scholarly journals The smoothing of landscapes during snowfall with no wind

2019 ◽  
Vol 65 (250) ◽  
pp. 173-187 ◽  
Author(s):  
SIMON FILHOL ◽  
MATTHEW STURM
Keyword(s):  
Snow Depth ◽  
Cumulative Effect ◽  
Snow Accumulation ◽  
Narrow Gap ◽  
Lateral Transport ◽  
Directional Bias ◽  
Strobe Light ◽  
Artificial Surfaces ◽  
Topographic Relief ◽  
Snow Deposition

ABSTRACTEvery winter, snowy landscapes are smoothed by snow deposition in calm conditions (no wind). In this study, we investigated how vertically falling snow attenuates topographic relief at horizontal scales less than or approximately equal to snow depth (e.g., 0.1–10 m). In a set of three experiments under natural snowfall, we observed the particle-scale mechanisms by which smoothing is achieved, and we examined the cumulative effect at the snowpack scale. The experiments consisted of (a) a strobe-light box for tracking the trajectories of snowflakes at deposition, (b) allowing snow to fall through a narrow gap (40 mm) and examining snow accumulation above and below the gap, and (c) allowing snow to accumulate over a set of artificial surfaces. At the particle scale, we observed mechanisms enhancing (bouncing, rolling, ejection, breakage, creep, metamorphism) and retarding (interlocking, cohesion, adhesion, sintering) the rate of smoothing. The cumulative effect of these mechanisms is found to be driven by snowpack surface curvature, introducing a directional bias in the lateral transport of snow particles. Our findings suggest that better quantification of the mechanisms behind smoothing by snow could provide insights into the evolution of snow depth variability, and snow-vegetation interactions.

scholarly journals Topographical effects of Snow Deposition on Restructured Land

1980 ◽  
Vol 26 (94) ◽  
pp. 518-518
Author(s):  
E. Chaco ◽  
M. Molnau
Keyword(s):  
Snow Depth ◽  
Winter Season ◽  
Surface Mining ◽  
Snow Accumulation ◽  
Grid Data ◽  
Grid Pattern ◽  
Contour Maps ◽  
Snow Properties ◽  
Waste Dumps ◽  
Snow Deposition

AbstractThe measurement of snow accumulation and distribution is one of the primary objectives of a study on the melt of snow-drifts and erosion in the phosphate mining region of south-eastern Idaho. The study area is located in an active phosphate mine and is limited to the sites of waste dumps, a product of the surface mining technique used in this area. Four sites are included in the overall study with one dump selected for intensive snow measurement. Snow deposition data have been collected for one winter season (November 1977—June 1978) on a grid pattern over this dump. The area of the study site has been expanded and similar measurements are planned for the coming snow season.The snow measurements were made monthly on a pre-established 23 m (75 ft) square grid overlaid on the dump. The analysis of the snow data consists of contour mapping of any one or all the snow properties measured—snow depth, density, or water equivalent. In addition, since the measurements are made on the same grid each month, mathematical manipulation of grid data allows contour maps of the residual of the monthly snow properties to be plotted. A similar analysis of terrain properties collected on the same grid results in contour maps displaying ground slope, concavity-convexity of the surface, aspect, or distance from snow- deposition obstacles.The aim of the analysis using these types of data is to arrive at a model which will compute patterns of snow accumulation and distribution on the ground surface given a description of terrain type and probable meteorological properties of the region. A preliminary comparison of the maps shows a similar pattern of snow deposition occurring each month with the exposed areas of the dump swept clean and the greatest snow depth occurring in the sheltered concavities.

scholarly journals Inferring Watershed-scale Mean Snow Magnitude and Distribution Using Multidecadal Snow Reanalysis Patterns and Snow Pillow Observations

2021 ◽  
Author(s):  
Justin Pflug ◽  
Steven Margulis ◽  
Jessica Lundquist
Keyword(s):  
Spatial Heterogeneity ◽  
Sierra Nevada ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Airborne Lidar ◽  
Watershed Scale ◽  
Mountainous Regions ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Deposition

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

scholarly journals Topographical effects of Snow Deposition on Restructured Land

1980 ◽  
Vol 26 (94) ◽  
pp. 518
Author(s):  
E. Chaco ◽  
M. Molnau
Keyword(s):  
Snow Depth ◽  
Winter Season ◽  
Surface Mining ◽  
Snow Accumulation ◽  
Grid Data ◽  
Grid Pattern ◽  
Contour Maps ◽  
Snow Properties ◽  
Waste Dumps ◽  
Snow Deposition

Abstract The measurement of snow accumulation and distribution is one of the primary objectives of a study on the melt of snow-drifts and erosion in the phosphate mining region of south-eastern Idaho. The study area is located in an active phosphate mine and is limited to the sites of waste dumps, a product of the surface mining technique used in this area. Four sites are included in the overall study with one dump selected for intensive snow measurement. Snow deposition data have been collected for one winter season (November 1977—June 1978) on a grid pattern over this dump. The area of the study site has been expanded and similar measurements are planned for the coming snow season. The snow measurements were made monthly on a pre-established 23 m (75 ft) square grid overlaid on the dump. The analysis of the snow data consists of contour mapping of any one or all the snow properties measured—snow depth, density, or water equivalent. In addition, since the measurements are made on the same grid each month, mathematical manipulation of grid data allows contour maps of the residual of the monthly snow properties to be plotted. A similar analysis of terrain properties collected on the same grid results in contour maps displaying ground slope, concavity-convexity of the surface, aspect, or distance from snow- deposition obstacles. The aim of the analysis using these types of data is to arrive at a model which will compute patterns of snow accumulation and distribution on the ground surface given a description of terrain type and probable meteorological properties of the region. A preliminary comparison of the maps shows a similar pattern of snow deposition occurring each month with the exposed areas of the dump swept clean and the greatest snow depth occurring in the sheltered concavities.

scholarly journals Seasonal influence of snow conditions on Dall’s sheep productivity in Wrangell-St Elias National Park and Preserve

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244787
Author(s):  
Christopher L. Cosgrove ◽  
Jeff Wells ◽  
Anne W. Nolin ◽  
Judy Putera ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Snow Accumulation ◽  
Population Declines ◽  
Climate Conditions ◽  
Remote Cameras ◽  
Snow Conditions ◽  
Dall’S Sheep

Dall’s sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall’s sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall’s sheep recruitment dynamics.

scholarly journals A snow-transport model for complex terrain

1998 ◽  
Vol 44 (148) ◽  
pp. 498-516 ◽  
Author(s):  
Glen E. Liston ◽  
Matthew Sturm
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Snow Water Equivalent ◽  
Transport Model ◽  
Vegetation Type ◽  
Cold Season ◽  
Snow Accumulation ◽  
Surface Shear Stress ◽  
Surface Shear ◽  
Snow Transport

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2017 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Mariken Homleid
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Water Resource Management ◽  
Snow Accumulation ◽  
Surface Model ◽  
Snow Melt ◽  
Grid Spacing ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, thirty percent of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of approximately 1 km over an area in southern Norway for two years (01 September 2014–31 August 2016), using two different forcing data sets: 1) hourly meteorological forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing), and 2) gridded hourly observations of temperature and precipitation (1 km grid spacing) in combination with the meteorological forecasts from AROME MetCoOp. We present an evaluation of the modeled snow depth and snow cover, as compared to point observations of snow depth and to MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snow melt. The results are promising. Both experiments are capable of simulating the snow pack over the two winter seasons, but there is an overestimation of snow depth when using only meteorological forecasts from AROME MetCoOp, although the snow-covered area throughout the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season, showing that assimilation of snow depth observations into SURFEX/Crocus might be necessary when using only meteorological forecasts as forcing. When using gridded observations, the simulation of snow depth is significantly improved, which shows that using a combination of gridded observations and meteorological forecasts to force a snowpack model is very useful and can give better results than only using meteorological forecasts. There is however an underestimation of snow ablation in both experiments. This is mainly due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

scholarly journals Understanding of European Cold Extremes, Sudden Stratospheric Warming, and Siberian Snow Accumulation in the Winter of 2017/18

2020 ◽  
Vol 33 (2) ◽  
pp. 527-545 ◽  
Author(s):  
Zhuozhuo Lü ◽  
Fei Li ◽  
Yvan J. Orsolini ◽  
Yongqi Gao ◽  
Shengping He
Keyword(s):  
Snow Depth ◽  
Time Lag ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Planetary Waves ◽  
Stratospheric Warming ◽  
Mean Flow ◽  
Sudden Stratospheric Warming ◽  
Cold Extremes

AbstractIt is unclear whether the Eurasian snow plays a role in the tropospheric driving of sudden stratospheric warming (SSW). The major SSW event of February 2018 is analyzed using reanalysis datasets. Characterized by predominant planetary waves of zonal wave 2, the SSW developed into a vortex split via wave–mean flow interaction. In the following two weeks, the downward migration of zonal-mean zonal wind anomalies was accompanied by a significant transition to the negative phase of the North Atlantic Oscillation, leading to extensive cold extremes across Europe. Here, we demonstrate that anomalous Siberian snow accumulation could have played an important role in the 2018 SSW occurrence. In the 2017/18 winter, snow depths over Siberia were much higher than normal. A lead–lag correlation analysis shows that the positive fluctuating snow depth anomalies, leading to intensified “cold domes” over eastern Siberia (i.e., in a region where the climatological upward planetary waves maximize), precede enhanced wave-2 pulses of meridional heat fluxes (100 hPa) by 7–8 days. The snow–SSW linkage over 2003–19 is further investigated, and some common traits among three split events are found. These include a time lag of about one week between the maximum anomalies of snow depth and wave-2 pulses (100 hPa), high sea level pressure favored by anomalous snowpack, and a ridge anchoring over Siberia as precursor of the splits. The role of tropospheric ridges over Alaska and the Urals in the wave-2 enhancement and the role of Arctic sea ice loss in Siberian snow accumulation are also discussed.

scholarly journals Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

2016 ◽  
Vol 10 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Z. Zheng ◽  
P. B. Kirchner ◽  
R. C. Bales
Keyword(s):  
Sierra Nevada ◽  
Snow Depth ◽  
Point Cloud ◽  
Accumulation Point ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Conifer Forest ◽  
Vegetation Effects ◽  
Cloud Data ◽  
Depth Measurement

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 &amp;pm; 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

scholarly journals Zoning of snowiness and avalanching in the mountains of western Transcaucasia

2001 ◽  
Vol 32 ◽  
pp. 311-313
Author(s):  
E. Troshkina ◽  
T. Glazovskaya ◽  
N. Kondakova ◽  
V. Sokolov
Keyword(s):  
Interannual Variability ◽  
Sea Level ◽  
Snow Depth ◽  
Quantitative Characteristic ◽  
Snow Accumulation ◽  
Subtropical Zone ◽  
Mountain Ranges ◽  
Middle Mountains ◽  
Heavy Snow ◽  
Avalanche Risk

AbstractIn the mountains of the moist-subtropical zone of Western Transcaucasia seven zones of snowiness are distinguished, depending on the heights from sea level to the tops of mountain ranges. Data are taken from 17 years of snow-avalanche observations. In a legend of a map the quantitative characteristic of the avalanching conditions for each zone is given. Interannual variability of snow depth is up to 100 cm in low mountains and about 600 cm in the middle belt of mountains The inversion of snow accumulation is registered. The frequency cycles of heavy-snow (3–4 years) and of especially heavy-snow (10–12 years) winters are determined. The avalanche danger is provoked not by the total depth of the snow, but by the stormy snowfalls. The particular conditions of creation and development of nival processes are distinguished here: dry avalanches on the crests of mountains, wet avalanches in the middle mountains and slushflows in the low mountains. Despite low avalanche activity, avalanche risk is high due to the factor of unexpectedness.

Sign in / Sign up

Export Citation Format

Share Document