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Rheology of dense snow flows: Inferences from steady state chute-flow experiments
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View: Figures


Image of FIG. 1.
FIG. 1.

Experimental set-up at the Col du Lac Blanc (Alpes d’Huez, ). (a) Set-up: (1) moving tank, (2) hopper, (3) Archimedean screw, (4) channel. (b) Position of sensors (flow depth , velocity profile and basal stresses and ): lateral view of the chute, and schematic views (c) from the top, and (d) from the side.

Image of FIG. 2.
FIG. 2.

Velocity measurements: (a) typical signals recorded from a pair of optical sensors, (b) cross-correlation of these signals, inset: quadratic fit around the peaks of , (c) distribution of sensors.

Image of FIG. 3.
FIG. 3.

Details of performed runs: (a) number of runs for each series (the labels inside are year), (b) ranges of slope and flow depth for which steady uniform flows were obtained.

Image of FIG. 4.
FIG. 4.

Scheme of the three flow regimes as a function of the slope , deduced from flow depth measurements along the flume.

Image of FIG. 5.
FIG. 5.

Time evolution of various quantities during a typical steady uniform flow with : (a) flow depths measurements (averaged values: , , ), (b) basal normal stress and deduced average density , (c) measured basal tangential stress (—) and tangential stress deduced from the momentum balance (gray).

Image of FIG. 6.
FIG. 6.

Macroscopic behavior of dense snow flows: mean velocity for steady and uniform flows performed over (a) with and (b) discharge equation with and its linear fit: (—).

Image of FIG. 7.
FIG. 7.

Velocity profile of a typical snow flow : each point (◆) corresponds to the time-averaged measurements from one pair of sensors. The horizontal error bars correspond to the associated standard deviation. The vertical error bars represent the width of a sensor. The highest point (◇) was obtained from the correlation between the flow depth measurements taken along the channel. It represents the time-average flow velocity at the free surface, and its vertical error bar corresponds to the standard deviation of the flow thickness over time. The inset shows the velocity of aggregates at the free surface as a function of their lateral position .

Image of FIG. 8.
FIG. 8.

Variation of the velocity profiles. Reproducibility of the experiment: flows of similar thicknesses and the same slope (37°) (a) performed over the same night, (b) performed over different series a few weeks apart. (c) Effect of the slope: six flows of similar thickness performed over one night. (d) Effect of the thickness: three flows at a constant slope performed over one night; the inset shows the corresponding shear rate at different depths: .

Image of FIG. 9.
FIG. 9.

Typical snow velocity profile with (●) fitted by the bilinear function of Eq. (2) (—).

Image of FIG. 10.
FIG. 10.

Rheological properties of the two layers: (a) , (b) , (c) , (d) , (e) , (f) . The data are presented as a function of the series of experiments performed over the same night, i.e., with constant snow grain properties. Flows of similar thickness (left column): (▲), (●, ▼), (◼, ◆). Flows of same slope (right column): (○, △), (◻, ▽), (◇). The uncertainty of the velocity measurements is typically . The uncertainty of the shear rate in the upper layer (thickness ) can be estimated by and the uncertainty of the shear rate in the basal layer (much thinner: ) by . The estimation of the uncertainty of the interfacial velocity by makes it possible to deduce the uncertainty of the basal thickness .

Image of FIG. 11.
FIG. 11.

Size distribution of snow aggregates during steady and uniform flows: measurements from video of the free surface of one flow (bars) and power law (—) obtained by the correlation of the velocities between two neighboring sensors inside several flows, a method developed by (Bouchet, 2003).


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Rheology of dense snow flows: Inferences from steady state chute-flow experiments