scholarly journals Scaling properties of wind and snow depth distribution in an Alpine catchment

2011 ◽  
Vol 116 (D6) ◽  
Author(s):  
R. Mott ◽  
M. Schirmer ◽  
M. Lehning
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Scaling Properties

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

2020 ◽  
Vol 34 (26) ◽  
pp. 5384-5401
Author(s):  
Jesús Revuelto ◽  
Paul Billecocq ◽  
François Tuzet ◽  
Bertrand Cluzet ◽  
Maxim Lamare ◽  
...  
Keyword(s):  
Random Forests ◽  
Snow Depth ◽  
Depth Distribution ◽  
Mountain Areas

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.

A comparison between coincident laser and Ku radar versus S- to C-band 'snow radar' data for airborne retrievals of snow depth on sea ice

2021 ◽  
Author(s):  
Claude de Rijke-Thomas ◽  
Jack Landy ◽  
Joshua King ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Depth Distribution ◽  
Laser Scanner ◽  
Radar Data ◽  
Scanner Data ◽  
Sea Ice Thickness ◽  
Band Frequency ◽  
Radar Waveforms ◽  
Calibration Step

<p>Snow depth estimates remain a large uncertainty for constraining the accuracy of sea ice thickness retrievals from polar altimetry. There have been several recent investigations into methods for estimating snow depth from airborne observations over sea ice; this poster outlines a comparison between two different methods applied to Operation IceBridge data from the Spring 2016 campaign. The first co-locates visible-band laser scanner data from the Airborne Topographic Mapper with Ku-band data from the CReSIS radar, using a fixed threshold first-maximum retracker algorithm for retracking radar waveforms and applying a calibration step to remove the vertical offset between sensors at leads. This method represents an airborne proxy for the newly-aligned ICESat-2 and CryoSat-2 orbits of the Cryo2Ice campaign. The second method uses the conventional CReSIS ultrawide-band frequency‐modulated continuous‐wave ‘snow radar’ system, that ranges between S- and C-band, applying the retracker algorithm described by Newman et al 2014. We evaluate properties of the estimated snow depth distribution, and alignment of air-snow and snow-ice interfaces, along the aircraft track and the scale of correlation between sensors.</p>

scholarly journals Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

2013 ◽  
Vol 7 (5) ◽  
pp. 4633-4680 ◽  
Author(s):  
J. Veitinger ◽  
B. Sovilla ◽  
R. S. Purves
Keyword(s):  
Surface Roughness ◽  
Snow Depth ◽  
Depth Distribution ◽  
Management Strategies ◽  
Snow Accumulation ◽  
Swiss Alps ◽  
Transfer Energy ◽  
Hazard Management ◽  
Terrestrial Lidar ◽  
Basin Scale

Abstract. In alpine terrain, the snow covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation, avalanche dynamics and affects surface heat transfer, energy balance as well as snow depth distribution. To characterize the effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and its corresponding underlying terrain for three alpine basins in the Swiss Alps characterized by low medium and high terrain roughness. To this end, elevation models of winter and summer terrain are derived from high-resolution (1 m) measurements performed by airborne and terrestrial LIDAR. We showed that on basin scale terrain smoothing not only depends on mean snow depth in the basin but also on its variability. Terrain smoothing can be modelled in function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth does not exist on a pixel scale. Further we demonstrated the high persistence of snow surface roughness even in between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can potentially improve avalanche release area definition and in the long term natural hazard management strategies.

scholarly journals On the Use of Telephone Poles for the Observation of Areal Snow-Depth Distribution

1973 ◽  
Vol 12 (66) ◽  
pp. 514-517
Author(s):  
Katsuhiro Kikuchi
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Wide Area ◽  
Zoom Lens ◽  
Lower Edge ◽  
Snow Surface ◽  
Good Coincidence

For the observation of areal snow-depth distribution over a wide area, such as the Ishikari Plain in Hokkaido, Japan, existing telephone poles were used in place of the snow scales. Telephone pole number plates (telephone poles in Japan have number plates) were photographed by a 35 mm camera with zoom lens ranging from 80 mm to 200 mm through the window of the observation vehicle. The snow depth was calculated by the ratios of the length of the number plates to the distance between the lower edge of the number plates and the snow surface. Since measurements by direct sounding using a rod as compared with the values by the above method showed a good coincidence with an accuracy of ± 10 cm, it was considered satisfactory for snow depth observation over a wide area.

scholarly journals Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Douglas M. Hultstrand ◽  
Steven R. Fassnacht ◽  
John D. Stednick ◽  
Christopher A. Hiemstra
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Generalized Additive Models ◽  
Winter Season ◽  
Regression Tree ◽  
Distribution Patterns ◽  
Time Frame ◽  
Additive Models ◽  
Depth Measurement ◽  
Accumulation Index

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

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