Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects

Techel, Frank; Pielmeier, Christine

doi:10.5194/tc-5-405-2011

Cited by 115 publications

(127 citation statements)

References 31 publications

(39 reference statements)

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“…For the resulting snow depth this relative change of up to À13:1% could make a significant difference but cannot be determined exactly. Furthermore, the water content varies not only with depth in the snowpack but also spatially (Techel and Pielmeier, 2011). This makes it difficult to incorporate this factor in the analysis.…”

Section: Uncertainties In Gpr-derived Snow Depthmentioning

confidence: 99%

Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

Sold¹,

Huss²,

Hoelzle³

et al. 2013

J. Glaciol.

148

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ABSTRACT. Snow accumulation is an important component of the mass balance of alpine glaciers. To improve our understanding of the processes related to accumulation and their representation in state-ofthe-art mass-balance models, extensive field measurements are required. We present measurements of snow accumulation distribution on Findelengletscher, Switzerland, for April 2010 using (1) in situ snow probings, (2) airborne ground-penetrating radar (GPR) and (3) differencing of two airborne light detection and ranging (lidar) digital elevation models (DEMs). Calculating high-resolution snow depth from DEM-differencing requires careful correction for vertical ice-flow velocity and densification in the accumulation area. All three methods reveal a general increase in snow depth with elevation, but also a significant small-scale spatial variability. Lidar-differencing and in situ snow probings show good agreement for the mean specific winter balance (0.72 and 0.78 m w.e., respectively). The lidar-derived distributed snow depth reveals significant zonal correlations with elevation, slope and curvature in a multiple linear regression model. Unlike lidar-differencing, GPR-derived snow depth is not affected by glacier dynamics or firn compaction, but to a smaller degree by snow density and liquid water content. It is thus a valuable independent data source for validation. The simultaneous availability of the three datasets facilitates the comparison of the methods and contributes to a better understanding of processes that govern winter accumulation distribution on alpine glaciers.

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Section: Uncertainties In Gpr-derived Snow Depthmentioning

confidence: 99%

Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

Sold¹,

Huss²,

Hoelzle³

et al. 2013

J. Glaciol.

148

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show abstract

“…The presence of liquid water within the snowpack in the starting zone is a prerequisite and several field campaigns (Brun and Rey, 1987;Bhutiyani, 1996) and experiments under laboratory conditions (Zwimpfer, 2011;Yamanoi and Endo, 2002) found decreasing shear strength with increasing liquid water content. However, quantifying the amount of liquid water within the snowpack is a difficult task as even experienced observers tend to overestimate the amount of liquid water (Fierz and Föhn, 1995;Techel and Pielmeier, 2011). Only objective measurement devices relating the relative permittivity of the 3-phase medium wet snow to an amount of water provide reliable results.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction

Mitterer¹,

Schweizer²

2013

The Cryosphere

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Abstract. Wet-snow avalanches are notoriously difficult to predict; their formation mechanism is poorly understood since in situ measurements representing the thermal and mechanical evolution are difficult to perform. Instead, air temperature is commonly used as a predictor variable for days with high wet-snow avalanche danger -often with limited success. As melt water is a major driver of wet-snow instability and snow melt depends on the energy input into the snow cover, we computed the energy balance for predicting periods with high wet-snow avalanche activity. The energy balance was partly measured and partly modelled for virtual slopes at different elevations for the aspects south and north using the 1-D snow cover model SNOWPACK. We used measured meteorological variables and computed energy balance and its components to compare wet-snow avalanche days to non-avalanche days for four consecutive winter seasons in the surroundings of Davos, Switzerland. Air temperature, the net shortwave radiation and the energy input integrated over 3 or 5 days showed best results in discriminating event from non-event days. Multivariate statistics, however, revealed that for better predicting avalanche days, information on the cold content of the snowpack is necessary. Wet-snow avalanche activity was closely related to periods when large parts of the snowpack reached an isothermal state (0 • C) and energy input exceeded a maximum value of 200 kJ m −2 in one day, or the 3-day sum of positive energy input was larger than 1.2 MJ m −2 . Prediction accuracy with measured meteorological variables was as good as with computed energy balance parameters, but simulated energy balance variables accounted better for different aspects, slopes and elevations than meteorological data.

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“…Dielectric permittivity of the upper 1 m snow layer along the 2800 km long expedition route near Syowa and Wasa stations was measured; it was found to be a function of snow density 6 . Snow fork and other devices have been used in the Swiss Alps to observe point liquid water content in snowpack 7 . Snow melt and freeze conditions using in situ measurement have been reported by many researchers but owing to difficult terrain and inaccessibility, they have used satellite remote sensing to obtain spatial distribution of melt [8][9][10][11] .…”

mentioning

confidence: 99%

Understanding Relationship between Melt/Freeze Conditions Derived from Spaceborne Scatterometer and Field Observations at Larsemann Hills, East Antarctica during Austral Summer 2015-16

Bothale¹,

Anoop²,

Rao³

et al. 2017

Current Science

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Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects

Cited by 115 publications

References 31 publications

Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction

Understanding Relationship between Melt/Freeze Conditions Derived from Spaceborne Scatterometer and Field Observations at Larsemann Hills, East Antarctica during Austral Summer 2015-16

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