Velocity measurements of wet snow avalanche on the Dhundi snow

Velocity measurements of wet snow avalanche on the Dhundi snow
Annals of Glaciology A51(54) 2010
139
Velocity measurements of wet snow avalanche on the Dhundi
snow chute
Agraj UPADHYAY, Amod KUMAR, Arun CHAUDHARY
Snow and Avalanche Study Establishment, Him Parisar, Sector 37A, Chandigarh 160036, India
E-mail: [email protected]
ABSTRACT. Wet snow avalanches in India are common during the mid- and late winter in the Pir Panjal
Range (2000–3000 m a.s.l.) and during the late winter in the Great Himalayan Range (3000 m a.s.l. and
above). Although it is well known that the presence of liquid water in snow makes the flow behaviour of
wet snow avalanches different from that of dry snow avalanches, there exist few actual flow
measurements with wet snow. The aim of this investigation is to understand the dynamics of wet snow
avalanches by conducting medium-scale experiments (volumes of 3, 6 and 11 m3) on the Dhundi snow
chute in Himachal Pradesh, India. We measured flow velocities using video data, as well as optical
velocity sensors installed on the side walls and running surface. Measurement results relating to the slip
velocity of the front and tail of the moving snow mass, as well as the average slip velocity, are presented.
In addition, we use the results of the vertical velocity profile measurements to calculate the effective
viscosity of snow at two locations within the flow. We identified a shear thinning type of behaviour,
suggesting that a single avalanche rheology cannot describe wet snow avalanche behaviour.
INTRODUCTION
A central problem in avalanche dynamics is to model
avalanche speed and runout distance, primarily for avalanche hazard applications (Gruber and Bartelt, 2007).
Decades of research has concentrated on modelling the
behaviour of dry snow avalanches because these avalanches
have higher flow velocity (and therefore higher impact
pressures). Numerical run-out models are available in
Switzerland (Christen and others, in press), Austria (Sampl
and Granig, in press) and France (Naaim and others, 2004).
The empirical Voellmy model (Salm, 1993) is still in
widespread use in many countries, since well-calibrated
parameters for extreme, dry flowing avalanches have been
found by back-calculation of documented avalanche events.
Other models for flowing avalanches have been proposed by
Dent and Lang (1983), Norem and others (1987, 1989),
Bartelt and others (2006) and Bartelt and McArdell (2009).
However, few researchers have attempted to model the
dynamics of wet snow avalanches.
Chute experiments have identified differences between
dry and wet snow flows (Kern and others, 2004), as well as
dry and slush flows (Jaedicke and others 2008). Measured
internal velocity profiles at the Vallée de la Sionne
experimental site in Switzerland reveal clear differences
between dry and wet snow avalanches (Kern and others
2009). Furthermore, granulometric investigations of wet and
dry snow avalanche deposits reveal a tendency to larger
mean particle sizes in wet snow avalanches, suggesting
smaller shear gradients (Bartelt and McArdell, 2009). Gauer
(2005) proposed as a solution the modelling of slush flows
using numerical techniques, considering wet snow as a
multiphase flow of ice and water.
Obtaining experimental data from real-scale avalanches
is expensive and time-consuming, hence the use of chute
experiments is an alternative method. Kern and others
(2004) conducted chute experiments to determine the
vertical velocity profile and basal friction forces of sheared
flow of snow on a rough surface. They reported the presence
of a 0.05 m thick shear layer at the bottom and a 0.35 m
thick overlaying low shear layer. Platzer and others (2007)
attempted to determine basal friction (basal shear to normal
stress ratio) using chute experiments. Rastello and Bouchet
(2007) used a similar experimental facility at Cemagref in
France to measure surface oscillations and determine
friction laws in channelled snow flows.
In the present work, we present the results of wet snow
avalanche experiments carried out on the snow chute in
Dhundi, Himachal Pradesh, India. The objective of this work
is to determine flow parameters such as slip, front and tail
velocities of the moving mass, understand the shearing of
snow layers from vertical velocity profile data and determine
the approximate effective viscosity of snow, and thus obtain
an understanding of wet snow avalanche dynamics. We do
not consider the formation of wet snow avalanches.
METHODOLOGY
Inclined chute facility at Dhundi
The Snow and Avalanche Study Establishment (SASE),
Manali, India, has constructed an inclined chute at Dhundi,
which is located about 20 km from Manali at 2800 m a.s.l.
(Fig. 1). At this site, cumulative average snowfall of
approximately 11 m occurs every winter season and average
winter ambient temperatures fall in the range –15 to +108C.
The inclined chute is 61 m long and 2 m wide. The side walls
of the snow chute’s main channel are 1 m high. A schematic
diagram (L-section) of the chute with instrumentation
scheme is shown in Figure 2a. The chute is divided into
six sections (1–6 from top to bottom); details of each section
are shown in Table 1.
The bottom of the chute is composed of smooth steel
sheets. We painted the bottom chute surface in alternate red
and grey strips 0.5 m wide, to aid the determination of front
velocity with video recordings. The side walls of the chute
are made of transparent polycarbonate sheets to minimize
friction between the side walls and the flowing snow. These
transparent sheets also facilitate observation of the flow
through the side walls of the chute.
140
Upadhyay and others: Wet snow avalanche velocity
Fig. 1. Inclined chute facility at Dhundi.
We gathered snow from the surrounding area to fill chute
section 1 and released the snow mass by opening the release
gate. The released snow mass flows down the chute and
usually stops in the run-up section (chute section 6).
Video data acquisition system
To visualize the snow flow, we mounted video cameras over
the side walls of the chute. The image-capturing speed of
these cameras was 25 frames s–1. The camera location is
shown in Figure 2d. We used recorded video data to
determine the flow velocity and flow height of the moving
snow mass.
Optical velocity sensors
We used optical velocity sensors at various locations on the
chute to determine flow velocity. Each optical velocity
sensor consists of two optical sensors placed at a fixed
distance of 12 mm inside a tube. Each optical sensor consists
of an optical transmitter and a receiver. Any snow particle
passing over the optical sensor reflects the transmitted
optical signal, which is received by the sensor receiver. We
recorded the reflected signal of both optical sensors with a
sampling frequency of 20 kHz using an analogue/digital
converter. We used stored data to calculate velocity,
employing a cross-correlation technique with a time
window of 1 s. Velocity calculations using this technique
are discussed in detail by McElwaine and Tiefenbacher
(2003) and Tiefenbacher and Kern (2004).
Fig. 2. (a) Schematic diagram of chute facility at Dhundi. (b)
Velocity sensor array 3. (c) Velocity sensor array 4. (d) Arrangement
of video cameras, flow height markers and velocity sensor arrays 1
and 2 on chute.
Slip velocity measurement
We measured slip velocities at the centre line of the chute
bottom surface using velocity sensor arrays 1 and 2 on
sections 4 and 5 respectively (Fig. 2d). Each array consists of
five optical velocity sensors placed 1 m apart from each other.
Table 1. Details of different sections of inclined chute
Section
No.
8
Vertical velocity profile measurement
To measure the vertical velocity profiles, we used velocity
sensor arrays 3 and 4 on sections 4 and 5 respectively. In
array 3, we placed five optical velocity sensors at distances
of 24, 34, 44, 54 and 64 mm from the chute surface. The
maximum downstream distance between the two sensors is
not more than 12.5 cm (Fig. 2b). In array 4, we placed five
optical velocity sensors at distances of 9, 17, 25, 39 and
53 cm from the chute surface (Fig. 2c).
Flow height measurements
We measured flow heights at two locations on section 4
using video data, and flow height on section 5 using optical
Slope
Length Cross-section shape
Width
m
1
40
5.5
Rectangular
2
35
7.5
Rectangular
3
30
6.0
Rectangular
4
30
22
Rectangular
5
12
8
Rectangular
6
–1.8
12
Open platform
2 m (uniform in flow
direction)
Diverging from 2 m to
4 m in flow direction
Converging from 4 m to
2 m in flow direction
2 m (uniform in flow
direction)
2 m (uniform in flow
direction)
4 m (uniform in flow
direction)
Upadhyay and others: Wet snow avalanche velocity
141
Table 2. Details of experiments
Experiment Date in 2008
No.
1
2
3
4
5
6
7
8
13 March
13 March
9 March
9 March
10 March
13 March
10 March
11 March
Ambient
temperature
Volume
released
Average
density
8C
m3
kg m–3
7
7
9.5
9.5
10
6.5
10
8
3
3
6
6
6
6
11
11
446
446
450
450
473
446
473
480
Fig. 3. Snow wetness measured using TDR.
sensors of velocity sensor array 4. On section 4 we placed
flow height markers 1 and 2 on the side wall and video
cameras 2, 3 and 4 (Fig. 2d). By analysing the video data of
flowing snow with markers in the background, we estimated
the maximum flow height of every avalanche released.
On section 5 we analysed the data of each optical
velocity sensor of array 4 and obtained the lower and upper
limits of the interval in which flow height at that location
lies.
Equation (2) can be solved for velocity with boundary
conditions of slip velocity, v0, at the basal chute surface to
obtain
1
z
v ðz Þ ¼
sin ð’Þ þ v0 ,
ghz 1 ð3Þ
eff
2h
Snow wetness
Experimentation
We used time-domain reflectometer (TDR) techniques to
determine snow wetness. The instrument consists of forktype sensors and a data-logging system. It records the
dielectric constant of the material in which the sensor is
inserted. We inserted these sensors at depths of 5, 10, 15 and
20 cm from the top snow surface in an observatory near the
experimental site and recorded the dielectric constant of the
snow samples from 0830 to 1700 h on days of experimentation. Using the measured dielectric constant and density of
snow, we calculated the volumetric moisture content
(volume of water/volume of snow) of the released snow
mass (Denoth, 1994). The equation used for this purpose is
%moisture ¼ 108:6957 0:187
We conducted a total of eight experiments on the chute,
with three release volumes and density varying between 400
and 500 kg m–3. Ambient temperatures during experimentation were 5–108C. Details of these experiments are given in
Table 2.
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
þ 0:1872 4 0:0046ð1 þ 1:92 þ 0:442 DCÞ , ð1Þ
where is the measured snow density and DC is the
measured dielectric constant of snow.
Effective viscosity determination
We used the depth-averaged momentum balance equation
for two-dimensional incompressible, steady flows to find the
effective viscosity of wet snow. We therefore assume a
hydrostatic pressure gradient. This simplified equation is
solved with boundary conditions of zero shear stress at the
free surface and by replacing the shear stress term according
to a Newtonian law. As a result, the following equation for
velocity gradient is obtained:
@v
1
g ðh z Þ sin ’,
¼
@z eff
ð2Þ
where @/@z is the velocity gradient in the flow normal
direction, eff is the effective viscosity, is the density of
flowing snow mass, is the slope angle and h is the flow
height.
where v 0 is the basal slip velocity. Equation (3) is applied to
find the effective viscosity of the flowing snow using
experimental data of the measured vertical velocity profiles.
RESULTS AND DISCUSSION
Snow wetness
The average wetness of the avalanches released on the
Dhundi chute was found to be 10–30%. Average values of
snow wetness (with standard deviation) on the days of
experimentation are plotted in Figure 3. We measured snow
wetness within the top 20 cm of the snowpack, but the
actual wetness of the avalanche snow released may be
somewhat higher due to exposure to higher temperatures at
release.
General observations from video data
We observed the accelerating and longitudinally spreading
snow mass from section 2 to section 4 (Fig. 4a–c). It
comprised non-uniformly distributed softened wet snow and
well-bonded discrete snowballs in the avalanche mass.
These snowballs either form or disintegrate during the flow
and generally remain in the upper part of the flowing mass.
Sometimes they roll ahead of the main avalanche body and
create an irregular avalanche front. The irregularity of the
avalanche front makes it difficult to estimate the front
propagation speed using video data.
Flow velocity using video data
After analysing video data we obtained front propagation
velocities of 6–12 m s–1 on section 4. We also calculated the
probable errors introduced due to speed limitations of the
video camera (25 frames s–1) and the spatial resolution of the
strips on the chute surface (0.5 m). The flow velocities
142
Upadhyay and others: Wet snow avalanche velocity
Fig. 4. (a–c) Longitudinal spreading of moving mass between sections 1 and 4.
obtained along with the error bars are plotted in Figure 5a–c
for release volumes of 3, 6 and 11 m3, respectively. The
results show that flow velocities are 6–10, 7–12 and 8–
12 m s–1, respectively. Probable errors were calculated to be
15–20%. We observed considerably more variation in
flow velocity for the 6 m3 release volume than for the other
release volumes. We also observed outliers in the velocity
data of all the experiments, generally either at the start or the
end of the measurement. This large variation in velocity over
a small distance is certainly due to human error in
identifying the flow front at these locations. Better video
data results could be obtained by using high-speed video
cameras and improving the spatial resolution of the strips on
the chute surface.
Slip velocity
We obtained slip velocities of avalanche masses from front
to tail at each sensor of velocity sensor arrays 1 and 2
(Fig. 6). Similar curves were obtained for each experiment.
In general, we observed a decrease in slip velocity from front
to tail of the avalanche, with spikes at irregular intervals. At a
few locations we observed a sudden drop in velocity. We
obtained the average slip velocity at each sensor location of
arrays 1 and 2, by averaging slip velocities of the avalanche
from front to tail. Between sections 4 and 5 the average slip
velocity varied for release volumes of 3, 6 and 11 m3
(Fig. 7a–c).
For release volumes of 3, 6 and 11 m3 we obtained
average slip velocities of 7–10, 6.5–12.5 and 8–12 m s–1,
respectively, at section 4, and 4.5–6, 5–8.5 and 5–8 m s–1,
respectively, at section 5. The average slip velocities at the
end of section 4 were 8.5, 9.25 and 10.25 m s–1 for release
volumes of 3, 6 and 11 m3, respectively. These results
suggest slightly lower velocities for lower release masses.
We observed a significant decrease in average slip
velocity due to slope angle reduction between sections 4
and 5. The average reduction in slip velocity due to slope
change was observed to be about 37% for a release volume
Fig. 5. Front velocity at section 4, measured using video data for
release volumes of (a) 3 m3, (b) 6 m3 and (c) 11 m3.
Fig. 6. Slip velocity data obtained using cross-correlation analysis of
optical sensor signals.
Upadhyay and others: Wet snow avalanche velocity
143
Fig. 8. Variation of front and tail velocities of moving snow mass at
sections 4 and 5.
mass with lower velocities. Due to the higher difference in
front and tail velocities, we can expect higher longitudinal
spreading in such conditions.
Vertical velocity profile
Fig. 7. Variation of average slip velocity at sections 4 and 5 for
release volumes of (a) 3 m3, (b) 6 m3 and (c) 11 m3.
of 3 m3 and about 30% for release volumes of 6 and 11 m3,
indicating that the effect of slope reduction on velocity is
greater for small avalanches.
We did not find a specific trend in velocity variation over
sections 4 and 5 due to the considerable fluctuations in
average slip velocities from front to tail of the avalanche
(Fig. 6).
We obtained vertical velocity profiles at two locations, one
at section 4 at a distance of about 35 m from the point of
release (over section 4) and the other at a distance of about
40 m (over section 5). At the first location, average slip
velocities of about 8 m s–1 and a shear rate of 62.5 s–1 were
observed in a bottom layer with thickness of 0.064 m. At the
second location, average slip velocities of 7 m s–1 were
obtained, with a shear rate of 6 s–1 over the entire flow
height. The results obtained are shown in Figure 9. The
values of shear rate obtained in our experiments are very
close to that obtained by Kern and others (2004) for a basal
shear layer with an upper plug layer. The presence of
different shear rates at two places with different flow
conditions shows the varying rheological behaviour of wet
snow avalanches as a function of position within the
avalanche.
Flow height
On section 4, we found flow heights of 10–25, 15–35 and
20–35 cm for release volumes of 3, 6 and 11 m3, respectively. On section 5 we obtained flow heights of 17–25 cm for
a release volume of 11 m3, and 9–17 cm for release volumes
of 3 and 6 m3. We used the flow height data to determine the
effective viscosity of wet snow.
Front and tail velocities
At section 4, we obtained higher average velocities at the
avalanche front (9–12 m s–1) than at the tail (6–9 m s–1)
(Fig. 8). These results are supported by video data observations of longitudinal spreading of the avalanche mass.
Also Gauer and others (2007), in natural avalanche
measurements at the Ryggfonn test site, Norway, found
maximum avalanche speed in the frontal part and a rapid
decrease in velocity afterwards, with no constant velocity
gradient over the avalanche body from front to tail.
We measured front and tail velocities at section 5 after a
slope reduction, and obtained front velocities of 7–10 m s–1
and tail velocities of 3–5 m s–1 (Fig. 8). These results show a
slightly higher reduction in average tail velocity in comparison to front velocity due to slope angle reduction, which
leads to a higher difference of front and tail velocity at
section 5 in comparison to section 4. This suggests that the
effect of a slope reduction is more dominant on a moving
Fig. 9. Vertical velocity profile of moving snow mass on sections 4
and 5.
144
Effective viscosity
Using the measured vertical velocity profiles and flow
heights, we estimated the effective viscosity of the flows and
compared it to previously reported values.
Within a 64 mm shear layer at section 4, we obtained an
effective viscosity of 8.64 5.23 Pa s, averaged over all
experiments. The effective viscosity in the run-out zone is
higher: 42.2 15.95 Pa s at section 5. Thus, the effective
viscosity changes as a function of position on the chute.
The viscosity values obtained for section 4 are close to the
viscosity of honey (2–10 Pa s) and molasses (5–10 Pa s).
Assuming an average wet snow density of 450 kg m–3, we
found effective kinematic viscosity coefficients of wet snow
of 0.019 0.012 m2 s–1 at section 4 and 0.094 0.035 m2 s–1
at section 5. These values are much higher than those of
0.001 m2 s–1 given by Maeno and Nishimura (1979) and
Maeno and others (1980) for dry sieved snow in sub-zero
temperatures and 0.004 m2 s–1 given by Dent and Lang
(1982) for dry snow of density 250–350 kg m–3. Jaedicke and
others (2008) obtained much higher effective viscosity values
(67 8 Pa s) for their slushflow experiments.
The flow conditions were not the same in all experiments,
hence the large standard deviation in effective viscosity
values. A decrease in shearing rate and increase in viscosity
between sections 4 and 5 indicates the effect of shear rate on
the effective viscosity. This increase in viscosity with
decrease in shear rate (or vice versa) is termed shear
thinning.
CONCLUSIONS
The aim of the present investigation was to improve our
understanding of wet snow avalanche dynamics by performing medium-scale experiments at the Dhundi snow chute.
We released wet snow avalanches with a moisture content
of 10–30% by volume. The flowing avalanche mass
contained well-bonded snowballs; interstitial pore space
contained wet snow ice grains. We obtained maximum
average slip velocities of >10 m s–1, much higher than the
values obtained previously by researchers working on
inclined chutes with snow. This is due to the very smooth
surface of the Dhundi chute.
The measured slip velocities provide information about
the variation of velocity between the avalanche front and
tail. For smaller release masses we found lower slip
velocities. In addition, the shear velocity gradient varied
between front and tail. Front velocities are higher than tail
velocities, especially in the acceleration phase, and this is
responsible for longitudinal spreading of the avalanche.
Slope reduction contributes to the total energy dissipation, and its effect is greater on smaller avalanches.
Avalanche masses moving at lower velocities are affected
more at the point of slope reduction.
Internal shearing of snow layers is an integral part of wet
snow avalanche rheology. However, the extent of the
shearing in the avalanche path depends on the location of
the avalanche. Depending upon surface roughness, wet
snow avalanches may move with zero shear (plug flow, tail)
or as a highly sheared flow (front). We observed shear
thinning material behaviour. Hence, a single rheological
model cannot define the shearing phenomena in wet snow
avalanches. The effective Newtonian shear viscosity for wet
snow avalanches, with moisture content 10–30%, is higher
Upadhyay and others: Wet snow avalanche velocity
than that of dry snow reported in the literature and lower
than that of slush flow. However, its value is highly variable
depending upon the position.
Our conclusions are based on results from a limited
number of experiments. Measurements of slip velocity and
vertical velocity profiles at a greater number of locations
along the snow chute will contribute to a better understanding of the dynamics of wet snow avalanches.
ACKNOWLEDGEMENTS
We thank R.N. Sarwade, former Director, and A. Ganju,
present Director of the Snow and Avalanche Study Establishment, for constant support for carrying out the present work.
We also thank all the members of the Dhundi team for their
support during the experiments.
REFERENCES
Bartelt, P. and B.W. McArdell. 2009. Granulometric investigations
of snow avalanches. J. Glaciol., 55(193), 829–833.
Bartelt, P., O. Buser and K. Platzer. 2006. Fluctuation–dissipation
relations for granular snow avalanches. J. Glaciol., 52(179),
631–643.
Christen, M., J. Kowalski and P. Bartelt. In press. Avalanche
modelling with RAMMS. In Proceedings of the International
Snow Science Workshop, 27 September–2 October 2009,
Davos, Switzerland. Davos, International Snow Science Workshop. CD-ROM.
Denoth, A. 1994. An electronic device for long-term snow wetness
recording. Ann. Glaciol., 19, 104–106.
Dent, J.D. and T.E. Lang. 1982. Experiments on mechanics of
flowing snow. Cold Reg. Sci. Technol., 5(3), 253–258.
Dent, J.D. and T.E. Lang. 1983. A biviscous modified Bingham
model of snow avalanche motion. Ann. Glaciol., 4, 42–46.
Gauer, P. 2005. Numerical modeling of a slush flow event. In Elder,
K., ed. Proceedings of the International Snow Science Workshop, 19–24 September 2004, Jackson Hole, Wyoming, USA.
Jackson Hole, WY, International Snow Science Workshop,
39–43. CD-ROM.
Gauer, P. and 7 others. 2007. On full-scale avalanche measurements at the Ryggfonn test site, Norway. Cold Reg. Sci. Technol.
49(1), 39–53.
Gruber, U. and P. Bartelt. 2007. Snow avalanche hazard modelling
of large areas using shallow water numerical methods and GIS.
Environ. Model. Softw., 22(10), 1472–1481.
Jaedicke, C., M.A. Kern, P. Gauer, M.-A. Baillifard and K. Platzer.
2008. Chute experiments on slushflow dynamics. Cold Reg. Sci.
Technol., 51(2–3), 156–167.
Kern, M., F. Tiefenbacher and J. McElwaine. 2004. The rheology of
snow in large chute flows. Cold Reg. Sci. Technol., 39(2–3),
181–192.
Kern, M., P. Bartelt, B. Sovilla and O. Buser. 2009. Measured shear
rates in large dry and wet snow avalanches. J. Glaciol., 55(190),
327–338.
Maeno, N. and K. Nishimura. 1979. Fluidization of snow. Cold
Reg. Sci. Technol., 1(2), 109–120.
Maeno, N., K. Nishimura and Y. Kaneda. 1980. Viscosity and heat
transfer in fluidized snow. J. Glaciol., 26(94), 263–274.
McElwaine, J.N. and F. Tiefenbacher. 2003. Calculating internal
avalanche velocities from correlation with error analysis. Surv.
Geophys., 24(5–6), 499–524.
Naaim, M., F. Naaim-Bouvet, T. Faug and A. Bouchet. 2004. Dense
snow avalanche modeling: flow, erosion, deposition and
obstacle effects. Cold Reg. Sci. Technol., 39(2–3), 193–204.
Norem, H., F. Irgens and B. Schieldrop. 1987. A continuum model
for calculating snow avalanche velocities. IAHS Publ. 162
Upadhyay and others: Wet snow avalanche velocity
(Symposium at Davos 1986 – Avalanche Formation, Movement
and Effects), 363–379.
Norem, H., F. Irgens and B. Schieldrop. 1989. Simulation of snowavalanche flow in run-out zones. Ann. Glaciol., 13, 218–225.
Platzer, K., P. Bartelt and M. Kern. 2007. Measurements of dense
snow avalanche basal shear to normal stress ratios (S/N).
Geophys. Res. Lett., 34(7), L07501. (10.1029/2006GL028670.)
Rastello, M. and A. Bouchet. 2007. Surface oscillations in channeled snow flows. Cold Reg. Sci. Technol., 49(2), 134–144.
145
Salm, B. 1993. Flow, flow transition and runout distances of flowing
avalanches. Ann. Glaciol., 18, 221–226.
Sampl, P. and M. Granig. In press. Avalanche simulation with
SAMOS-AT. In Proceedings of the International Snow Science
Workshop, 27 September–2 October 2009, Davos, Switzerland.
Davos, International Snow Science Workshop. CD-ROM.
Tiefenbacher, F. and M. Kern. 2004. Experimental devices to
determine snow avalanche basal friction and velocity profiles.
Cold Reg. Sci. Technol., 38(1), 17–30.
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