scholarly journals Statistical modelling of the snow depth distribution on the catchment scale

2013 ◽  
Vol 10 (3) ◽  
pp. 3237-3282 ◽  
Author(s):  
T. Grünewald ◽  
J. Stötter ◽  
J. W. Pomeroy ◽  
R. Dadic ◽  
I. Moreno Baños ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Statistical Modelling ◽  
Snow Accumulation ◽  
Catchment Scale ◽  
Local Conditions ◽  
Large Variability ◽  
Explanatory Variables ◽  
Unit Scale

Abstract. The spatial distribution of alpine snow covers is characterized by a large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale, that 30% to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could still explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, there seem to be some temporal transferability, in which models developed on one peak snow season were good predictors for other peak snow seasons.

scholarly journals Statistical modelling of the snow depth distribution in open alpine terrain

2013 ◽  
Vol 17 (8) ◽  
pp. 3005-3021 ◽  
Author(s):  
T. Grünewald ◽  
J. Stötter ◽  
J. W. Pomeroy ◽  
R. Dadic ◽  
I. Moreno Baños ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Statistical Modelling ◽  
Snow Accumulation ◽  
Catchment Scale ◽  
Local Conditions ◽  
Large Variability ◽  
Explanatory Variables ◽  
Unit Scale

Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

scholarly journals Multiscale spatial variability of lidar-derived and modeled snow depth on Hardangervidda, Norway

2013 ◽  
Vol 54 (62) ◽  
pp. 273-281 ◽  
Author(s):  
Kjetil Melvold ◽  
Thomas Skaugen
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Laser Scanning ◽  
Regional Scale ◽  
Local Scale ◽  
Snow Accumulation ◽  
Large Variability ◽  
Airborne Laser ◽  
Snow Distribution ◽  
Snow Model

AbstractThis study presents results from an Airborne Laser Scanning (ALS) mapping survey of snow depth on the mountain plateau Hardangervidda, Norway, in 2008 and 2009 at the approximate time of maximum snow accumulation during the winter. The spatial extent of the survey area is >240 km2. Large variability is found for snow depth at a local scale (2 m2), and similar spatial patterns in accumulation are found between 2008 and 2009. The local snow-depth measurements were aggregated by averaging to produce new datasets at 10, 50, 100, 250 and 500 m2 and 1 km2 resolution. The measured values at 1 km2 were compared with simulated snow depth from the seNorge snow model (www.senorge.no), which is run on a 1 km2 grid resolution. Results show that the spatial variability decreases as the scale increases. At a scale of about 500 m2 to 1 km2 the variability of snow depth is somewhat larger than that modeled by seNorge. This analysis shows that (1) the regional-scale spatial pattern of snow distribution is well captured by the seNorge model and (2) relatively large differences in snow depth between the measured and modeled values are present.

scholarly journals Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

2014 ◽  
Vol 8 (2) ◽  
pp. 1937-1972 ◽  
Author(s):  
J. Revuelto ◽  
J. I. López-Moreno ◽  
C. Azorin-Molina ◽  
S. M. Vicente-Serrano
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Laser Scanner ◽  
Climatic Conditions ◽  
Snow Accumulation ◽  
Small Catchment ◽  
Explanatory Variables ◽  
Spanish Pyrenees ◽  
Snow Distribution ◽  
Variance Explained

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions and binary regression trees were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that better explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra- and inter-annual time scales. The topographic position index, which has rarely been used in these types of studies, most accurately explained the distribution of snow accumulation. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation, and northing (or potential incoming solar radiation). The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the most explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a~year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snow pack was thinner and more patchily distributed. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, some similarities in snow accumulation patterns were observed.

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 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 Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

2020 ◽  
Vol 14 (9) ◽  
pp. 2925-2940 ◽  
Author(s):  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
Etienne Berthier ◽  
Jeffrey Deems ◽  
Ethan Gutmann ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Snow Depth ◽  
Laser Scanning ◽  
Unmanned Aircraft ◽  
Small Scale ◽  
High Mountain ◽  
Catchment Scale ◽  
Absolute Deviation ◽  
Covered Area ◽  
Tuolumne River

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

scholarly journals Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

2020 ◽  
Author(s):  
Ahmad Hojatimalekshah ◽  
Zach Uhlmann ◽  
Nancy F. Glenn ◽  
Christopher A. Hiemstra ◽  
Christopher J. Tennant ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Local Scale ◽  
Snow Accumulation ◽  
Tree Structure ◽  
Scale Model ◽  
Fine Scale ◽  
Individual Tree ◽  
The Impact

Abstract. Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts, and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA, to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We apply the Marker-controlled watershed algorithm for individual tree segmentation and measure the snow depth using the Multi-scale Model to Model Cloud Comparison algorithm. Canopy, topography and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity) along with local scale processes such as wind are highly influential on snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results emphasize the importance of tree species and distribution on snow depth patterns. Fine scale analysis from TLS provides information on local scale controls, and provides an opportunity to be readily coupled with airborne or spaceborne lidar to investigate larger-scale controls on snow depth.

scholarly journals LiDAR snow cover studies on glacier surface: significance of snow- and ice dynamical processes

2013 ◽  
Vol 7 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
K. Helfricht ◽  
M. Kuhn ◽  
M. Keuschnig ◽  
A. Heilig
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Snow Cover ◽  
Snow Depth ◽  
Laser Scanning ◽  
Snow Accumulation ◽  
Surface Elevation ◽  
High Mountain ◽  
Dynamical Processes ◽  
Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source for runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally only a small number of precipitation measurements deliver precipitation input for modeling in remote mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of Light Detecting And Ranging (LiDAR) techniques from aircraft, so-called airborne laser scanning (ALS), enables to derive surface elevations changes even in inaccessible terrain. Within one snow accumulation season these surface elevation changes can be interpreted as snow depths as a first assumption for snow hydrological studies. However, dynamical processes in snow, firn and ice are contributing to surface elevation changes on glaciers. To evaluate the magnitude and significance of these processes on alpine glaciers in the present state, ALS derived surface elevation changes were compared to converted snow depths from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers in the high alpine region of Ötztal Alps. LANDSAT data were used to distinguish between firn and ice areas of the glaciers. In firn areas submerging ice flow and densification of firn and snow are contributing to a mean relative deviation of ALS surface elevation changes from actually observed snow depths of −20.0% with a mean standard deviation of 17.1%. Deviations between ALS surface elevation changes and GPR snow depth are small along the profiles on the glacier tongues. At these areas mean absolute deviation of ALS surface elevation changes and GPR snow depth is 0.004 m with a mean standard deviation of 0.27 m. Emergence flow leads to distinct positive deviations only at the very front of the glacier tongues. Snow depths derived from ALS deviate less from actually measured snow depths than expected errors of in-situ measurements of solid precipitation. Hence, ALS derived snow depths are an important data source for both, spatial distribution and total sum of the snow cover volume stored on the investigated glaciers and in the corresponding high mountain catchments at the end of an accumulation season.

scholarly journals Assessing the application of a laser rangefinder for determining snow depth in inaccessible alpine terrain

2010 ◽  
Vol 7 (1) ◽  
pp. 417-440 ◽  
Author(s):  
J. L. Hood ◽  
M. Hayashi
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Late Summer ◽  
Cost Effective ◽  
Snow Accumulation ◽  
Laser Rangefinder ◽  
Generate Surface ◽  
Distance Data ◽  
Talus Slopes ◽  
Alpine Watersheds

Abstract. Snow is a major contributor to stream flow in alpine watersheds and quantifying snow depth and distribution is important for hydrological research. However, direct measurement of snow in rugged alpine terrain is often impossible due to avalanche and rock fall hazard. A laser rangefinder was used to determine the depth of snow in inaccessible areas. Laser rangefinders use ground based light detection and ranging technology but are more cost effective than airborne surveys or terrestrial laser scanning systems and are highly portable. Data was collected within the Opabin watershed in the Canadian Rockies. Surveys were conducted on one accessible slope for validation purposes and two inaccessible talus slopes. Laser distance data was used to generate surface models of slopes when snow covered and snow-free and snow depth distribution was quantified by differencing the two surfaces. The results were compared with manually probed snow depths on the accessible slope. The accuracy of the laser rangefinder method as compared to probed depths was 0.21 m or 12% of average snow depth. Results from the two inaccessible talus slopes showed regions near the top of the slopes with 6–9 m of snow accumulation. These deep snow accumulation zones result from re-distribution of snow by avalanches and are hydrologically significant as they persist until late summer.

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