Point observations of liquid water content in wet snow

Transcription

The Cryosphere, 5, 1–14, 2011
www.the-cryosphere.net/5/1/2011/
doi:10.5194/tc-5-1-2011
© Author(s) 2011. CC Attribution 3.0 License.
The Cryosphere
Point observations of liquid water content in wet snow –
investigating methodical, spatial and temporal aspects
F. Techel and C. Pielmeier
WSL Institute for Snow and Avalanche Research SLF, 7260 Davos, Switzerland
Received: 22 September 2010 – Published in The Cryosphere Discuss.: 12 October 2010
Revised: 30 March 2011 – Accepted: 13 May 2011 – Published:
Abstract. Information about the volume and the spatial
and temporal distribution of liquid water in snow is important for forecasting wet snow avalanches and for predicting
melt-water run-off. The distribution of liquid water in snow
is commonly estimated from point measurements using a
“hand” squeeze test, or a dielectric device such as a “Snow
Fork” or a “Denoth meter”. Here we compare estimates of
water content in the Swiss Alps made using the hand test to
those made with a Snow Fork and a Denoth meter. Measurements were conducted in the Swiss Alps, mostly above tree
line; more than 12 000 measurements were made at 85 locations over 30 days. Results show that the hand test generally
over estimates the volumetric liquid water content. Estimates
using the Snow Fork are generally 1 % higher than those derived from the Denoth meter. The measurements were also
used to investigate temporal and small-scale spatial patterns
of wetness. Results show that typically a single point measurement does not characterize the wetness of the surrounding snow. Large diurnal changes in wetness are common in
the near-surface snow, and associated changes at depth were
also observed. A single vertical profile of measurements is
not sufficient to determine whether these changes were a result of a spatially homogeneous wetting front or caused by infiltration through pipes. Based on our observations, we suggest that three measurements at horizontal distances greater
than 50 cm are needed to adequately characterize the distribution of liquid water through a snowpack. Further, we suggest a simplified classification scheme that includes five wetness patterns that incorporate both the vertical and horizontal
distribution of liquid water in a snowpack.
Correspondence to: F. Techel
([email protected])
1
Introduction
The distribution and amount of liquid water in snow affects surface albedo (Warren and Wiscombe, 1985; Gupta
et al., 2005), snow stability (e.g. Armstrong, 1976; Kattelmann, 1985; Conway and Raymond, 1993), and is important
for forecasting the onset of melt-water run-off (Jones et al.,
1983) and reservoir management (Kattelmann and Dozier,
1999).
In Switzerland, most snowpack information comes from
snow profiles measured by researchers, avalanche professionals, and observers from the Swiss snow observation network. Measurements are made at flat study plots as well as
on potential avalanche slopes with different elevations and
aspects. Typically about 1000 snow profiles (mostly in dry
snow) are collected each year, and used by the avalanche
warning service as input to the national avalanche hazard
forecast (Schweizer and Wiesinger, 2001). Liquid water content is typically estimated by conducting “hand” tests for
each stratigraphic snow layer according to Swiss and international observational guidelines (WSL, 2008; Fierz et al.,
2009, Table 1). In this test, a sample is squeezed by hand, and
the water content is estimated by observing its response (Table 1). A magnifying lens can also be used to detect the presence/absence of liquid water. Measurements of snow temperature can also be used to determine the presence/absence
of water; snow is expected to be dry at temperatures less than
0 ◦ C. However, estimating liquid water content is difficult
even for experienced observers (Martinec, 1991b; Fierz and
Föhn, 1994), partly because water flow through snow varies
both spatially and temporally (e.g. Colbeck, 1979; Marsh,
1988; Conway and Benedict, 1994).
Our objectives in this study are threefold: (i) investigate
the reliability of point observations in relation to temporal and small-scale spatial variability; (ii) compare measurements using the hand test with other measurements using di-
Published by Copernicus Publications on behalf of the European Geosciences Union.
2
F. Techel and C. Pielmeier: Point observations of liquid water content in wet snow
Table 1. Hand test for the qualitative estimation of liquid water content (mWC) and the approximate range of liquid water content (θ ). The
detailed description is taken from the International Classification of Seasonal Snow on the Ground (Fierz et al., 2009, p. 8). This classification
is also used in Swiss observational guidelines (WSL, 2008). Half index classes may also be used. ts – snow temperature.
Wetness
Content
Index
(mWC)
Description
θ
[vol. %]
Dry
1
ts ≤0.0 ◦ C. Disaggregated snow grains have little tendency
to adhere to each other when pressed together.
0
Moist
2
ts = 0.0 ◦ C. The water is not visible, even at 10× magnification.
When lightly crushed, the snow has a tendency to stick together.
0–3
Wet
3
ts = 0.0 ◦ C. The water can be recognized at 10× magnification
by its meniscus between adjacent snow grains, but water cannot
be pressed out by moderately squeezing the snow in the hands.
3–8
Very Wet
4
ts = 0.0 ◦ C. The water can be pressed out by moderately
squeezing the snow in the hands, but an
appreciable amount of air is confined within the pores.
8–15
Soaked
5
ts = 0.0 ◦ C. The snow is soaked with water and
contains a volume fraction of air from 20 to 40 %.
>15
electric methods; (iii) examine whether wetness observations
over larger areas would help improve wet snow avalanche
forecasting.
2
2.1
Background
Liquid water in snow
Liquid water is introduced to snow by rain and/or melt. Melting at the surface depends primarily on the incoming flux of
shortwave radiation, which varies with slope aspect and elevation. The advance of a wetting front into snow is seldom
uniform; even under controlled laboratory conditions it is difficult to achieve a homogeneous distribution (Brun, 1989).
Infiltration usually starts through isolated small “flow fingers” (Marsh, 1988; Schneebeli, 1995; Waldner et al., 2004),
which enlarge into channels with increased time and flow
(Kattelmann and Dozier, 1999). The pattern and timing of
infiltration depends on snow structure, temperature, slope angle and the amount of liquid water entering the snowpack
(Conway and Benedict, 1994; Fierz and Föhn, 1994). Gravitational forces usually dominate infiltration, although layerparallel infiltration is often observed above impermeable ice
layers, or capillary barriers that consist of fine grains layers
of coarse grains (Wankiewicz, 1979; Jordan, 1994; Waldner
et al., 2004). Introduction of liquid water into snow leads to
rapid changes in grain shape (Brun, 1989; Coléou and Lesaffre, 1998), grain coarsening (Raymond and Tusima, 1979;
Brun, 1989; Marsh, 1987) and an increase in bulk density
(Colbeck, 1997; Marshall et al., 1999; Jordan et al., 2008).
Important feedback mechanisms exist between snow metaThe Cryosphere, 5, 1–14, 2011
morphism, hydraulic conductivity and water flow (Jordan
et al., 2008). The amount of liquid water influences the
mechanical properties of snow. Relatively small amounts
of liquid water can reduce the strength of snow; Techel
et al. (2011) found that the strength of temperature-gradient
snow (such as facets or depth hoar) decreased significantly
at low water contents (θ < 3 vol. %), while Colbeck (1982)
described loss in strength of seasonal snow at approximately
8 vol. %.
2.2
Estimation and measurement of liquid water in
snow
Liquid water content of snow has been measured by centrifugal separation, melting calorimetry, freezing calorimetry, alcohol calorimetry and the dilution method. These methods,
which are summarized by Stein et al. (1997), are difficult to
perform and time-consuming, which makes them impractical for operational forecasting. Liquid water content has also
been estimated by measuring the real and imaginary parts
of the permittivity of snow. This measurement is diagnostic
0
of liquid water because the permittivity of water (i ≈ 86) is
0
0
much higher than those of air (i ≈ 1), and ice (i ≈ 3.15)
(Frolov and Macharet, 1999; Louge et al., 1998). In this
study, we compare “hand” measurements with measurements
made with a “Finnish Snow Fork” (SnF, Sihvola and Tiuri,
1986; Toikka, 2009) and also with a Denoth meter (Dn, Denoth, 1994).
The Snow Fork is a two-pronged wave-guide that operates at ∼1 GHz and measures both the real and the imaginary
parts of the permittivity simultaneously measuring changes
in the resonance curve between air and snow (Fig. 1).
10 cm
nd C. Pielmeier: Point observations of liquid water content in wet snow
Fig. 1. Denoth (left) and Snow Fork (right) measurement devices.
Figures
3
Fig.
Shown are
are 50
50cm
cmwide
wide
Fig.2.2.Response
Responseof
of water
water flow
flow in
in aa slope.
slope. Shown
frontal
down-slopein
inexperiments
experimentsusing
using
frontalviews
viewsof
ofthe
thepit-wall
pit-wall facing
facing down-slope
dye-tracers.
immediately after
aftercutting
cuttinga a
dye-tracers. The
The left
left photo
photo was
was taken
taken immediately
pit-wall,
the
right
picture
approximately
30
s
later.
The
size
the
pit-wall, the right picture approximately 30 s later. The size ofofthe
wet
due to
towater
waterflowing
flowingfrom
from
wetarea
areaatatthe
thepit-wall
pit-wall increased
increased rapidly
rapidly due
lateral
lateralflow
flowchannels
channels into
into the
the pit-wall.
10 cm
Fig.
Fork (right)
(right)measurement
measurementdevices.
devices.
Fig.1.1.Denoth
Denoth (left)
(left) and Snow Fork
Insertion of the Snow Fork compresses the surrounding
snow, which increases snow density by approximately 1–
2 %, which has a small effect on the estimate of the real part
of the permittivity (Sihvola and Tiuri, 1986). The snow fork
that we used provided an estimate of water content on samples of area 7.5 × 2 cm2 . The SnF has been used in a variety
of studies
including
measuring
spatial
wetness
Fig.
2. Response
of water
flow in athe
slope.
Shown
are 50distribucm wide
tion (Williams
et pit-wall
al., 1999),
snow
characteristics
in Antarcfrontal
views of the
facing
down-slope
in experiments
using
tica (KärkäsThe
et left
al., photo
2005),was
and
the immediately
wetness of after
snowcutting
in ski a
dye-tracers.
taken
pit-wall,
the right picture
approximately 30 s later. The size of the
tracks (Moldestad,
2005).
wetThe
area Denoth
at the pit-wall
rapidly probe,
due to water
from
meter increased
is a capacitance
whichflowing
measures
lateral
flow channels
theofpit-wall.
the permittivity
of into
snow
area 13 × 13.5 cm2 (Fig. 1). A
separate measurement of density is required to solve for the
imaginary part of the permittivity (Denoth, 1994), which is
necessary to estimate liquid water content. The Denoth meter has also been used in field studies to monitor changes in
snowpack wetness during the melt period (Martinec, 1991a;
Kattelmann and Dozier, 1999), and to validate snowpack
models (Mitterer et al., 2010).
The accuracy of measurements made by dielectric methods is ±0.5 vol. % (Sihvola and Tiuri, 1986; Fierz and Föhn,
1994). Additional uncertainty can arise if sensors near
the surface are affected by solar radiation (Lundberg et al.,
2008). These methods are destructive to the snow sample and
also require the excavation of a snow-pit, which can cause
local disturbances to water flow (Fig. 2). More recently, nondestructive measurement methods like the Snow Pack Analyser (Mess-Systemtechnik, 2010) and ground-penetrating
radar installed upward-looking at the snow-ground interface
have been applied to measure snow wetness (Heilig et al.,
2009). In addition, multispectral imaging from satellites has
been used successfully to map regions of dry and wet snow
surfaces (e.g. Gupta et al., 2005).
3
Scope and aim
To address the three objectives of this study, we designed a
series of measurements to investigate the following specific
questions:
1. Does the “hand” test provide a realistic point estimate
of water content in snow?
2. How do layer characteristics such as hardness, grain
shape or size, influence the results from the “hand” test?
3. Can comparable measurements of snowpack wetness be
achieved by either measuring before digging a snow pit
or at the side-wall of a snow profile?
4. Is it necessary to consider small-scale spatial variability
in water content when interpreting wetness profiles?
Based on the answers to these questions we developed a robust sampling strategy for field measurements. For practical
purposes, we introduced a simplified snow wetness classification, which incorporates information on vertical and horizontal wetness distribution.
4
Methods and data
Most of the data analyzed was collected in winter and
spring 2008/2009 and spring 2009/2010 in Alpine terrain in
Switzerland in the Fribourg and Western Bernese Alps and
Pre-Alps, the region of Davos and in the Lower Engadin
(Fig. 3). The majority of these observations were carried
out in potential avalanche terrain (slopes steeper than 30◦ )
above tree-line. The data-set includes only one day when
observations followed a rain-on-snow event (less than 5 mm
precipitation fell as rain).
4
Observation areas
0
N
50 km
Davos
2009 / 2010, 23 days
Table 2. Spacing and extent of measurement lay-outs as shown
in Fig. 4 (concept according to Blöschl, 1999). x-direction corresponds to horizontal distance, y-direction is distance between consecutive measurements as in Fig. 4a, and z-direction is equivalent
to snow depth measured vertically (in cm, Fig. 4b, c).
mode
Fribourg and West. Bernese
extent
[cm]
spacing
x
[cm]
y
[cm]
z
[cm]
40
300
40
500
20
10
20
50
–
–
5
–
5
5
–
5
Prealps and Alps
2009, 6 days
profile
profile
horizontal
vertical
Lower Engadin
2009, 1 day
Fig. 3. Map showing Switzerland (grey) and the regions (blue),
where measurements were conducted in 2009 and 2010. The number of days is shown, when measurements were carried out. The
majority of measurements were taken in the region surrounding
Davos.
ble 1), the Finnish Snow Fork was used to obtain a more
quantitative measure of liquid water content while the Denoth meter was used to allow a comparison between the two
instruments.
4.1.1
4.1
Field methods
All measurements were carried out in seasonal snow. Our
plan was to sample data that were representative of diverse
topographic, snowpack and wetness conditions. Slope selection was dictated by safety concerns, as many observations
were carried out during periods of wet snow avalanche activity.
In 2008/2009, we focused on measuring diurnal changes
in wetness and comparing results from hand tests with dielectric measurements. Measurements were carried out in
the morning and repeated in the afternoon in the same slope
at a distance of approximately 3–5 m. Wetness content θ was
first measured horizontally (Fig. 4a), these were followed by
the excavation of a snow-pit and measurements made on a
shaded side-wall of the snow-pit (Fig. 4b). Lastly, a manual snow profile including measurements of layer thickness,
hardness, wetness, grain shape and size was recorded according to observational guidelines (WSL, 2008; Fierz et al.,
2009). Snow temperatures were measured using a calibrated,
digital thermometer with an accuracy of ±0.5 ◦ C (Milwaukee Stick Thermometer TH310). Snow stability during this
period is discussed in detail by Techel and Pielmeier (2009).
During the 2009/2010 winter we focused on measuring
changes in snow wetness over several days and the distribution of water within the vicinity of a snow profile recorded
according to Swiss observational guidelines. θ was usually
measured by inserting the Snow Fork vertically into the snow
in cross-sections of up to 5 m width. These measurements
were followed by a manual snow profile and a snowpack stability test. A qualitative estimation of liquid water content
(mWC) was part of the standard observation procedure in
all manual snow-profiles (WSL, 2008; Fierz et al., 2009, TaThe Cryosphere, 5, 1–14, 2011
Sampling design for liquid water content
measurements in a natural snowpack with the
Snow Fork
Liquid water content was measured in one of the following
three ways:
– horizontal – These measurements preceded all slope
profiles. Three contiguous measurements spaced 20 cm
apart (across the slope) with horizontal measurement
intervals of 5 cm into the snowpack were conducted
(Fig. 4a, Table 2).
– profile – These measurements accompanied all slope
profiles. Measurements were undertaken on the sidewall of a manual snow profile. Measurements were
slope- and layer-parallel, and generally less than 50 cm
from horizontal measurements. Vertical measurement
intervals were 5 cm and the distance between the three
measurement rows was 20 cm (Fig. 4b, Table 2).
– vertical – These measurements were conducted to determine the spatial variability of water content. The vertical distance between consecutive measurements was
5 cm, the horizontal distance (across the slope) 50 cm
(Fig. 4c, Table 2).
Applying the concept from Blöschl (1999), measurement
spacing and extent are shown in Table 2. The support, the integrated volume of a measurement device (Blöschl, 1999), is
approximately 47 cm3 for the Snow Fork (Moldestad, 2005).
The idea behind horizontal measurements was to minimize
the possibility of water flow in front of the probe. This strategy, together with the fast measuring speed of the Snow Fork
(it typically took less than 2 min to measure one vertical profile consisting of 15 single measurements) minimized this
5
F. Techel
and C. Pielmeier: Point observations of liquid water content in wet snow
A
5
5
13
C
profile
B
horizontal
50
before profile observations5
50
5
50 manual
50
beside
profile observations
50
5
Fig. 4. Liquid water content measurements: sampling design for horizontal,
inhorizontal
5 cm steps to a depth profile
of 75 cm. B) observations adjacent to manual sno
beside manual
example of a
vertical
beforevertical
cross-section observations,
over 2.5 m distance
C)
measurements
for spatial variability
measurement
extent
and
spacing
of measurements.
4. Liquid
water
content
measurements:sampling
sampling
design for
for horizontal,
horizontal, profile
measurements.
(A)A)
horizontal
measurements
Fig. Fig.
4. Liquid
water
content
measurements:
design
profileand
andvertical
vertical
measurements.
horizontal
measurements
cm steps
a depth
cm.B)(B)
observationsadjacent
adjacenttotomanual
manual snow
snow profile,
profile, vertical
distance
20 cm.
in 5 in
cm5 steps
to atodepth
of of
7575
cm.
observations
verticaldistance
distance5 5cm,
cm,slope-parallel
slope-parallel
distance
20 cm.
(C) vertical
measurements
spatial
variability
observations,measurement
measurement steps
steps 55 cm,
5050
cm.cm.
Refer
alsoalso
to Table
2 for2 for
C) vertical
measurements
for for
spatial
variability
observations,
cm,horizontal
horizontaldistance
distance
Refer
to Tab.
extent
spacing
of measurements.
extent
and and
spacing
of measurements.
LWC [vol. %]
10
0
Cross section 5 m
LWC [vol. %]
10
0
Cross section 5 m
−20
6
−40
4
2
4
200
300
400
a)
500
distance [cm]
12
Fierz et al. 2009
10
●
−80
θSnF [vol.%]
Fig. 5. Contour plot showing cross-section of snow wetness (θ) to
8
a depth of 80 cm over 5 m wide areas across the slope. Measurements were conducted0at horizontal intervals
of
50
cm
(lines)
with
6
100
200
300
a vertical spacing of 5 cm. 4 April 2009, S aspect, 2660 m, 30◦ .
4
θ, measured with the Snow Fork, is corrected by -0.8 vol.% distance
(this [cm]
2
corresponds to the median offset in dry snow).
●
●
●
●
400
●
●
●
●
●
●
●
correctly estimated mWC [%]
0
100
−60
0
8
6
b)
100
2
75
Martinec, 1991b
0
50 500
25
Fig. 5. Contour plot showing cross-section of snow wetness (θ ) to a depth of080 cm over 5 m wide areas across the slope. Measurements
0
were conducted at horizontal intervals of 50 cm (lines) with a vertical spacing of 5 cm. 4 April 2009, S aspect, 2660 m, 30◦ . θ, measured
dry
dry moist wet v. wet
a) is corrected by −0.8 vol. % (this corresponds
b)
moist
wet v. wet
with the Snow Fork,
to the median offset in dry
snow).
Fig. 5. Contour plot showing cross-section of snow wetness (θ) to
mWCthe slope. MeasuremWC
a depth of 80 cm over 5 m wide areas across
potential
problem.were
As shown
in Fig. 5, thereat
washorizontal
usually
rectlyintervals
above and below
the
Denoth
meter
measurements
ments
conducted
of
50
cmdigital
(lines)
withus●
3 density comparing
Fig.
liquid water content
a clear distinction between wet to dry layers indicating that
ing a 7.
100 a)
cmBox-plot
cutter and aestimated
scale.
◦ (θ ,
(mWC)
and waterScontent
measured
with them,
Snow30
Fork
water running
ahead of the
SnF was notof
a problem.
a vertical
spacing
5 cm. 4 April
2009,
aspect,
2660
.
n=318, 4 observers). As comparison the mean for each class is
4.2 Data
on the conversion
et al. (2009).
measured
with
the
Snow
Fork,shown
is based
corrected
by given
-0.8in Fierz
vol.%
(thisb) shows
4.1.2 θ,
Comparison
of Denoth
wetness
meter
and
the frequency that mWC was correctly estimated (light blue bars).
Snow Fork device
Snow
wetness was
measured using the Snow Fork in more
Fordry
comparison,
data by a previous study is shown (black dashed
corresponds
to the median offset in
snow).
than 80 different locations in a variety of measurement de8
1.5
●
● SnF
●
●
● ● ●● ●
4
2
0
●●
●
●● ● ●
●●
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●
●
●
●
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●
1
0.5
●
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●
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●
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●
●
● ● ●●● ● ●●●●● ● ●
●
●● ●●
●
● ● ●
●
●
●
0
−0.5
0
6
100
200 (2–3 300
−2
2
4
Adjacent
measurements
from
the Denoth
meter
measurements) were
compared
with
measurements
from
the
ρ [kg/m3]
θDn [vol.%]
Snow Fork (2–6 measurements). The measurements were always undertaken on a sidewall of snow pits and
a) parallel to the
Fig. 6. Comparison of measured liquid water content using the Delayer stratigraphy. Horizontal and vertical distances between
noth (θDn ) and the Snow
8 Fork (θSnF ) instruments in a) all wetness
measurements was 5 cm. Snow densities were sampled diconditions (n=134) and in b) in dry snow as a function of snow
den●
sity (ρ). Linear regression models are shown in both plots. The
median
off-set of the θSnF for the dry snow data is marked by the● ●
6 m3 , θSnF =0.8 vol.%). ● ● ●● ● ●
dark-blue dot (ρ=250 kg
ol.%]
SnF
●
●
●
line, black squares Martinec, 1991b, n=518, 9 observers). θSnF is
signs andby
wetness
conditions
in natural to
snow.
corrected
-0.8 vol.%
(this corresponds
the median offset in dry
Measurements
in
2008/2009
targeted
small-scale variabilsnow).
ity (within 40 cm), diurnal changes in wetness within the
same slope and compared hand and b)
dielectric estimates of
snow wetness. More than 7000 measurements were taken
1.5
accompanying manual snow profiles using the horizontal
● SnF
Dn
1
l.%]
●
●●
θ [vol.%]
θSnF [vol.%]
6
●●
Dn
●●
● ●
θSnF [vol.%]
depth [cm]
−40
−80
−60
depth [cm]
−20
8
● ●
●
●
●●
●
The Cryosphere, 5,●
●1–14, 2011
● ●
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●
Fig
6
Table 3. Data overview: shown are the number of days (nday ),
locations (nloc ), measurement depths (ndepth ) and single measurements (n) for the various measurement modes (Fig. 4) using the
Snow Fork (SnF), and for the comparison between SnF and Denoth
instrument (Dn).
in the contour plots (as in Fig. 5) and is described in the Rpackage graphics (R, 2009).
5
5.1
mode
horizontal
profile
vertical
Dn-SnF comparison
nday
(nloc )
ndepth
n
24 (60)
26 (63)
10 (26)
8 (11)
>1300
>1200
330
134
>3500
>3500
>3500
251–637
Results
Liquid water content measurements using Snow
Fork and Denoth wetness instrument
In a variety of snow wetness situations, the liquid water content measured with the Snow Fork (θSnF , in vol. %) is generally higher than with the Denoth meter (θDn , Fig. 6a), but the
measurements were strongly correlated:
θSnF ≈ 1.06 × θDn + 1.0
(Fig. 4a) and profile measuring modes (Fig. 4b, Table 3).
These were recorded in more than 2500 different measurement depths or layers.
In spring 2010, the focus was on investigating the wetness variability at spatial scales of up to 5 m. Vertical measurements (Fig. 4c) were carried out in 25 locations. The
comparison of Snow Fork and Denoth instrument is based
on measurements in 134 different snow layers made in profile mode (Table 3).
4.3
Data analysis
The water content measured with the Snow Fork (θSnF )
ranged from 0 to 23.6 vol. %. According to the Snow Fork
manual (Toikka, 2008), measurements with θSnF > 10 vol. %
may not be accurate. However, as these values often corresponded to areas of high snowpack wetness, they were not
excluded from analysis but considered as 10 vol. %. The calculation of the water content using the Denoth instrument
(θDn ) requires a measurement of snow density ρ. We used the
mean of two measurements adjacent to the dielectric measurement.
The relationship between estimated water content (wetness index, mWC) and measured liquid water content (θSnF )
used the conversion shown in Table 1. If θSnF was within
±0.5 vol. % of a class limit, this is considered as an intermediate (half) index class and not considered as a false classification or measurement. Snow wetness was often nonnormally distributed. Therefore, the median and the interquartile range are considered as robust measures of central tendency and data distribution. If several measurements
are available the median θ is used. Linear regression models
were derived for θ and the Pearson coefficient of determination r 2 was calculated. For categorical variables (mWC), the
Spearman correlation rs was used. Data was tested for significant differences using non-parametric tests (Mann Whitney
U-test, Kruskal-Wallis H-test, sign-test, Crawley, 2007), the
level of significance α ≤ 0.05. Linear interpolation is applied
(r 2 = 0.78,p ≤ 0.001)
(1)
Our results are similar to previous studies (Denoth, 1994;
Williams et al., 1999; Frolov and Macharet, 1999). The variability between adjacent measurements was similar regardless which instrument was used.
In dry snow (hand test dry and snow temperature ≤
−0.5 ◦ C), we investigated the effect of snow density (ρ) on
the measured water content. The Snow Fork recorded a
median θSnF = 0.8 vol. % (standard deviation σ = 0.2 vol. %,
nSnF = 487). The Denoth wetness device showed lower values θDn = 0.1 vol. % (σ = 0.17 vol. %, nDn = 281). Measured
water content in dry snow was generally 0.65 vol. % higher
using the Snow Fork than with the Denoth meter (Fig. 6b).
In dry snow poor positive correlations between θ and ρ were
observed:
θSnF = 0.0019ρ + 0.32
(r 2 = 0.28,p < 0.001)
(2)
θDn = 0.0019ρ − 0.33
(r 2 = 0.11,p < 0.05)
(3)
5.2
Influence of sampling design
Liquid water content measurements made before digging a
snow-pit (horizontal mode, Fig. 4a) and following manual
snow profile observations (profile mode, Fig. 4b) were compared in 86 locations.
No significant difference between either mode of measuring the water content was noted in locations with low water
content (median θSnF < 1.3 vol %, dry or barely moist snow).
In moist and wet snow (θSnF ≥ 1.3 vol %), horizontal measurements were wetter than the measurements at the sidewall following snow pit excavation (p = 0.03). However,
the median difference (θ = 0.43 vol. %) is within the range
of measurement uncertainty (±0.5 vol. %, Sihvola and Tiuri,
1986; Fierz and Föhn, 1994).
In our data-set, 2 % of the recorded values were higher
than 10 vol. %. These high values were more frequently
observed when we measured across layer boundaries in an
undisturbed snowpack before digging the snow-pit than in
correctly estimat
θSnF [vo
−40
−60
−80
2
●
●
●
0
50
25
7
0
0
100
a)
200
300
8
dry
500
1.5
distance [cm]
12
●
● SnF
●●
Dn
●●
Fig. 65.
b)
400
●
●
●
●
●
● ●●● ●●● ●
●
●●
●
●●
●●●
●● ●●●
●
●
●●●
●●●●
●
● ●● ● ●
●
●
●●
●
● ●●
●
●●
● ●
●
●
●
●
● ● ●●● ● ●●●●● ● ●
●
●● ●●
●
● ● ●
●
●
●
Contour plot showing
of snow wetness (θ) to
●●
a depth of 80 cm over● 5●● m wide areas across the slope. ●Measure●
●
0.5
4 were conducted
ments
of 50 cm (lines) with
● ● ●● ● at horizontal intervals
◦
●● ● ●
●
a vertical spacing
●of
●
●
● 5 cm. 4 April 2009, S aspect, 2660 m, 30 .
●
●
● ●●●
0
2
● ●the Snow Fork, is corrected
θ, measured
with
by
-0.8
vol.%
(this
● ●● ●
●●●
●
●
●
●●
●
●●
● ● ●●
● ●
●
●
●
●●
●●
●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
corresponds
to
the
●
●
●
●
●
●
●●
●
●
●
●
●● median offset in dry snow).
●
●
●
●
●
●
●●
●
●●
●●
1
cross-section
θ [vol.%]
θSnF [vol.%]
● ●
0
● ●
●
−0.5
−2
8
0
2 a)
6
4
θDn [vol.%]
100
●
● SnF
● ●
1
●●
●●
●
300
ρ [kg/m3]
●●
●
●
●
●
●
● ●●● ●●● ●
●
●●
●
● ●●●
●
●
●
●
●
●
●●
●
●●
● ●●
● ●
● ●● ● ●
●
●
●
●
● ●●
●
●●
● ●
●
●● ●
●
● ● ●●● ● ●●●●● ● ●
●
●● ●●
●
● ● ●
●
●
●
● ●
●
2
θ [vol.%]
θSnF [vol.%]
0
4
dry
v. wet
Fierz et al. 2009
●
100
moist
wet
b)
v. wet
mWC
75
Martinec, 1991b
Fig. 7. 8 a) Box-plot comparing estimated liquid water content
●
(mWC)6and water content
measured with
50 the Snow Fork (θSnF ,
●
●
●
n=318, 4 observers). As comparison the mean for each class is
4
shown based on the conversion given in Fierz
et al. (2009). b) shows
25
●
●
2
the frequency
that mWC was correctly estimated (light blue bars).
●
●
●
●
●
For comparison,
data by a previous study is shown (black dashed
0
0
line, black squares Martinec, 1991b, n=518, 9 observers). θSnF is
corrected by -0.8 vol.% (this corresponds to the median
offset in dry
snow).
mWC
mWC
●
Fig. 6.
6. Comparison
Comparisonof
ofmeasured
measuredliquid
liquidwater
watercontent
contentusing
usingthe
theDeDeFig.
●
●
●
noth
(θ
)
and
the
Snow
Fork
(θ
)
instruments
in
(a)
all
wetness
●
Dn
SnF
noth (θDn
Fork (θSnF ) instruments
in a) all wetness
●
0.5
4 ) and the Snow
● ● ●● ●
conditions(n=134)
(n = 134)
and in (b) in dry snow as a function of snow
conditions
and
●● ● ● in b) in dry snow as a function of snow den●
●
●
● ●
●
density
Linear
regression
models
shown
both
plots.The
The
●
●
● ●
sity
(ρ).2(ρ).
Linear
regression
models
areare
shown
in in
both
plots.
●
0
● ●
● ●● ●
●●●
●
●
●
●●
●
median
off-set
of
the
θ
for
the
dry
snow
data
is
marked
by
the
●
●
●
●
●
●
●
●
median off-set
of
the
●
SnF for the dry snow data is marked by the
● ●
●●
●●
●●
●
●
●● θSnF
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
3 3, θ
●
●●
● kg m
●●
dark-blue
dot
(ρ
=
250
=
0.8
vol.
%).
dark-blue
dot
(ρ=250
kg
m
,
θ
=0.8
vol.%).
SnF
SnF
0
−0.5
−2
wet
a)
mWC
Dn
●●
6
200 b)
1.5
10
●
moist
correctly estimated mWC [%]
0
θSnF [vol.%]
depth [cm]
−20
●
8
ments were conducted at horizontal intervals of 50 cm (lines) with
6
●
●
●
a vertical spacing of 5 cm. 4 April 2009, S aspect, 2660 m, 30◦6 .
4
θ, measured with the Snow Fork, is corrected by -0.8 vol.% (this
4
●
●
2
corresponds
to the
offsetPoint
in dryobservations
snow).
● snow
F. Techel and
C. median
Pielmeier:
of liquid water content in wet
●
6
100
200
300
layer-parallel measurements
taken at the sidewall
of a snow
ρ [kg/m3]
θDn [vol.%]
profile. In horizontal measurements, θ-values greater than
10
vol.
occur more
frequently
in layers
relatively
to
Fig.
6. %
Comparison
of measured
liquid
water content
usingclose
the Dethe
snow
measurements
also
noth
(θDnsurface
) and theand
Snowwhen
Fork neighboring
(θSnF ) instruments
in a) all wetness
showed
high
values.
conditions
(n=134)
and in b) in dry snow as a function of snow density (ρ). Linear regression models are shown in both plots. The
median
off-set of the
θSnFprofile
for theobservations
dry snow data is
by the
5.3
Qualitative
snow
inmarked
wet snow
dark-blue dot (ρ=250 kg m3 , θSnF =0.8 vol.%).
Manually estimated water content (mWC) and liquid water content (θSnF ) were compared in 314 layers. mWC and
θSnF were strongly correlated (rs = 0.73, p ≤ 0.001). Correctly estimated water content mWC decreased with increasing θSnF (Fig. 7a, b). These results are similar to an earlier
study (Martinec, 1991b). Both, Martinec (1991b) and our
results, show that dry layers are normally well described,
while wetter layers are often incorrectly classified (30 % in
our study for wet layers). It is of note, however, that few
measurements were done in snow with θSnF > 8 vol. %. Very
few layers were estimated as being very wet. In these layers,
mWC was always overestimated.
The wetness in layers consisting of coarse melt-freezeparticles (MF, snow class MF, Fierz et al., 2009) is more
frequently falsely estimated (33 % of cases) than in layers
consisting of fine precipitation particles and snow which has
undergone low-temperature gradient metamorphism (LTG,
snow classes PP, DF, RG, 13 %) or coarse medium to
high temperature gradient metamorphosed grains (TG, snow
classes FC, DH, 13 %). No significant correlation was observed between snow hardness and the correct estimation of
the water content. The wetness range in MF is much greater
than in LTG or TG layers (MF: 0–10 vol. %; LTG/TG: 0–
5.5 vol. %). θSnF > 3 vol. % is more frequently observed in
MF (>20 % of cases) and rarely in LTG/TG (<4 % cases).
LTG layers estimated as being wet were almost always overestimated. The error rate in snow estimated as being wet is
Fig.
Box-plot comparing
comparing estimated
estimated liquid
liquid water
water content
content
Fig. 7.
7. (a)
a) Box-plot
(mWC)
and water
water content
content measured
measured with
withthe
theSnow
SnowFork
Fork(θ(θSnF, ,
(mWC) and
SnF
n
= 318,4 4observers).
observers).AsAs
comparison
mean
class
n=318,
comparison
thethe
mean
for for
eacheach
class
is
is
shown
based
on
the
conversion
given
in
Fierz
et
al.
(2009).
shown based on the conversion given in Fierz et al. (2009). b) shows
(b)
shows the that
frequency
that correctly
mWC was
correctly(light
estimated
(light
the frequency
mWC was
estimated
blue bars).
blue
bars). For data
comparison,
data by
a previous
shown
For comparison,
by a previous
study
is shownstudy
(blackisdashed
(black
dashed
line, Martinec,
black squares
Martinec,
n = 518,
9 obline, black
squares
1991b,
n=518, 91991b,
observers).
θSnF
is
servers).
is vol.%
corrected
−0.8 vol. %
(thismedian
corresponds
the
SnF
corrected θby
-0.8
(thisby
corresponds
to the
offset intodry
median
snow). offset in dry snow).
smallest in TG snow (20 %).
Hardness of snow is influenced by the liquid water content. This can be observed by comparing the hardness (hand
hardness test, Fierz et al., 2009) with estimated and measured water content. A negative correlation exists in layers
classified as MF (rs = −0.70 for mWC, rs = −0.43 for θSnF ,
p ≤ 0.001). Not surprisingly, the transition from dry (frozen)
to wet infers a significant hardness decrease. While no clear
trend was observed for TG snow, all layers estimated as being wet (n = 6), or measured as being wet (θSnF > 3 vol. %,
n = 9), had a hand hardness of 1. This was significantly
softer than the dry hand hardness (p ≤ 0.01) in TG snow.
5.4
Temporal changes in snow wetness: morning vs.
afternoon
Snowpack wetness was compared in 33 locations between
morning and afternoon. Generally, snow wetness increased
within the uppermost 10 cm of the snowpack (p < 0.05,
Fig. 8a). This is not surprising, as measurements were carried out when day-time warming was expected. Analyzing each location individually, the median snowpack wetness (excluding the uppermost 10 cm) changed significantly
in only nine cases (p < 0.05). Surprisingly, six of these nine
wetness profiles showed decreasing values (Fig. 8b). These
significant changes always involved a transition from dry or
m ≤ 1.3 vol. %, third quartile
barely moist snow (median θSnF
q3
m ≥ 1.2 vol. %,
θSnF ≤ 1.9 vol. %) to moist or wet snow (θSnF
q3
θSnF ≥ 1.9 vol. %) or vice versa (Fig. 8c), where both the
changes in median and third quartile are significant (p ≤
0.001).
8
14
−20
depth [cm]
represents the interquartile range. b) and c) example of profiles with
significant changes in θSnF during the day. The bold line represents
the morning measurements (median of 3), the shaded area and the
arrows
indicate
the changeofduring
the
day.
F.
Techel
and
C.
Pielmeier:
Point
observations
of liquid
water
content
in wet
snow
F. Techel and C. Pielmeier:
Point
observations
liquid
water
content
in wet
snow
−30
−40
−50
−60
−70
c)
20
40
60
40
60
1
40
60
80
80
●
●
0
−2
2
100
6
4
0
2
∆θSnF [vol.%]
4
6
8
●
−10
●
●
●
●
●
−20
●
●
0.7
θ [vol.%]
a) 18 March − S, 32°, 2350 m
10
●
−10
10
0
2
θSnF [vol.%]
6
4
8
10
θSnF [vol.%]
depth [cm]
100
●
●
0.8
0
lag(x) [cm]
b)
2
−70
0
100
200
300
400
∆θSnF [vol.%]
∆θSnF [vol.%]
1
−10
depth [cm]
−20
0.5
−30
●
●
●
●
3
2
●
●
●
●
●
●
5.5 1.5 Spatial variability in water content distribution
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0
1
The variability
of liquid water content
θSnF was investigated
at horizontal distances of 10–500 cm. The difference be0.5
tween θSnF at measurement spacings of 10 and 20 cm was
significantly
less than at 40 cm or
0
−3 greater (p < 0.001). In
50 % of0 the100cases
variability
was
similar
to, or
the
200 300 400 500
0
6 than
8
10
2
4 less
measurement lag(x)
accuracy
(θ
±
0.5
vol.
%).
However,
even
at
[cm]
Median θSnF [vol.%]
relatively small horizontal spacings of 20 cm, 20 % of the
measurements differed by more than 1 vol. % and 10 % of
Fig. 9. Variability in measured water content as a function of lagthe measurements
differed by
1.8 a)
vol.
%. Generdistance
between measurements
andmore
waterthan
content.
Pearson
corally
the
correlation
between
measurements
at
various
relation coefficient r for all measurement pairs with the same mealag
surement
distances
was moderate
to strong
9a).) beSigdistance
lag(x).
b) Difference
in liquid water
content(Fig.
(∆θSnF
tween
measurement
pairs
at lag distance
(x). Compared
the menificant
differences
between
the median
θSnF inare
each
vertidian
line)existed
and the range
between
and the third
cal (bold
column
in five
of themedian
twenty-five
500 quartile
cm-wide
for
each(plag≤distance
area).inThe
data-set
is split marginally
into meagrids
0.05). (shaded
Variability
θSnF
increased
surements where the median θ of the measurements taken over 5
with greater measurement spacings (Fig. 9b). The variabilm was less than 1.3 vol. % (lower values, red) and more than 1.3
ity (expressed as the interquartile range) as a function to
vol. % (higher values, blue). c) Difference between median water
the median
water
m distance
(θ5 mwithin
) is much
content
and the
first content
and thirdwithin
quartile5(always
measured
5
smaller
at
θ
<
1.3
vol.
%
than
at
θ
≥
1.3
vol.
%
(±0.16
5
m
5
m
m distance, ∆θSnF ). The shaded area highlights the interquartileand ±0.52
vol.
%,25
respectively,
p < 10−16 , Fig. 9b, c).
range
averaged
over
measurements.
Randomly selecting one single measurement showed
strong correlations to the θ5 m (r 2 > 0.83) with the large
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θ [vol.%]
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0
●
0
100 200 300 400 500
0
2
4
6
8
10
0
100
lag(x) [cm]
200
300
400
Median 500
θSnF [vol.%]
●
θ [vol.%] of lagMarch − SW, 33°,
2350 m content
Fig.
Variabilityinc)in19measured
measured
water
contentas
asaafunction
function
Fig.
9.9. Variability
water
of
0
8 lagdistancebetween
betweenmeasurements
measurements and
and water
water content.
content. a)
(a)Pearson
Pearsoncorcordistance
−10
6
relationcoefficient
coefficient rrfor
forall
allmeasurement
measurementpairs
pairswith
withthe
the same
same
lag
−20
relation
lag
−30
distance
lag(x).
(b)
Difference
in
liquid
water
content
(1θ
)
beSnF
distance lag(x). b) Difference in liquid water content (∆θSnF
) be4
−40
tweenmeasurement
measurementpairs
pairsatatlag
lagdistance
distance(x).
(x).Compared
Comparedare
arethe
thememetween
−50
dian(bold
(boldline)
line)and
andthe
therange
rangebetween
betweenmedian
medianand
andthe
thethird
thirdquartile
quartile
2
dian
−60
foreach
eachlag
lagdistance
distance(shaded
(shadedarea).
area). The
Thedata-set
data-setisissplit
splitinto
intomeameafor
−70
0
surements
where
the
median
θ
of
the
measurements
taken
over
surements
where
the median
θ of300 the measurements
taken over5 5m
0
100
200
400
500
less
than
1.31.3
vol.vol.
% (lower
values,
red) and
than 1.3
vol.
%
distance
[cm]
mwas
was
less
than
% (lower
values,
red)more
and more
than
1.3
(higher
values, values,
blue).
(c)
Difference
between
medianmedian
water
content
vol.
%
(higher
blue).
c)
Difference
between
θ [vol.%] water
d) 20 March
− SSE,
27°,
2290 m
0
10
and theand
firstthe
and
third
(always(always
measured
withinwithin
5 m discontent
first
andquartile
third quartile
measured
5
−10
8
tance,
1θSnF
).SnF
The). shaded
area highlights
the interquartile-range
m
distance,
∆θ
The shaded
area highlights
the interquartile−20
averaged
over 25
measurements.
6
range
averaged
over
25 measurements.
−30
−40
4
−50
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
depth [cm]
∆θSnF [vol.%]
∆θSnF [vol.%]
●
x
●
●
x
●
●
distance [cm]
●
●
●
−10
●
●
●
2
●
●
●
●
−50
depth [cm]
correlation coefficient r
●
500
●
distance [cm]
b) 19 March − S, 32°, 2300 m
0
a)
●
0
●
●
1.5
0
●
0
●
●
−70
●
2
c)
3
●
−60
●
0
4
−60
●
−70
100 200 300 400 500
−40
●
We1 have only one explanation for the profiles where overall 0.9
water content decreased during the day: the snowpack
was in the initial part of the melt-phase with spatially and
0.8
temporally
highly variable water infiltration patterns where
both dry and wet regions exist within the snowpack. In such
0.7
situations it is unclear if morning or afternoon measurements
were
0.6a better representation of snowpack wetness at the time.
Considering
just the six observations, where snowpack wet0 100 200 300 400 500
ness decreasedlag(x)
during
the day, almost 20 % of the measure[cm]
ments may be a poor
representation
of snowpack
b)
c) wetness.
−40
−60
6
−30
−50
Fig. 8.
8. a)
(a)Difference
Differencebetween
betweenmorning
morningand
andafternoon
afternoon
liquid
water
Fig.
liquid
water
content(θ(θSnF
measured in
in33
33locations.
locations.Positive
Positivevalues
values
indicate
content
indicate
SnF), measured
an increase
increase in
in θSnF
an
frommorning
morningtotoafternoon.
afternoon.Depth
Depthisisgiven
giveninincm
SnF from
below
snow
surface.
boldline,
line,the
theshaded
shaded
area
cm
below
snow
surface.The
Themedian
median is
is the bold
area
represents
range.
b) and
example
of profiles
with
representsthe
theinterquartile
interquartile
range.
(b) c)
and
(c) example
of profiles
significant
changes
in θSnFinduring
the day.the
The
bold
linebold
represents
with significant
changes
θSnF during
day.
The
line repthe
morning
measurements
(median of
3), the of
shaded
area
and the
resents
the morning
measurements
(median
3), the
shaded
area
arrows
change
theduring
day. the day.
and theindicate
arrows the
indicate
theduring
change
−30
−50
8
0.6
−20
100
0
0.9
0
80
0
depth [cm]
20
a)
depth [cm]
20
depth [cm]
5
b)
5
depth [cm]
depth [cm]
a)
5
2
majority of measurements being within 0.5 vol. % (Table
4,
0
Fig.
10a,
b).
In
more
than
90
%
of
the
cases
the
measure0
100
200
300
400
500
ment was within the same
wetness class (as defined in Tadistance [cm]
ble 1) (Fig. 10c). The median water content observed in three
measurements θm3wetness,
at regular
intervals of 50 cm,southerly
100 cm or
Fig. 10. State of snow
18 - 20 March 2010 on
2 > 0.93). θ
200
cm
was
very
strongly
correlated
to
θ
(r
aspect slopes at similar altitudes in the beginning5ofm the melt phase. m3
was inplots
more
than cross-sections
70 % of the cases
within
0.5(θ)vol.
of θ5 m
Contour
showing
of snow
wetness
to a%
depth
than
98wide
% within
±0.5the
mWC-classes.
The inof and
up toin70more
cm over
5m
areas across
slope. All observations
were observed
shallow 5snowpack
areasisatgenerally
similar elevations
terquartile
rangein within
m sections
very well
(2000
- 2300 m) by
in southerly
aspect
slopes
(SE, S,
measured
represented
the minima
and
maxima
ofSW).
threeθ,values
(95 %
with
the Snow
is correctedFig.
by -0.8
vol.%
(thisthe
corresponds
to the
within
±0.5Fork,
mWC-class,
10d–f
shows
results for
themeasurements
median offset inatdry
snow).
θ-values
greater
than
10
vol.%
are
100 cm distance). With an increase in meashown as 10 vol.%.
surement spacing, the devation between θ5 m and θm3 was
smaller and the minima and maxima of the three measurements tend to fall outside the interquartile range of 5 m crosssections. The recorded snow wetness differed less from θ5 m
if θm3 was used rather than just one measurement (p < 0.05).
−60
−70
Fig. 10.
aspect sl
Contour
of up to
tions we
(2000 - 2
with the
the medi
shown as
a)
b)
5
id water content in wet snow
20
c)
5
5
20
20
15
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●
4
0
−2
2
8
0
2
4
b)
6
c)
0
2
∆θSnF [vol.%]
4
6
8
θ [vol.%]
a) 18 March − S, 32°, 2350 m
0
80
100
100
9
60
200
80
●
6
100
60
200
●
10
−10
8
−20
100100
10
0
2
θSnF [vol.%]
6
4
8
10
θSnF [vol.%]
0
depth [cm]
80
8
●
Frequency
10
θSnF [vol.%]
[vol.%]
●
depth [cm]
a) one measurement
10
depth [cm]
60
Frequency
depth [cm]
F. Techel
and C. Pielmeier:
Point observations40of liquid water content in wet snow
40
40
−30
6
−40
4
−50
0
2
−60
10
8
0
0
[vol.%]
7
6
5
4
3
−4 −2
0
2
4
−2
0
−1
1
2
2
4
6
8
10
−4 −2
θ5m [vol.%]
0
2
4
−2
0
−1
∆θSnF [vol.%]
1
−70
0
0
100
200
300
400
500
distance [cm]
θ [vol.%]
b) 19 March − S, 32°, 2300 m
0
8
−10
−20
6
−30
4
−40
−50
2
2
−60
∆ mWC class
−70
a)
1
Fig. 12.
10. Comparison
Comparison between
between measurement
measurement samples
samplesand
andthe
thememeFig.
dian0.9
of aa 55 m
m cross-section
cross-section at
at aa certain
certain depth
depthθθ5m
.
Upper
row
plots
dian
of
.
Upper
row
plots
5m
●thecomparison
(a–c)show
showthe
comparison
forone
onerandomly
randomlyselected
selectedmeasurement
measurement
(a-c)
for
●
●
●
●
● ● ●
●
to
row
compare
to θθ5m
lower
row plots
plots● (d-f)
(d–f)
comparethe
themedian
medianofofthree
threemeasuremeasure0.8
5 m,, lower
●
ments
distance
ments (θ
(θm3
observed at
at 11 m horizontal
horizontal
distance to
to θθ5m
Scatter●
m3) observed
5 m.. Scatter0.7show
plots
plots
show
the absolute
absolute values
values (a,
(a,d),
d),the
thehistograms
histogramsthe
thedifference
difference
● the
between
andand
e) (e))
and and
the difference
in wetness
between θ-values
θ -values (∆θ,
(1θ , plots
plotsb(b)
the difference
in wetclasses
(∆ mWC)
using using
the international
classification
(Fierz et
al.,
ness0.6classes
(1mWC)
the international
classification
(Fierz
2009,
c,
f).
et al., plots
2009,
plots
(c,
f)).
0 100 200 300 400 500
2
10
●
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●
6
4
8
depth [cm]
[vol.%]
6
Frequency
0
4
Frequency
2
2
Fig. 8. a) Difference
between morning
and afternoon ∆liquid
water
θ5m [vol.%]
∆θSnF [vol.%]
mWC class
three
mesaurements
contentd)(θ
), measured in 33 locations.
Positive values f)indicate
e)
SnF
10
an increase in θSnF from 200
morning to afternoon.200Depth is given in
8
cm below snow surface. The median is the bold line, the shaded area
6
represents the interquartile100range. b) and c) example
of profiles with
100
4
significant changes in θSnF during the day. The bold line represents
2
the morning measurements (median of 3), the shaded area and the
0
0
0
arrows indicate the change during the day.
θm3 [vol.%]
4
0
0
0
100
200
300
400
500
distance [cm]
θ [vol.%]
c) 19 March − SW, 33°, 2350 m
0
8
−10
6
−20
depth [cm]
6
−30
4
−40
−50
2
−60
−70
0
0
100
200
300
400
500
2
lag(x) [cm]
distance [cm]
1
0
●
b)
c)
Table2 4. Deviation between median measured
water content in 5 m
3
cross-sections θ5 m : if one measurement is done θ1 and to median
1.5 content if three measurements are done θ
water
m3 at regular horizontal spacings of 50–200 cm.
0
●
●
θ [vol.%]
d) 20 March − SSE, 27°, 2290 m
10
●
●
−10
●
●
●
●
8
●
●
●
●
6
1dry
deviation
0.5
2upper part wet
from θ5 m
3transitional
θ1
●
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●
● ●
●
● ●
●
●
●
●
●
●
●
●
●
●
0
0
0 March
titudes.
a depth
bservaevations
easured
onds to
ol.% are
4
●
●
θm3 –200 cm
−50
●
2
● ●
●
● ●
●
●
●
−60
●
●
●
2
6
−40
●
●
●
0
−30
●
●
●
●
●
●
●
●
●
●
4
−20
●
●
●
x
●
●
depth [cm]
8
1
∆θSnF [vol.%]
10
∆θSnF [vol.%]
●
[vol.%]
>0.5 vol. %
>1 vol. %
100>2200
vol.300
%
33 %
18 %
4008 %
500
lag(x) [cm]
−3
25 %
10 %
20 %
●
15 %
5%
6 2%
8
−70
2
4
Median θSnF [vol.%]
0
●
10
100
200
300
400
500
distance [cm]
●
Fig.
Variability evolution
in measuredofwater
contentwetness
as a function of lag5.6 9. Temporal
snowpack
distance between measurements and water content. a) Pearson correlation coefficient r for all measurement pairs with the same lag
The evolution of snowpack wetness during spring 2010 for
distance lag(x). b) Difference in liquid water content (∆θSnF ) besoutherly aspect slopes above tree-line is shown in Fig. 11
tween
measurement
pairswetat lag
distance
(x).dryCompared
are
the me-wet
dry mixed
mixed
dry mixed
mixed
wet
dry mixed
and
Fig. wet
12. dryand
The
snowpack
was wet
shallow
with
depth
dian
(bold
line)
the
range between
median
and
the snow
third quartile
often
less
than
1
m
and
dominated
by
soft,
coarse-grained
for each lag distance (shaded area). The data-set is split into meaFig.
13. Wetness
classification
incorporating
verticalwith
and horizontal
faceted
layers
due
the relatively
winter
sustained
surements
where
the to
median
of the dry
measurements
taken
overof5
water content
distribution.
Theθx-direction
shows the percentage
cold
periods.
The
snowpack
was
predominantly
dry
and
cold
m
was
less
than
1.3
vol.
%
(lower
values,
red)
and
more
than
1.3
the snow which has been wetted, where dry/wet is◦ fully dry or fully
with
snow
temperatures
mostly
below
−1
C
on
18
March
vol.
%
(higher
values,
blue).
c)
Difference
between
median
water
wet and mixed consists of both dry and wet regions. The y-direction
infiltration
was (always
slow the
following
day5
(Fig. 11a).
content
andvertical
theWater
firstdistribution
and
third quartile
measured
within
shows
the
of snow
wetness.
Diurnal
changes
m
distance,
∆θ
).
The
shaded
area
highlights
the
interquartiletoo
(Fig.
11b,
c).
On
20
March
two
grids
were
measured:
SnF
occur mostly within the upper-most 10 - 15 cm of the snowpack
range
averaged
over 25
measurements.
the are
first
higher
elevation
showed a relatively dry snowand
notatconsidered
in
this classification.
pack with first weak flow channels (Fig. 11d), while the second, measured later in the afternoon and at lower elevation,
was already moist to the ground (Fig. 12a). Four days later,
on 24 March the snowpack was moist or wet throughout
(Fig. 12b). This first wetting of the snowpack coincided with
0
●
Fig. 11.
10. State
Stateofofsnow
snowwetness,
wetness,
18 - March
20 March
southerly
Fig.
18–20
20102010
in theonbeginning
aspect
slopes
at
similar
altitudes
in
the
beginning
of
the
melt
phase.
of the melt phase. Contour plots showing cross-sections of snow
Contour(θ
plots
of snow
wetness
(θ)across
to a depth
wetness
) to showing
a depth ofcross-sections
up to 70 cm over
5 m wide
areas
the
of up to
cm over 5 m
wide
areas across
the slope.
All areas
observaslope.
All70observations
were
observed
in shallow
snowpack
at
tions were
observed
in shallowm)
snowpack
areasaspect
at similar
elevations
similar
elevations
(2000–2300
in southerly
slopes
(SE, S,
(2000θ-, 2300
m) inwith
southerly
aspect
slopes
(SE, S, SW).
θ, measured
SW).
measured
the Snow
Fork,
is corrected
by −0.8
vol. %
with corresponds
the Snow Fork,
is median
corrected
by -0.8
vol.%
(this
corresponds
(this
to the
offset
in dry
snow).
θ -values
greaterto
the median
offset
in dry snow).
θ-values
greater than 10 vol.% are
than
10 vol. %
are shown
as 10 vol.
%.
shown as 10 vol.%.
wide-spread wet snow avalanching in the region of Davos
(Fig. 3). A cold period with new snow (Fig. 12c) was followed by further melting (Fig. 12d). Subsequent avalanching from southerly aspect start zones was minor and related
to shallow failures of the surface snow.
10
10
●●
●
200
●
●●
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● ●
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●
●
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●
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●
●
●
●
b)
200
c)
−30
6
−40
4
−50
ground
2
−60
ground
−70
0
0
100
200
300
400
distance [cm]
θ [vol.%]
b) 24 March − SSE, 28°, 2330 m
0
10
−10
8
−20
−30
6
−40
4
−50
2
−60
−70
0
0
100
200
300
400
500
distance [cm]
θ [vol.%]
c) 03 April − S, 20°, 2050 m
0
7
−10
6
−20
5
−30
4
−40
3
−50
2
−60
1
−70
0
0
100
200
300
400
500
distance [cm]
θ [vol.%]
d) 17 April − S, 30°, 2210 m
0
−10
10
8
−20
6
−30
4
−40
2
−50
ground
ground
−60
0
0
100
200
300
400
500
distance [cm]
Fig.
Fig.12.
11. Evolution
Evolutionof
ofsnow
snowwetness
wetnessduring
duringmelting
meltingfrom
from20
20March
March
till
Contour
plotsaspect
showing
cross-sections
snow
till 17
17 April
April2010.
2010 on
southerly
slopes
at similar of
altitudes.
wetness
(θ
)
to
a
depth
of
up
to
70
cm
over
5
m
wide
areas
across
the
Contour plots showing cross-sections of snow wetness (θ) to a depth
slope.
All
observations
were
observed
in
shallow
snowpack
areas
at
of up to 70 cm over 5 m wide areas across the slope. All observasimilar elevations (2000–2300 m) in southerly aspect slopes (SE, S,
tions were observed in shallow snowpack areas at similar elevations
SW). θ , measured with the Snow Fork, is corrected by −0.8 vol.%
(2000 - 2300 m) in southerly aspect slopes (SE, S, SW). θ, measured
(this corresponds to the median offset in dry snow). θ -values greater
with the Snow Fork, is corrected by -0.8 vol.% (this corresponds to
than 10 vol.% are shown as 10 vol.%.
the median offset in dry snow). θ-values greater than 10 vol.% are
shown as 10 vol.%.
6
6.1
Discussion
Measurement methods and sampling strategy
Two dielectric instruments were used to measure the liquid
water content of snow: the Finnish Snow Fork and the Denoth meter. As in previous studies, the measured water content correlated well. However, the Snow Fork showed consistently higher values. This offset was most obvious in cold,
dry snow, where the Denoth meter provided more likely estiThe Cryosphere, 5, 1–14, 2011
100
Frequency
Frequency
6
100
●
●
●
●
●
●
●
● ● ●● ●
●
●
●
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● ●●
●
●
●
●
●
●
●●●●
● ●
●●
●
● ●
●
●
●● ●●●
●
●●●
●●
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●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
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●
●
●
●
●
●
●
● ●
●
● ● ●●
●
●●●
●●
●●
●●
●
●
●●
●
●●
●
●
●●●● ●
●●● ●
●
●●
●
●●
●
●
●●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Frequency
depth [cm]
● ●
Frequency
8
θSnF [vol.%]
10
−20
depth [cm]
●
mates4 of water content. We found that one particularly useful
2 of the Snow Fork is its long arm allowing measurefeature
0
0
ments0 at depth without previously
disturbing the
water flow
0
6
8
10
0
0sam2
4
−4
−2
2
4
−2
−1
1
2
by digging a snow-pit. Although the Denoth instrument
θ5m [vol.%]
∆θSnF [vol.%]
∆ mWC class
ples a 7.5
times larger measurement
volume than the Snow
d) three mesaurements
e)
f)
Fork,10we did not note significant effects on the variability of
200
8
adjacent
measurements. 200Our measurement methods
do not
6
allow a conclusive interpretation on the accuracy of the mea100
100
sured4water content.
2
We have not investigated the effect impurities, such as soot
0
0
0
or dust, or acid components have on the permittivity of the
0
6
8 10
0
2
4
−4 −2 0
2
4
−2
−1
1
2
ice. We are aware, however, that such impurities exist and
θ5m [vol.%]
∆θSnF [vol.%]
∆ mWC class
that these also influence permittivity measurements (e.g. Fujita et al., 1992). As our observations were conducted away
Fig. 12. Comparison between measurement samples and the mefrom industrial areas we can only suspect that these effects
dian of a 5 m cross-section at a certain depth θ5m . Upper row plots
will
relatively
small. Infor
theone
Swiss
Alps,selected
visual contamina(a-c)beshow
the comparison
randomly
measurement
tion
of
the
snowpack
is
most
frequently
observed
following
to θ5m , lower row plots (d-f) compare the median of three
measurestrong
airstreams,
when dustdistance
particles
ments southerly
(θm3 ) observed
at 1 m horizontal
to originating
θ5m . Scatterfrom
Sahara
desert values
may color
surface
plots the
show
the absolute
(a, d),the
the snow
histograms
the reddish,
difference
orbetween
when θ-values
snow algae
present.
However,
during the
mea(∆θ,isplots
b and e)
and the difference
in wetness
classes (∆campaign
mWC) using
international
classification
surement
we the
could
neither observe
snow (Fierz
algae et
noral.,
2009,
plots in
c, f).
dust
layers
the snowpack.
The water content distribution or the detection of lateral
flow patterns in a snowpack may be best measured using the
vertical measurement mode (Fig. 4c). It should be considered
that the water content is measured over the length of the sensor (75 mm) and thinner layer specific observations are not
possible. The horizontal or vertical wetness measuring cause
12345onlydrysmall disturbances
in the
snowpack;fullyyet
upper part wet
transitional
wet they are
lowerstill
part wet
destructive to the snow sample. Truly non-destructive methods like the ground penetrating radar looking upward from
the snow-soil interface (GPR, Heilig et al., 2009) might be
better suited to monitor changes in snow wetness in the same
location.
The estimation of liquid water content in the field is difficult, even for experienced observers. When estimating the
wetness of snow layers by hand test, it is important to observe
stratigraphic layers always on a shaded side-wall of a snowpit (WSL, 2008). Snow temperature measurements may be
andryindicator
snow,
small
mixed
wet ofdrydry
mixed
wet although
dry mixed
wet
dryamounts
mixed
wet ofdryliquid
mixed
wet
water have been measured in snow in temperatures below
◦ C (θ < 1 vol. %, this study, also Kattelmann and Dozier,
0Fig.
13. Wetness classification incorporating vertical and horizontal
1999).
An additional
help
inx-direction
the field may
fact thatof
water content
distribution.
The
showsbethethe
percentage
the snow
whichseldom
has been
wetted,8where
is fully
dry(Maror fully
snow
wetness
exceeds
vol. %dry/wet
in natural
snow
wet and
mixedFierz
consists
both dry
and Kattelmann
wet regions. The
1991b;
andofFöhn,
1994;
andy-direction
Dozier,
tinec,
shows the
vertical
distribution
snow wetness.
1999).
Layers
which
containof water
contentsDiurnal
greaterchanges
than
mostly
within therelatively
upper-most
10 (as
- 15for
cminstance
of the snowpack
8occur
vol. %,
are normally
thin
above
and are not
considered
in this
capillary
barriers)
or may
beclassification.
observed in vertical flow paths
(as in Fig. 5). Thus, in freely draining snow, estimated snowpack wetness would be expected to lie mostly in the dry,
moist or wet range.
As with other observed parameters like the hardness of
snow layers, the hand test often provides information on relative differences rather than absolute values. Thus, estimated
snow wetness must not be interpreted strictly according to the
θm3 [vol.%]
θ [vol.%]
a) 20 March − S, 28°, 2190 m
−10
depth [cm]
●
●
0
depth [cm]
a) one measurement
F. Techel and C. Pielmeier: 8Point observations of liquid water content in wet snow
international guidelines but should be understood as an indication only (Fierz and Föhn, 1994). Expanding on a previous
study (Martinec, 1991b), we investigated the effect of layer
characteristics like hardness or grain shape on snow wetness
estimation. It seems that it is more difficult to correctly estimate the wetness in layers consisting of melt-freeze particles. The liquid water content of melt-freeze-crusts undergoing melting is particularly hard to estimate. We assume
that the reason for this is the larger range of snow wetness
(θ0−10 vol. %) and hardness. In particular at low water content when the ice matrix is still frozen for the most part, water cannot be seen using a magnifying lens and the squeeze
test is not suitable in such hard layers. While layer hardness,
grain shape and size may unconsciously influence wetness
estimates, we were unable to quantify these effects. In cases
where it is necessary to quantitatively interpret the estimated
wetness, we propose a rough guide which is based on the
study by Martinec (1991b) (Table 5).
Additionally to methodical aspects, small-scale spatial aspects should be considered when recording snow wetness in
point locations. Due to a lack of data at greater spatial distances than 5 m, we can only assume that a 5 m wide crosssection is a good representation of the snowpack wetness
in a given slope and that the median water content and interquartile range are robust indicators of snow wetness at a
certain snow depth. In particular in a partially wet snowpack, measuring (for instance) three wetness profiles at distances greater than 50 cm will provide a robust estimate of
snow wetness and enhance the quality of wetness recordings
using dielectric methods.
6.2
Monitoring the advancement of the wetting front
The measurements of snowpack wetness during spring 2010
were conducted on slopes of all aspects (illustrated for
southerly aspects in Fig. 11 and 12). This information was
available for the SLF avalanche warning team during the
March melt-phase and was considered as very helpful to assess the advancement of the wetting front. However, for operational purposes it would be of advantage if continuous,
real-time information from potential avalanche slopes would
be available. In some avalanche forecasting operations, for
instance in the maritime climate of Snoqualmie Pass (USA,
Stimberis and Rubin, 2009) and the Milford Road area (New
Zealand, Carran et al., 2002) snow temperature and water
outflow measurements are used operationally to assess the
advancement of the wetting front. Currently, in Switzerland, snow temperature is measured mostly at automatic
weather stations in flat study plots. However, it would be
very useful for avalanche forecasting purposes if such realtime snowpack information would be available from potential avalanche slopes.
The first significant period of water infiltration into
the lower parts of the snowpack coincided with intense
avalanche activity in both the springs of 2009 and 2010. It is
11
Table 5. Proposition of interpretation of manually estimated water
content (hand test as per guidelines WSL, 2008; Fierz et al., 2009).
The proposition from Martinec (1991b) (θMartinec , n = 518, 9 observers) is compared to our study (θ , n = 314, 4 observers). This
interpretation may apply only to experienced observers estimating
the liquid water content. θ , measured with the Snow Fork, is corrected by −0.8 vol. %.
Hand test
(mWC)
Signature
θMartinec
[vol. %]
θ
[vol. %]
Dry
Moist
Wet
Very Wet
Slush
1
2
3
4
5
<0.5
0.5–2
2–4
4–5
>5
<0.5
0.5–2
2–4.5
4.5–6
–
of note that very wet horizontal layers, as in Fig. 5, were absent during the avalanche cycle in spring 2010. We suspect
that this is due to the snowpack structure, which contained
few spatially expanded possible capillary barriers, like fineover-coarse grained layer boundaries. Also, clear patterns of
larger vertical flow paths could not be observed.
6.3
Qualitative description of the wetness of a snowpack
As we have discussed before, the reliable estimation and
measurement of snow wetness in the field is difficult. Temporal changes in the amount and distribution of liquid water in
snow may occur rapidly. Therefore, it might be more practical and sufficient for avalanche forecasting and snow hydrology purposes to use a very general description of the wetness
of a snowpack. Based on our observations, we propose five
wetness profile types, which may be based on estimated or
quantitatively measured water content. It could also be based
simply on the distinction between dry and not-dry snow. We
suggest that surface layers are not included in this classification as these show significant changes during the diurnal
freeze-melt-cycles. The classification incorporates both vertical and horizontal wetness distribution.
The snowpack is dry before the melt-phase (Fig. 13, type
1). With continued input of liquid water through melt or
rain, snowpack wetness increases. Initially, only a part of
the snowpack is wet while some areas remain dry. The wetting may follow a ”step-and-fill-pattern” (Conway and Benedict, 1994) (Fig. 13, type 2). Often, in a snowpack consisting
predominantly of coarse-grained temperature-gradient snow,
preferential flow fingers will penetrate the snowpack relatively quickly and water may temporarily flow laterally along
capillary barriers (Marsh, 1988) (Fig. 13, type 3). At this
stage, first water outflow at the base of the snowpack may
be observed. With continued water infiltration the snowpack will be fully wet and homogenize (Jordan et al., 2008)
(Fig. 13, type 4). Once drainage channels are well established, water outflow will respond quickly to additional input
12
1dry
dry
mixed
2upper part wet
wet
dry
mixed
wet
3transitional
dry
mixed
4fully wet
wet
dry
mixed
5lower part wet
wet
dry
mixed
wet
Fig. 13. Wetness classification incorporating vertical and horizontal
water content distribution. The x-direction shows the percentage of
the snow which has been wetted, where dry/wet is fully dry or fully
wet and mixed consists of both dry and wet regions. The y-direction
shows the vertical distribution of snow wetness (50–100 cm). Diurnal changes occur mostly within the upper-most 10–15 cm of the
snowpack and are not considered in this classification.
of melt-water (Carran et al., 2002). A special case is the
situation that the snowpack begins to refreeze or new snow
falls on an already wet snowpack after a melt-event (Fig. 13,
type 5).
We propose this very simplified classification and are
aware that more variations will exist. However, such a basic
classification can facilitate the description of the snowpack
wetness, in particular for hydrological and avalanche forecasting purposes. One advantage of such a classification is
that the distinction between dry and not dry snow will likely
be more accurate than the estimated wetness classes. Additionally, it describes the spatial wetness distribution which
currently is not included in a snow profile observation. The
spatial wetness distribution may be observed when excavating a snow pit for avalanche forecasting or snow hydrological
purposes.
7
Conclusions
Methodical, spatial and temporal aspects must be considered
when observing snow wetness in the field and interpreting
wetness information.
The method of estimating the liquid water content using
the hand test can not be regarded as a reliable method to
record snow wetness if absolute values are of interest (research question 1). If it is necessary to quantitatively interpret the qualitative wetness recordings, Table 5 may provide
a rough aid for conversion. The hand test is more suited
to record the relative wetness differences within one profile
(Fierz and Föhn, 1994) and the difference between dry and
not dry snow. Our study expands previous research (Martinec, 1991b) by incorporating snow layer properties like
hardness, grain shape and size. Differences in correctly estiThe Cryosphere, 5, 1–14, 2011
mated wetness existed in layers consisting of different grain
shape, however no conclusive results were obtained (research
question 2).
Dielectric measurement methods are generally a more reliable indicator of snow wetness at a given point within the
snowpack. If the measured liquid water content is low (less
than approximately 1.0–1.5 vol. %), the measured wetness
should be interpreted with caution. In such cases, we believe,
that the snow could be either dry or contain small quantities
of liquid water. If the presence of very wet layers is of interest and an instrument like the Snow Fork is available, measurements should be undertaken before excavating a snow
pit. Otherwise, only small differences existed between measurements made on the shaded side-wall of a snow-pit or in a
previously undisturbed snowpack (research question 3). Vertical measurements of snow wetness using the Snow Fork are
an efficient way to record spatial distribution of snow wetness.
As for dry snow observations, site selection is important to
observe representative information. To obtain representative
wetness information, slope aspect and inclination, elevation
and the distance to rocky areas should be considered. Still,
the small-scale spatially heterogeneous distribution of dry
and wet areas within the snowpack may lead to unrepresentative results. Our observations showed that approximately every fifth to tenth wetness profile was a poor representation of
the surrounding snow wetness (research question 4). Therefore we propose to observe several measurements at horizontal distances greater than 50 cm. Because our measurement
extent was limited to 5 m, we can not give conclusive results
on spatial correlation of liquid water content. As far as we
are aware, this is the first study quantifying the variability of
liquid water distribution in a snowpack at scales up to 5 m.
Based on our observations on spatial variability in snow
wetness, we propose a snowpack-wetness classification. We
see this as a first step towards the development of a wet
snow classification scheme as exists for the assessment of
dry snow profiles (Schweizer and Wiesinger, 2001). Incorporating additional snowpack properties like the state of wet
snow metamorphism (grain shape), snow layering and snow
temperature may improve the value of such a classification
for avalanche forecasting purposes and could also assist in
flood forecasting during snow melt periods.
Point observations in wet snow provide important information on the state of wetting of a snowpack, which influences wet snow stability. However, as water infiltration
in snow is a fast process, timing of observations is critical.
Therefore, for operational avalanche forecasting purposes it
is necessary to continuously observe parameters like snowpack temperatures, liquid water content or water outflow not
only in flat study plots, but also in potential avalanche slopes.
This is operationally done in some forecasting operations (for
instance in the US or New Zealand).
To our knowledge, there is currently no reliable, economical and practical alternative available to the hand test to
measure snow wetness in the field. Therefore, we propose
that future research should investigate possibilities of developing a practical, hand-held instrument to quantitatively
measure the liquid water content in snow (similar to a digital thermometer). Further, it would be advantageous to know
the distribution of the liquid water content at the slope-scale
to allow better interpretations of point observations in wet
snow.
Acknowledgements. We greatly thank Adrian Räz, who assisted in
the field. Christoph Mitterer shared important field information.
He, as well as Thomas Stucki and Stephan Harvey provided
valuable comments which helped improve the manuscript. We
thank the editor Andrew Klein, one anonymous reviewer and
Howard Conway for their very helpful feedback on this paper.
Edited by: A. Klein
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Point observations of liquid water content in wet snow

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