Ice-Wedge Cracks, Garry Island, Northwest Territories

1974 ◽  
Vol 11 (10) ◽  
pp. 1366-1383 ◽  
Author(s):  
J. Ross Mackay
Keyword(s):  
Northwest Territories ◽  
Snow Depth ◽  
Ground Surface ◽  
Ice Wedge

Observations made on winter ice-wedge cracks at Garry Island, N.W.T., for the 1967–73 period show that cracking tends to occur between mid-January and mid-March. On the average, nearly 40% of the ice wedges crack in any given year. The crack frequency varies inversely with snow depth. Medium sized ice wedges, about 1 m wide, crack more often than smaller or larger wedges, ice wedges crack preferentially near the center and often year after year at nearly the same place. The cracks average about 1 cm wide at the surface and taper downwards to depths which may exceed 5 m. The cracks partially close in spring before a new ice veinlet forms in them. Evidence provided by multiple wedges suggests that cracking may be initiated at times within the wedge rather than at the ground surface, and thus the cracks propagate both upwards and downwards.

scholarly journals Lidar measurement of snow depth: a review

2013 ◽  
Vol 59 (215) ◽  
pp. 467-479 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Thomas H. Painter ◽  
David C. Finnegan
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Near Infrared ◽  
Ground Surface ◽  
Lidar Measurement ◽  
Snow Hydrology ◽  
Sensing Technology ◽  
Transfer Error ◽  
Scale Range ◽  
Visible Wavelengths

AbstractLaser altimetry (lidar) is a remote-sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. Recently lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of both airborne and ground-based sensors. Modern sensors allow mapping of vegetation heights and snow or ground surface elevations below forest canopies. Typical vertical accuracies for airborne datasets are decimeter-scale with order 1 m point spacings. Ground-based systems typically provide millimeter-scale range accuracy and sub-meter point spacing over 1 m to several kilometers. Many system parameters, such as scan angle, pulse rate and shot geometry relative to terrain gradients, require specification to achieve specific point coverage densities in forested and/or complex terrain. Additionally, snow has a significant volumetric scattering component, requiring different considerations for error estimation than for other Earth surface materials. We use published estimates of light penetration depth by wavelength to estimate radiative transfer error contributions. This paper presents a review of lidar mapping procedures and error sources, potential errors unique to snow surface remote sensing in the near-infrared and visible wavelengths, and recommendations for projects using lidar for snow-depth mapping.

scholarly journals Fractional snow-covered area: scale-independent peak of winter parameterization

2021 ◽  
Vol 15 (2) ◽  
pp. 615-632
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Empirical Relationship ◽  
Ground Surface ◽  
Model Parameters ◽  
Data Sets ◽  
Snow Covered Area ◽  
Covered Area ◽  
Geographical Regions

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

Towards a scale-independent fractional snow-covered area parameterization for complex terrain

2020 ◽  
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Ground Surface ◽  
Longwave Radiation ◽  
Data Sets ◽  
Depth Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wide Range

<p>Whenever there is snow on the ground, there will be large spatial variability in snow depth. The spatial distribution of snow is significantly influenced by topography due to wind, precipitation, shortwave and longwave radiation, and even snow avalanches relocate the accumulated snow. Fractional snow-covered area (fSCA) is an important model parameter characterizing the fraction of the ground surface that is covered by snow and is crucial for various model applications such as weather forecasts, climate simulations and hydrological modeling.</p><p>We recently suggested an empirical fSCA parameterization based on two spatial snow depth data sets acquired at peak of winter in Switzerland and Spain, which yielded best performance for spatial scales larger than 1000 m. However, this parameterization was not validated on independent snow depth data. To evaluate and improve our fSCA parameterization, in particular with regards to other spatial scales and snow climates (or geographic regions), we used spatial snow depth data sets form a wide range of mountain ranges in USA, Switzerland and France acquired by 5 different measuring methods. Pooling all snow depth data sets suggests that a scale-dependent parameter should be introduced to improve the fSCA parameterization, in particular for sub-kilometer spatial scales. Extending our empirical fSCA parameterization to a broader range of scales and snow climates is an important step towards accounting for spatio-temporal variability in snow depth in multiple snow model applications.</p>

scholarly journals Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

2016 ◽  
Author(s):  
Haruko M. Wainwright ◽  
Anna K. Liljedahl ◽  
Baptiste Dafflon ◽  
Craig Ulrich ◽  
John E. Peterson ◽  
...  
Keyword(s):  
High Precision ◽  
Snow Depth ◽  
Spatial Sampling ◽  
Multi Scale ◽  
Arctic Alaska ◽  
Winter Snow ◽  
Digital Elevation ◽  
Tundra Ecosystem ◽  
Ice Wedge ◽  
Ground Penetrating

Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes, and estimated using ground penetrating radar (GPR) surveys and the Photogrammetric Detection and Ranging (PhoDAR) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. UAS-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm by 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free LiDAR digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free LiDAR DEM and multi-scale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high precision estimates of snow depth (RMSE = 6.0 cm), at 0.5-meter resolution and over the LiDAR domain (750 m by 700 m).

scholarly journals Response of seasonal soil freeze depth to climate change across China

2017 ◽  
Vol 11 (3) ◽  
pp. 1059-1073 ◽  
Author(s):  
Xiaoqing Peng ◽  
Tingjun Zhang ◽  
Oliver W. Frauenfeld ◽  
Kang Wang ◽  
Bin Cao ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Snow Depth ◽  
Vegetation Index ◽  
Regional Scale ◽  
Ground Surface ◽  
Driving Factors ◽  
Hydrological Process ◽  
Vegetation Growth ◽  
Air Temperatures

Abstract. The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of −0.18 ± 0.03 cm yr−1, resulting in a net decrease of 8.05 ± 1.5 cm over 1967–2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr−1 in most parts of China during 1950–2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze–thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

scholarly journals Seasonal and Interannual Ground-Surface Displacement in Intact and Disturbed Tundra along the Dalton Highway on the North Slope, Alaska

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 22
Author(s):  
Go Iwahana ◽  
Robert C. Busey ◽  
Kazuyuki Saito
Keyword(s):  
Ground Surface ◽  
Surface Displacement ◽  
Satellite System ◽  
The North ◽  
Surface Displacements ◽  
Standard Deviation Range ◽  
Thaw Depth ◽  
Ice Wedge ◽  
Global Navigation Satellite ◽  
Northern Alaska

Spatiotemporal variation in ground-surface displacement caused by ground freeze–thaw and thermokarst is critical information to understand changes in the permafrost ecosystem. Measurement of ground displacement, especially in the disturbed ground underlain by ice-rich permafrost, is important to estimate the rate of permafrost and carbon loss. We conducted high-precision global navigation satellite system (GNSS) positioning surveys to measure the surface displacements of tundra in northern Alaska, together with maximum thaw depth (TD) and surface moisture measurements from 2017 to 2019. The measurements were performed along two to three 60–200 m transects per site with 1–5 m intervals at the three areas. The average seasonal thaw settlement (STS) at intact tundra sites ranged 5.8–14.3 cm with a standard deviation range of 2.1–3.3 cm. At the disturbed locations, averages and variations in STS and the maximum thaw depth were largest in all observed years and among all sites. The largest seasonal and interannual subsidence (44 and 56 cm/year, respectively) were recorded at points near troughs of degraded ice-wedge polygons or thermokarst lakes. Weak or moderate correlation between STS and TD found at the intact sites became obscure as the thermokarst disturbance progressed, leading to higher uncertainty in the prediction of TD from STS.

The first 20 years (1978-1979 to 1998–1999) of ice-wedge growth at the Illisarvik experimental drained lake site, western Arctic coast, Canada

2002 ◽  
Vol 39 (1) ◽  
pp. 95-111 ◽  
Author(s):  
J Ross Mackay ◽  
C R Burn
Keyword(s):  
Active Layer ◽  
Bottom Sediments ◽  
Ground Surface ◽  
Thermal Contraction ◽  
Linear Coefficient ◽  
Vegetation Growth ◽  
Ground Temperatures ◽  
Lake Bottom ◽  
Ice Wedge ◽  
Lake Bottom Sediments

In August 1978, a large tundra lake was drained to study the aggradation of permafrost into newly exposed lake-bottom sediments. Ice-wedge growth, which started in the first winter following drainage, had ceased in most of the lake bottom within about twelve years. The gradual cessation of thermal contraction cracking can be attributed to rapid vegetation growth, snow entrapment, an increase in winter ground temperatures, and a decrease in the linear coefficient of thermal contraction associated with freeze–thaw consolidation of the initially saturated lake-bottom sediments. The tilt and separation of markers in the active layer revealed gradual convergence towards the troughs even after ice-wedge growth had ceased. For the first few years the ice-wedge growth rate was up to 3 cm/a as determined by excavation, drilling, separation of the bottoms of benchmarks installed into permafrost, and divergence of free-floating inductance coils placed on the sides of ice wedges well below the bottom of the active layer. The vertical extent of most ice wedges was probably about 2 m, as deduced from the depths of ice-wedge cracks and the geometries of the wedge tops. Many thermal contraction cracks propagated upward to the ground surface from the tops of the ice wedges rather than downward from the ground surface. Small, upward facing, horizontal steps and vertical slickensided surfaces in permafrost on both sides of an excavated ice wedge near its top indicated that the adjacent permafrost had moved upward, relative to the wedge, from thermal expansion during the warming period.

scholarly journals Large area land surface simulations in heterogeneous terrain driven by global datasets: application to mountain permafrost

2013 ◽  
Vol 7 (6) ◽  
pp. 5853-5887 ◽  
Author(s):  
J. Fiddes ◽  
S. Endrizzi ◽  
S. Gruber
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Ground Surface ◽  
Swiss Alps ◽  
Large Area ◽  
Heterogeneous Terrain ◽  
Ground Surface Temperature ◽  
Mountain Permafrost

Abstract. Numerical simulations of land-surface processes are important in order to perform landscape-scale assessments of earth-systems. This task is problematic in complex terrain due to: (i) high resolution grids required to capture strong lateral variability, (ii) lack of meteorological forcing data where it is required. In this study we test a topography and climate processor, which is designed for use with large area land surface simulation, in complex and remote terrain. The scheme is driven entirely by globally available datasets. We simulate air temperature, ground surface temperature, snow depth and test the model with a large network of measurements in the Swiss Alps. We obtain RMSE values of 0.64 °C for air temperature, 0.67–1.34 °C for non-bedrock ground surface temperature, and 44.5 mm for snow depth, which is likely affected by poor input precipitation field. Due to this we trial a simple winter precipitation correction method based on melt-dates of the snow-pack. We present a test application of the scheme in the context of simulating mountain permafrost. The scheme produces a permafrost estimate of 2000 km2 which compares well to published estimates. We suggest that this scheme represents a good first effort in application of numerical models over large areas in heterogeneous terrain.

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