Remote Sensing, Model-Derived and Ground Measurements of Snow Water Equivalent and Snow Density in Alaska

Muskett, Reginald R.

doi:10.4236/ijg.2012.35114

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…The influence of snow thermal conductivity on soil temperature has been demonstrated by many model studies (e.g. Bartlett et al, 2006;Saha et al, 2006;Vavrus, 2007;Nicolsky et al, 2007;Dankers et al, 2011;Gouttevin et al, 2012). Winter soil temperature can change by up to 20 K simply by varying the snow thermal conductivity by 0.1-0.5 W m −1 K −1 (Cook et al, 2008).…”

Section: Introductionmentioning

confidence: 95%

Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region

et al. 2016

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Abstract. A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

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Section: Introductionmentioning

confidence: 95%

Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region

et al. 2016

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“…Phase and LOS change gradients have been derived before using ERS Tandem Mission SAR data for InSAR processing of digital elevation models [8]. The LOS gradients are an expression of tropopause pressure changes [9]. Troposphere pressure field, atmospheric moisture and wind fields and their seasonal non-stationary variations are an expression of the Beaufort Gyre [16].…”

Section: Troposphere Phase Barrow Alaskamentioning

confidence: 99%

“…The site is situated between emergent ice-wedge networks. The thermistors are deployed at depths of the root zone, mineral soil and permafrost-active layer interface [9] [14]. Snow thickness, relative humidity and air temperature sensors are mounted on a tri-pod and secured to the tundra by anchors give us measurements of the surface environment and changes during the project.…”

Section: L-band Insar Penetration Depth Experiments On Arctic Tundra mentioning

confidence: 99%

“…Our snow depth survey measurements on the frozen lakes have been well used in other research of snow thickness, density and snow water equivalent that are important environmental factors in the stability and vulnerability of the active layer and permafrost [9]. In research published in 2015 we compared ICESat GLAS elevation changes on locations of the North Slope to demonstrate by comparison and first principles that Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) line-of-sight changes on tundra landscapes in winter are composed more from volume scatter (snow, vegetation and sub-surface soil and the multi-path from each layer) than surface scatter ( Figure 1) [10].…”

Section: Introductionmentioning

confidence: 99%

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L-Band InSAR Penetration Depth Experiment, North Slope Alaska

Muskett¹

2017

GEP

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Since the first spacecraft-based synthetic aperture radar (SAR) mission NASA's SEASAT in 1978 radars have been flown in Low Earth Orbit (LEO) by other national space agencies including the Canadian Space Agency, European Space Agency, India Space Research Organization and the Japanese Aerospace Exploration Agency. Improvements in electronics, miniaturization and production have allowed for the deployment of SAR systems on aircraft for usage in agriculture, hazards assessment, land-use management and planning, meteorology, oceanography and surveillance. LEO SAR systems still provide a range of needful and timely information on large and small-scale weather conditions like those found across the Arctic where ground-base weather radars currently provide limited coverage. For investigators of solidearth deformation attention must be given to the atmosphere on Interferometric SAR (InSAR) by aircraft and spacecraft multi-pass operations. Because radar has the capability to penetrate earth materials at frequencies from the Pto X-band attention must be given to the frequency dependent penetration depth and volume scattering. This is the focus of our new research project: to test the penetration depth of L-band SAR/InSAR by aircraft and spacecraft systems at a test site in Arctic Alaska using multi-frequency analysis and progressive burial of radar mesh-reflectors at measured depths below tundra while monitoring environmental conditions. Knowledge of the L-band penetration depth on lowland Arctic tundra is necessary to constrain analysis of carbon mass balance and hazardous conditions arising from permafrost degradation and thaw, surface heave and subsidence and thermokarst formation at local and regional scales.

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“…To account for this influx during the month of April, a modified Snow Water A4 Equivalent (SWE) calculation was used. The standard SWE is dependent on the density of the snow and the snow depth and is calculated as (Sturm et al, 2010 The snow type during the spring melt in April is estimated to be very wet due to high rates of melt with a density of 700kg/m 3 (Muskett, 2012). Pomeroy and Jones (1996) 36Where U is the mean monthly wind speed (in m/s), Tmax is the mean monthly air temperature (in o C),…”

Section: Snow Water Equivalent (Swe) and Sublimation Correctionmentioning

confidence: 99%

Reactive Transport Modeling of a Historic Waste Rock Pile at the Detour Lake Gold Mine

Dagenais¹

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Remote Sensing, Model-Derived and Ground Measurements of Snow Water Equivalent and Snow Density in Alaska

Cited by 9 publications

References 22 publications

Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region

Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region

L-Band InSAR Penetration Depth Experiment, North Slope Alaska

Reactive Transport Modeling of a Historic Waste Rock Pile at the Detour Lake Gold Mine

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