Snow Distribution Research Articles

CFD Simulations of Snowdrifts on a Gable Roof: Impacts of Wind Velocity and Snowfall Intensity

Roof structures are suffering serious threats caused by unbalanced snow distribution, especially long-span spatial structures, such as gable roofs. The formation of unbalanced snowdrifts on the gable roof is affected by the meteorological condition and the drifting snow. This study was conducted to explore the snowdrift characteristics on gable roofs under different snowfall conditions based on a new Eulerian–Eulerian multiphase approach. To consider the diffusive process of snow in different states, the governing equations of air and snow phases were modified separately according to the actual transport process. Additional terms based on the deposition/erosion process were inserted into the governing equations to consider the processes of snow particles being trapped or ejected by snow surface. The feasibility of the new model for the snowdrift was validated by comparing with a field observation. Then, the snowdrifts characteristics on typical gable roofs were investigated under different wind velocity and snowfall intensity conditions. The formation mechanism of snowdrifts and the influence of meteorological conditions on snowdrifts were clarified by analysis. The results show that the uneven distribution of snow on the gable roof becomes more significant with the increase in wind velocity. Furthermore, the distribution of snow on the roof tends to be more even in the case of heavier snowfall.

Optimizing embankment structures in a snowy permafrost region of the pan-Arctic based on a coupled numerical model

In pan-Arctic permafrost regions, snow drift may lead to more snow accumulation around the road, which prevents the heat release from the underlying soil in winter and aggravates the permafrost degradation beneath the road. However, the study of snow distribution patterns around the road is insufficient, which makes it difficult to accurately evaluate the long-term stability of the road. To investigate the thermal regime of the road and optimize the road structure in permafrost regions under the effect of snow drift, based on heat transfer and multiphase flow theories, a coupled numerical model is presented. The model includes snow transportation around the embankment, natural convection of air in the crushed-rock layer, and heat conduction in soil and snow layers. The results of model simulation show that an embankment with a height of 4 m leads to heavier snow accumulation on both sides of the embankment in winter compared to an embankment with a height of 3 m, which accelerates permafrost degradation. The full crushed-rock embankment with a height of 3 m can be used to counteract the permafrost degradation caused by snow accumulation to some extent, while the soil temperature beneath the toe of the embankment slope is still noticeably higher. By contrast, when the U-shaped crushed-rock embankment (UCRE) with a height of 3 m and 1:6(vertical: horizontal) side slopes is used, the snow accumulation around the embankment is greatly reduced, the embankment slope delivers better cooling performance. Considering the climate warming at a rate of 0.067 ℃ year−1 in pan-Arctic regions, the thermal stability of the UCRE can still be ensured after 20 years of its construction. These results provide a valuable reference for road construction in pan-Arctic snowy permafrost regions.

Evaluation of the long-term thermal stability of a crushed-rock revetment embankment in pan-Arctic permafrost regions under the effect of snow drift

In the pan-Arctic region, snow drift events usually result in heavy snow accumulations on both sides of road embankment which severely affects the energy exchange process at the embankment slopes and further changes the temperature distribution in the embankment and its subsoil. However, there is a lack of research on the prediction of snow distribution patterns around the embankment, which makes it difficult to accurately assess the impact of snowdrift on the thermal stability of the embankment in permafrost regions, and the embankment structure cannot be reasonably optimized accordingly. Herein, using the theories of two-phase flow and heat transfer, a numerical model is developed to evaluate the long-term thermal stability of embankments with different structures under the effect of snow drift. The results show that snow deposition occurs at the toe of the windward side slope and the whole leeward side of the embankment under the effect of snow drift. If the side slope of the embankment is reduced from 1:2 to 1:5 (vertical:horizontal), the snow accumulations on both sides of the embankment are significantly reduced, and the subsoil refreezes more effectively in cold season. When a crushed-rock revetment embankment (CRRE) with 1:5 side slopes is used, the soil beneath the embankment is sufficiently cooled, the warming of permafrost beneath both sides of the embankment caused by snow accumulation is eliminated, and the temperature distribution in the embankment and subsoil has a good symmetry in warm season. The crushed-rock revetment is thus proved to be an applicable and effective structure to ensure the long-term stability of the embankment in pan-Arctic permafrost regions.

Field measurement and outdoor wind tunnel test of snow-drifting: Snow distribution characteristics of railway subgrade and deposition mechanism

The snow deposits caused by snow-drifting are closely associated with railway cutting forms, which affect wind distribution and snow particle motion. Neither indoor wind tunnel tests using simulated material, nor tests which involve moving snow into a low-temperature wind tunnel, can reproduce the real properties and states of snow particles. This paper introduced a set of mobile low-speed direct current wind tunnel equipment, and outdoor wind tunnel tests were carried out to study the snow-drifting effect of railway cutting during snowfall.By adjusting the wind tunnel to simulate the natural flow field, and compared with the field monitoring results to verify the rationality of the wind tunnel design and the accuracy of results. According to the actual snowfall conditions, snow-drifting was tested under different forms of railway cutting and different inflow conditions, and the real-time kinematic photogrammetry was used to establish three-dimensional models for snow depth analysis. Finally, the mechanism of snow deposition was explained from the dynamic equilibrium shift of snow-drifting movement.

Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

Numerical Simulation and Forecasting Model of Snow Drift Hazard in Yanchong Expressway with Embankment Form

To reasonably predict the snow drift hazard of expressway embankment in alpine mountainous area, in this paper, the computational fluid dynamics (CFD) numerical simulation method is used to analyze the distribution law of flow field of snow drift, the regression orthogonal design method is used to establish the prediction model of snow drift hazard in the expressway with the embankment form. Results show that different factors have different effects on snow drift hazard, the influence of embankment slope is the largest, followed by the coupling term of embankment slope and wind speed, the wind speed, the coupling term of embankment height and embankment slope, the coupling term of the mountain relative height and embankment slope, and the embankment height, while the influence of the distance between embankment and mountain-embankment slope coupling is the smallest. Numerical simulation and field observation have confirmed that the combined action of the terrain and embankment of Yanchong expressway Chicheng branch line leads to great changes in wind speed, resulting in uneven distribution of snow. A large amount of snow can be accumulated at the foot of the leeward slope of the mountain and the windward slope of the embankment, and there is less snow on the embankment pavement. By comparing the snow drift prediction model and CFD numerical simulation results, the error of the prediction model established in this paper is only 8.67%, which has high applicability. The research of this paper can provide a theoretical reference for the prediction and prevention of snow drift hazard on expressway embankment in alpine mountainous area.

Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

Abstract. The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

Spatial patterns of snow distribution in the sub-Arctic

Abstract. The spatial distribution of snow plays a vital role in sub-Arctic and Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019; ∼ 2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22 000 data points), we developed simple models of the spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was random forest, using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, TPI), while slope, wind, and fine microtopography factors were less important. The characterization of the SWE spatial distribution patterns will be used to validate and improve snow distribution modeling in the Department of Energy's Earth system model and for improved understanding of hydrology, topography, and vegetation dynamics in the sub-Arctic and Arctic regions of the globe.

The spatial and temporal influence of infrastructure and road dust on seasonal snowmelt, vegetation productivity, and early season surface water cover in the Prudhoe Bay Oilfield

Increased industrial development in the Arctic has led to a rapid expansion of infrastructure in the region. Localized impacts of infrastructure on snow distribution, road dust, and snowmelt timing and duration feeds back into the coupled Arctic system causing a series of cascading effects that remain poorly understood. We quantify spatial and temporal patterns of snow-off dates in the Prudhoe Bay Oilfield, Alaska, using Sentinel-2 data. We derive the Normalized Difference Snow Index to quantify snow persistence in 2019–2020. The Normalized Difference Vegetation Index and Normalized Difference Water Index were used to show linkages of vegetation and surface hydrology, in relationship to patterns of snowmelt. Newly available infrastructure data were used to analyze snowmelt patterns in relation infrastructure. Results show a relationship between snowmelt and distance to infrastructure varying by use and traffic load, and orientation relative to the prevailing wind direction (up to 1 month difference in snow-free dates). Post-snowmelt surface water area showed a strong negative correlation (up to −0.927) with distance to infrastructure. Results from field observations indicate an impact of infrastructure on winter near-surface ground temperature and snow depth. This study highlights the impact of infrastructure on a large area beyond the direct human footprint and the interconnectedness between snow-off timing, vegetation, surface hydrology, and near-surface ground temperatures.

Simulating the extent and depth of spring snow cover for medieval settlements in Iceland and Greenland

Medieval settlements in Iceland and Greenland were vulnerable to changes in spring (April-June) snow cover duration and depth. These would have adversely affected the viability of their pastoral farming systems, but the impact would have been spatially variable. We use a physical-based model of snow distribution and melt to model spring snow cover and depth at a scale relevant to human activities across four sites: southern and northern Iceland, and inner and middle fjord sites in South Greenland, using both present day and simulated climate data from the HadCM3 GCM-model. Our climate scenarios cover the period CE 1000–1500, encompassing a climate shift to cooler conditions. We find that under average present climate conditions the inner fjord site in Greenland has similar spring snow conditions to sites in Iceland, but that the middle fjord site has notably greater snow cover, and as climate cools spring snow cover at this site becomes extensive (>60 days). The largest increase in snow cover duration between current average climate conditions and the coolest climate scenarios (47 days increase) is experienced at our Iceland sites. Inner and middle fjord sites in Greenland diverge in terms of snow cover under all scenarios, a potential driver of the growing importance of marine wild resources and the end of the Norse Greenland settlement.

Numerical simulation of snowdrift on an air-supported membrane structure and response analysis under snow loads

The air supported membrane structure is a typical nonlinear flexible long-span space structure, the wind-induced drift and the resulting accumulation and distribution of snow particles on the structure may be the primary design concern among all loads in heavy-snowfall region. Thus, an accurate prediction of snow distribution on membrane surface is vital to structural design. A numerical simulation method is used to estimate snowdrift in this paper. Based on Euler-Euler method in multi-phase flow theory, this numerical model adopted Mixture model and combined with the snow deposition and erosion model, the snowdrift on an air-supported membrane coal shed is simulated, the distribution factor for roof snow load is given under different wind speed and different directions to estimate the worst load case, snow load on the air-supported membrane structure is significantly affected by snowdrift which causes significant non-uniform snow load. Furthermore, the response analysis of the air-supported membrane structure under snow load is studied, for comparison, uniform snow load case, non-uniform snow load case, and simulated snow load case under 0° wind direction are all considered. The results show that non-uniform snow load caused by snow drifting is more dangerous and should be considered.

Low winter temperatures and divergent freezing resistance set the cold range limit of widespread alpine graminoids

AbstractAim“Where and why does a species exist” is a fundamental question in ecology. However, the actual range limits of alpine plant species are largely unexplored and unexplained. We aim at identifying the cold range limits of the two most abundant alpine graminoid species on acidic soils that intermingle in mosaics of high‐elevation habitats across the European Alps.LocationAlpine grasslands in the Swiss Alps.TaxonCarex curvula (Cyperaceae) and Nardus stricta (Poaceae).MethodsWe assessed temperature 3 cm below ground, closest to the plant meristems, year‐round as well as snow cover duration, soil chemistry and vegetation characteristics at high spatio‐temporal resolution at 115 microsites. Field data were combined with various freezing resistance analyses in plant individuals at 38 microsites by employing mixed regression models.ResultsCarex and Nardus segregated across different microsites. Season length, growing degree hours and soil chemistry did not demarcate the two species' ranges, while their distribution was strongly affected by soil minimum temperature in winter. Carex occurred at sites with and without protecting snow cover and resisted low soil temperatures (−13°C). Nardus was absent at microsites with snow cover duration < 5 months and soil minimum temperatures below −5°C. During the growing season Carex had a higher leaf/shoot freezing resistance (lethal temperature for 50% of the leaf tissue LT50: −16.1°C) than Nardus (−13.3°C). Shoot apices tolerated lowest temperatures: Carex −30°C, Nardus −24°C. Though, a vital shoot apex alone did not ensure regrowth after winter, but intact vessels and roots were required, all less freezing tolerant than apical meristems and young leaves.Main conclusionsThe cold range limits of these widespread alpine graminoids are evidently set by thermal extremes in winter. Microtopography, thus snow distribution pattern, in concert with the species' freezing resistance explains the cold edge of the fundamental niche of these two species.

Investigation of effects of repose angle and critical sliding angle of snow on curved roofs

Accompanied by extreme snowy weather occurred frequently, the number of collapse accidents of structures which are caused by uneven snowdrift is increasing. The formation of uneven snowdrift on the roofs is substantially related to snow profile. Especially to curved roofs, snow profile has certain peculiarity due to curvature of roof edge. The inter-particle properties such as repose angle and critical sliding angle of snow also affect snow profile on curved roofs. This study is to systematically investigate laws of inter-particle properties with measuring, provide a revision approach to accurately restore snow profile considering inter-particle properties, and validate the feasibility of approach by comparing simulation results with that of wind tunnel test. Firstly, a series measuring of repose angle is conducted and results show that repose angle is about 60°. Then the wind tunnel test on curved roofs is carried out to investigate properties of critical sliding angle and found that critical sliding angle is around 60°. Based on inter-particle properties, a revision approach of snow profile is proposed. Snow distribution of numerical simulation with revised snow profile is more in line with wind tunnel test. It is found that snow profile which is revised by inter-particle properties influences characteristics of flow field around roofs and snowdrift on curved roofs.

Spring snowmelt affects changes of alpine tundra vegetation in Changbai Mountains

AbstractClimate and environmental changes have caused changes in alpine tundra worldwide in recent decades. However, the roles of snow cover on tundra vegetation change remain less studied. There were obvious changes of alpine tundra vegetation, such as herbaceous encroachment and shrub expansion reported in Changbai Mountains in recent decades. This study investigates the relationship between snow cover and vegetation change (1988–2017) in alpine tundra of the Changbai Mountains by analysing the hydrological distribution of spring snowmelt in the tundra. We mapped the wet snow extent using Sentinel‐1 synthetic aperture radar (SAR) spaceborne data and estimated the snow water equivalent (SWE). We proposed a measure of relative water accumulation (RWA), which was determined by both rainfall and SWE, to represent water flowing through each pixel. We found strong correlations between RWA and vegetation change in different mountain aspects. Spring snowmelt leads to inter‐mountain aspect differences in hydrological distribution, with the western aspect showing a significant higher RWA than the eastern and northern aspects, which could attribute to the greater vegetation change in the western mountain aspect. Our results suggest that the distribution of snow in alpine tundra and the different mountain aspects hydrological conditions have important implications for the study of vegetation change in alpine tundra.

Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution, including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snowmelt estimation in mountainous regions is, however, further exacerbated by data scarcity. This study attempts to develop relatively simple extended degree-day snow models driven by freely available snow-cover images. This approach offers relative simplicity and a plausible alternative to data-intensive models, as well as in situ measurements, and has a wide range of applicability, allowing for immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snowmelt models on spatial snow distribution in contrast to the traditional snow-water-equivalent-based calibration. The spatial distribution of snow-cover is simulated using different extended degree-day models with parameters calibrated against individual MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg (Germany) and in Switzerland. The simulated snow-cover data show very good agreement with MODIS snow-cover distribution, and the calibrated parameters exhibit relative stability across the time domain. Furthermore, different thresholds that demarcate snow and no-snow pixels for both observed and simulated snow cover were analyzed to evaluate these thresholds' influence on the model performance and identified for the study regions. The melt data from these calibrated snow models were used as standalone inputs to a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) without the snow component in all the study catchments to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show an overall increase in Nash–Sutcliffe efficiency (NSE) performance and a reduction in uncertainty in terms of model performance. This can be attributed to the reduction in the number of parameters available for calibration in the modified HBV and an added reliability of the snow accumulation and melt processes inherent in the MODIS calibrated snow model output. This paper highlights that the calibration using readily available images used in this method allows for a flexible regional calibration of snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data and globally available inputs. Likewise, the study concludes that simpler specific alterations to processes contributing to snowmelt can contribute to reliably identify the snow distribution and bring about improvements in hydrological simulations, owing to better representation of the snow processes in snow-dominated regimes.

FlakeOut: A geometric approach to remove wind-blown snow from terrestrial laser scans

Wind-blown snow particles often contaminate Terrestrial Laser Scanning (TLS) data of snow covered terrain. However, common filtering techniques fail to filter wind-blown snow and incorrectly filter data from the true surface due to the spatial distribution of wind-blown snow and the TLS scanning geometry. We present FlakeOut, a filter designed specifically to filter wind-blown snowflakes from TLS data. A key aspect of FlakeOut is a low false positive rate of 2.8x10-4—an order of magnitude lower than standard filtering techniques—which greatly reduces the number of true ground points that are incorrectly removed. This low false positive rate makes FlakeOut appropriate for applications requiring quantitative measurements of the snow surface in light to moderate blowing snow conditions. Additionally, we provide mathematical and software tools to efficiently estimate the false positive rate of filters applied for the purpose of removing erroneous data points that occur very infrequently in a dataset.

Numerical study on snow erosion and deposition around an embankment with a snow fence under snowfall conditions

Snow drift which usually occurs in conjunction with snowfall can significantly alter the distribution pattern of snow cover around roads creating travel hazards for vehicles. To study the snow distribution around a road and evaluate the snow prevention efficiency of snow fence, based on the theories of two-phase flow and snowdrift erosion and deposition, a numerical model is developed. The model includes snowfall during snow drift, spatial distribution characteristics of wind speed and snow phase volume fraction, and dynamic changes of snow drift shape on the ground during drifting. We simulate the distribution characteristics of snowdrift around an embankment without and with the protection of a snow fence and under the conditions of no snowfall and snowfall. The results indicate that snow deposition is greatest on the leeward side of the embankment in comparison to the windward side of the embankment and takes a longer time to reach equilibrium. On the leeward side of the embankment, the snow accumulation rate under the condition of snow falling is higher than that under the condition of no snow falling. Nonetheless, the two conditions both suggest that installation of the snow fence intercepts a large amount of snow behind the snow fence, decreases the snow phase volume fraction near the ground, and reduces the snowdrift accumulation on the leeward side of the embankment within a certain period.

THE STABILITY OF A PERMANENT TERRESTRIAL LASER SCANNING SYSTEM – A CASE STUDY WITH HOURLY SCANS

Abstract. The stability of the permanently installed terrestrial laser scanner (TLS) in a high mountain environment at Hintereisferner glacier, Ötztal Alps, Austria, is tested. From previous studies it is already known that the uncertainty of the permanent setup results from scanning geometry, atmospheric conditions and instrumental limitations. This study focuses on the instrumental limitations related to the lack of perfect stability of the TLS. A case study is performed with hourly scans over the glacier and the data of the internal inclination sensors are read. A comparison of the scanning data with the inclination data shows that the TLS at Hintereisferner is affected by both high-frequency vibrations and coarser movements. The high-frequency vibrations cause radial stripes in the data, and cannot be corrected, as the internal inclinations sensors of the TLS measure at a frequency of 1 Hz, whereas pulses are emitted at effectively 23 kHz. The coarser movements are indicated by the measurement of roll and pitch with the internal inclination sensors and can be corrected by manually georeferencing the data.In order to complete the uncertainty assessment of a permanent long-range TLS system in a high mountain environment, future work will concentrate on the impact from the scanning geometry and from the atmospheric variables. The finalised uncertainty assessment is crucial to derive the smallest magnitude at which snow (re)distribution can be detected and, thus, significantly will improve the treatment of snow cover dynamics in future glacier mass balance research.

Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

Exploring snow distribution dynamics in steep forested slopes with UAV-borne LiDAR

Mapping snow in forests is important for understanding the snow cover dynamics in these environments in view of hydrological applications and water resources management. Today, Uncrewed Aerial Vehicles (UAVs) are widely used for snow studies due to their rather cheap and flexible operation. UAV-borne Light detection and ranging (LiDAR) systems are a promising technology for sub-canopy snow mapping at high temporal resolutions, concurrently providing information on both the canopy and the below-canopy snow surface. In this pilot study, we used a UAV-LiDAR system to investigate the snow cover dynamics within two steep forested slopes of opposing aspects in the Swiss Alps at both high spatial resolution and unprecedented temporal resolution. Using a Distance to Canopy Edge (DCE) algorithm to characterize local forest structure, snow depth was analyzed in terms of relative position within variable forest cover. The north-exposed site had higher mean snow depths throughout the season compared to the south-exposed site, especially in canopy gaps. Whereas snow depletion rate was consistent throughout the north-exposed site, snow depletion was much faster in the gaps at the south-exposed slope. Correlation coefficients between snow depths and local canopy closure were weaker at the south-exposed (between −0.5 and − 0.7) than at the north-exposed site (between −0.7 and − 0.9), and rapidly deteriorated right after the peak of winter at the south-exposed slope. This indicates shortwave radiation dominates snowmelt processes at this site, which was thought to be spatially uncorrelated to local canopy cover, unlike accumulation and melt processes on the north-exposed slope that generated snow patterns with a high spatial correlation to local canopy cover throughout the entire season. Calculations of incoming sub-canopy shortwave radiation (SWR) for both sites confirmed this assumption. While our findings encourage the use of UAV-borne LiDAR for further investigations of snow cover dynamics in steep forested slopes, we also outline and discuss technical challenges specific to this application. Our insights allow deriving useful recommendations for future studies using UAV-borne LiDAR over a similar environment.