2020;Günther et al, 2019), designing more holistic and physics-based operational models remains a key to achieving the most accurate representation of snow temporal dynamics and spatial patterns, especially at fine resolutions (see, for example, the results in Lafaysse et al, 2017;Vionnet et al, 2021). Strategies to include these processes in future releases of S3M are discussed in Sect.…”

Mass balance modelling and climate sensitivity of Saskatchewan Glacier, western Canada

“…More sophisticated wind downscaling (e.g. Vionnet et al, 2021;Wagenbrenner et al, 2016) could help improve further modelling at this site and other upland icefield-outlet valley glacier settings.…”

Wind‐Topo: Downscaling near‐surface wind fields to high‐resolution topography in highly complex terrain with deep learning

“…Other types of models like WindNinja (Wagenbrenner et al, 2016;Rios et al, 2018;Hilton and Garg, 2021) and TopoSCALE (Fiddes and Gruber, 2014) are based on physical descriptions of wind-topography interactions and are not bound to the domain of calibration. WindNinja is used frequently for wildfire propagation, but was used recently by Vionnet et al (2021) to model snowdrift. However, given their generic nature, those methods do not benefit from the valuable information of measurements or high-resolution simulations and thus might not perform well in particular terrain under particular conditions.…”

“…WindNinja is used frequently for wildfire propagation, but was used recently by Vionnet et al . (2021) to model snowdrift. However, given their generic nature, those methods do not benefit from the valuable information of measurements or high‐resolution simulations and thus might not perform well in particular terrain under particular conditions.…”

“…In highly complex terrain, given the required resolution, large domains cannot be modeled for long periods. The modeling of many surface processes like snow preferential deposition and transport, however, requires long time series of high‐resolution wind fields on large domains (Mott et al ., 2010; Vionnet et al ., 2021). This is also true for wind‐energy potential assessment in such types of terrain, where spatial variability is high.…”