Ice layers may form deep in the snowpack due to preferential water
flow, with impacts on the snowpack mechanical, hydrological and
thermodynamical properties. This detailed study at a high-altitude
alpine site aims to monitor their formation and evolution thanks to
the combined use of a comprehensive observation dataset at a daily
frequency and state-of-the-art snow-cover modeling with improved ice
formation representation. In particular, daily SnowMicroPen
penetration resistance profiles enabled us to better identify ice layer
temporal and spatial heterogeneity when associated with traditional
snowpack profiles and measurements, while upward-looking ground
penetrating radar measurements enabled us to detect the water front and
better describe the snowpack wetting when associated with lysimeter
runoff measurements. A new ice reservoir was implemented in the
one-dimensional SNOWPACK model, which enabled us to successfully
represent the formation of some ice layers when using Richards
equation and preferential flow domain parameterization during winter
2017. The simulation of unobserved melt-freeze crusts was also
reduced. These improved results were confirmed over 17
winters. Detailed snowpack simulations with snow microstructure
representation associated with a high-resolution comprehensive
observation dataset were shown to be relevant for studying and modeling
such complex phenomena despite limitations inherent to one-dimensional modeling.
Introduction
The presence of ice layers in a snowpack may impact its mechanical,
hydrological and thermodynamical properties. Monitoring the formation
and evolution of ice layers is thus crucial in many research
fields. Because of their low permeability , ice
layers may increase the liquid water storage of the snowpack, which
can substantially affect the snowpack runoff . In contrast, near-surface ice layers in Greenland were shown to prevent
access to deeper firn layers, thus reducing meltwater storage in the
firn and enhancing ice sheet mass loss . The
stability of a mountainous snowpack may also be affected, with a
possible increased faceting of the microstructure close to ice or
crusts . Moreover,
retrieval algorithms for the water equivalent of snow cover and snow depth
from passive microwave emissions are sensitive to the presence of ice
layers . Better knowledge about their
formation could help the assimilation of such data in detailed
snow-cover models .
Ice forms in the snowpack can have different origins
. They can form either at the surface because of
freezing rain or a firnspiegel formation process due
to radiative cooling or within the
snowpack through the percolation of rain or meltwater reaching
subfreezing snow . The present study focuses on the
latter case. As opposed to matrix water flow leading to a homogeneous
progression of the wetting front, preferential water flow occurs
through flow fingering e.g., transporting
liquid water to deeper regions of the snowpack where the cold content
is sufficient to refreeze it e.g.,. Preferential
flow occurs in the snowpack due to its microstructural heterogeneity
(density, grain size and shape) at layer transitions
, which may trigger flow fingering or form
hydraulic barriers (i.e., capillary or permeability barriers) where
water ponds and may subsequently refreeze. Hydraulic barriers may also
divert water flow and lead to lateral flow along slopes
. Knowledge about water percolation in
snow, and particularly preferential flow, has recently greatly
expanded in terms of process understanding and numerical
simulation. Using dye tracer and liquid water content (LWC)
measurements, observed preferential flow and water
ponding at capillary barriers for various layer transition
characteristics and water input. X-ray microtomography was also used
to observe wet-snow metamorphism under preferential flow
. Magnetic resonance imaging observations of finger
flow and lateral flow emphasized that even small differences in snow
properties may form capillary barriers in dry snow
. At larger scales, the effect of
preferential flow on the snowpack runoff was assessed through
measurements of the heterogeneity of water discharge
or under rain-on-snow conditions
. The new insights from measurement
campaigns enabled the development of multidimensional models that
account for preferential flow in the snowpack
. They
also enabled progress in the representation of water transport in
one-dimensional (1-D) models. The Richards equation was implemented in
detailed snow-cover models like SNOWPACK and Crocus
as an improvement over the more simplistic
bucket parameterization, enabling a more realistic representation of
water transport in snow with respect to snow microstructure. Based on
recent studies relating preferential flow to snow properties
with analogies to
preferential flow in soils , developed an original 1-D parameterization of preferential flow in
SNOWPACK through a dual-domain implementation separating matrix flow
and preferential flow, both of which are solved with the Richards equation.
The representation of ice layer formation in snow-cover models remains
very challenging because it depends on an accurate description of the
snow microstructure and water transport. Currently, most snow-cover
models do not take into account the processes of ice layer
formation. recently modeled ice formation on the
snowpack surface due to freezing precipitation in the detailed
snow-cover model Crocus. included for the first
time in a detailed snow-cover model the process of deep ice layer
formation due to preferential water flow. Using the dual-domain
implementation for water percolation in a subfreezing snowpack, they
investigated ice layer formation at an alpine site, comparing manual
snow profiles recorded every 2 weeks over 16 winter seasons with
SNOWPACK simulations. Nevertheless, many research gaps remain open
about deep ice layer formation in a snowpack. In particular, the
present study aims at answering the following questions.
Can daily SnowMicroPen (SMP) measurements improve the monitoring of ice layers in natural snowpacks over traditional snowpack profiling?
How well can 1-D snow-cover models represent a multidimensional process like ice formation due to preferential flow?
Can spatially discontinuous ice lenses be parameterized in a 1-D snow-cover model?
Can we provide useful information on ice layer origin and evolution in alpine snowpacks for various applications based on observations and simulations?
To address these research questions, we bring several novelties in a
detailed study pushing forward the investigation of
. First, a comprehensive observation dataset was
gathered at the same research site in order to better determine the
evolution of the snowpack and identify the formation of deep ice
layers in natural conditions at a high-altitude alpine site. The
originality of this dataset comes from the opportunity to monitor ice
formation in natural alpine conditions during a whole winter season at
daily resolution even though the present study does not include
detailed observations of preferential water flow paths. This dataset
is then used for a detailed assessment of the preferential flow
representation in SNOWPACK, bringing complementary insights to
and . As a result, we introduce a
new parameterization to improve the simulation of discontinuous deep
ice formation in the SNOWPACK model.
The paper is organized as follows. Section
describes the study site, the observation dataset, the snowpack
simulation configuration and new methods. Section
details the results with insights on water percolation and ice layer
formation from both the observation dataset and the simulations. Their
benefits and limitations are discussed in Sect. .
Data and methodsObservation dataset
The site of study is the Weissfluhjoch (WFJ) measurement site, a
research field dedicated to snowpack investigations which is located at an
elevation of 2536 m above sea level by Davos in the eastern Swiss
Alps . For comparison to simulations, we use a
comprehensive observation dataset collected during winter
2017. Figure indicates the location of the
measurements in the research field. Traditional snowpack profiles were
performed during the entire season every week (or every 2 weeks at
the beginning and the end of the season) along three corridors, moving
continuously along each corridor and turning into the next one once
the end of the previous one had been reached. These profiles were carried
out following the recommendations of , and they gather
observations of the grain shape, grain size, layer thickness, hand
hardness index and wetness through visual and manual assessments, as
well as measurements of snow depth (HS), water equivalent of snow
cover (SWE), snow density, snow temperature, snow hardness (Swiss
ramsonde) and liquid water content with a Denoth device
. Continuous melt-freeze crusts (MFcrs) and ice
layers (IFils) were identified in the snow profile, while ice lenses
were most often noted additionally. There was no measurement of ice
layer density as such measurements in natural conditions remain very
challenging e.g.,. Snowpack runoff measurements
were provided by a 5 m2 lysimeter at 10 min
temporal resolution . Finally, thermistors at a
fixed vertical interval of 20 cm provided half-hourly snow
temperature profiles of the snowpack .
Overview of the WFJ research field with contour lines in orange and labels indicating the location of the measurements used in this study.
An upward-looking ground penetrating radar (upGPR) was also installed
at the site . The dual frequency GPR from IDS
(Ingegneria Dei Sistemi, Italy) conducted measurements every
30 min and with two different frequencies, 600 MHz and
1.6 GHz. Every measurement contained 1800 traces with 1024
samples. In order to remove system ringing, a hoisting device was
installed by to move the GPR antennas vertically
during a measurement cycle. When transmitting electromagnetic waves
into snow, discontinuities result in reflections, refractions and
diffractions. The amount of energy reflected at the discontinuity is
proportional to the relative change across the discontinuity
. The percolated water changes the internal
properties of the snow. The boundary between wet and dry snow (called
water front hereafter) appears as a distinct reflector and can then be
determined by semiautomatic picking similar to the algorithm
developed by for snow surface picking. The picked
two-way travel times of the water front are multiplied by the wave
propagation velocity of 0.23 mns-1, which is a typical
value for snow . During winter 2017, the
water front could be derived until a technical malfunction on 9 April
prevented further analysis. Due to the 45∘ angle of beam spread of the upGPR, the footprint in the snowpack where the water front is derived enables us to identify a homogeneous state but not
local flow fingers.
In addition, daily measurements of the penetration resistance were
performed with a SnowMicroPen in the
context of the RHOSSA field campaign launched in
winter 2016. The SMP has a tip surface of 19.6 mm2. Every
day during the winter season, five to seven SMP measurements were
performed moving forward by steps of about 40 cm along the
corridors, with a 15 cm spacing perpendicular to the direction
of the corridor providing indications of the local spatial
heterogeneity of potential ice forms. A limited number of gaps in the
daily measurements have to be noted, in particular from 25 to
27 March and 7 to 8 April in the wetting period.
For longer-term observations of ice layers, we gathered traditional
snowpack profiles performed every 2 weeks during the winter seasons
1999/2000 to 2015/2016.
The comprehensive observation dataset of winter 2017 at WFJ is
publicly available (see Code and Data Availability section).
Snowpack simulationsSimulation configuration
SNOWPACK simulations were performed for the WFJ study site in winter
2017. They were initialized with a traditional profile recorded on
3 January 2017 to provide a realistic base of the snowpack with a
snow depth of 47 cm. The initialization date were chosen to be early
enough to assess the model's ability to simulate the microstructure
evolution, as well as water percolation, but to avoid early season
modeling errors, for example, the formation of unobserved basal
melt-freeze layers. The simulations were driven by optimal in situ
meteorological measurements (Fig. ) of air temperature
and humidity (ventilated sensors), near-surface wind, and solar and
longwave irradiance . The snowfall input
is driven by measured snow depth increments
enabling direct comparisons to the measurements and outcomes of
. In addition, for air temperatures above
1.2 ∘C, undercatch-corrected precipitation gauge
measurements are considered to be rain.
Three water transport schemes implemented in SNOWPACK were
evaluated. First, the bucket approach (BA) is a common method used in
snow-cover models e.g.,, which assumes
that water is transported to the next downward layer when the liquid
water content exceeds the water holding capacity of a given layer
depending on the ice volumetric content of
snow;. Second, the Richards equation (RE) was
implemented in SNOWPACK by to account for capillary
effects. These effects are modeled taking into account the water
retention curve and the hydraulic
conductivity of snow
. Third, a dual-domain
approach parameterizing preferential flow (PF) and using the Richards equation
(RE/PF) was recently developed. Water exchanges between the matrix
domain and the preferential flow domain are determined according to
the water entry pressure head in the matrix layers and the saturation
in the preferential flow domain; this implementation is described in
details in . The two tuning parameters of this
scheme were chosen here according to : the
threshold in saturation of the preferential flow domain,
Θth=0.08, and the parameter related to the number of
flow paths per square meter, N=0. In particular, N=0 implies no
refreezing of the preferential flow water
. Similar to , SNOWPACK
simulations were carried out at high vertical resolution with a layer-merging threshold of 0.25 cm and new snow layer initialization
of 0.5 cm. A high resolution is necessary to permit the
formation of very thin high density layers.
Implementation of ice reservoir
A new parameterization of ice layer formation due to preferential flow
was implemented as a complement to the RE/PF scheme. It is summarized
in Fig. . In the RE/PF scheme, when the
saturation in the preferential flow domain exceeds the threshold
Θth, water flows back to the matrix domain. First, a
volume of water corresponding to the available freezing capacity is
instantly frozen and added uniformly to the ice content of the matrix
domain. Ice lenses, in contrast, may only form locally at the base
of the flow fingers. If the threshold is still exceeded, then
saturations in both domains are equalized .
Scheme of the ice reservoir parameterization in SNOWPACK. Blue represents liquid water, cyan represents ice, and red arrows represent water or ice transfers to another domain. Steps 1 to 6 are described in the text.
To better reproduce the formation of continuous ice layers from
discontinuous and growing ice lenses, we developed an ice reservoir
parameterization. The water, which is normally transferred from the preferential
flow domain to the matrix domain that freezes instantly, is stored in
an ice reservoir (step 4 in Fig. ) instead
of being added to the ice volumetric content of the matrix. The ice
reservoir is representative of the volumetric content of ice lenses
(i.e., spatially discontinuous ice) in a given layer. The transferred
water that does not freeze goes in the matrix domain, i.e., is spread
homogeneously (step 5 in Fig. ).
Furthermore, the saturation threshold in the PF domain
was chosen as a simple solution to the inability of the
Richards equation to model the saturation overshoot present in the tip
of flow fingers . This simple parameterization can
lead to inconsistencies due to the vertical discretization of the
simulated snowpack. After water has been transferred to the matrix at
the layer corresponding to the finger tip (i.e., where the saturation
threshold was exceeded), the highest saturation is then more
likely reached at the layer above where no water transfer occurred because
water percolation from this layer to the finger tip layer only occurs
at the next time step. Because of that, the water transfer from the PF
domain to the matrix domain may spread over too many layers instead of
being concentrated in the lowest layer (i.e., the tip of the flow
finger). To overcome this issue, the ice reservoir was cumulated in
the lowest layer. When the ice volumetric content of the cumulated ice
reservoir plus the ice volumetric content and water volumetric
content of the associated matrix layer exceed the corresponding ice
density threshold of 700 kgm-3 in SNOWPACK, there is
enough ice to consider it horizontally homogeneous; the ice content
of the cumulated ice reservoir is then transferred to the associated
matrix layer (step 6 in Fig. ). As long as
it is kept in the ice reservoir, the forming ice has no effect on the
water transport in the matrix domain that still follows the RE/PF
scheme (, ). Furthermore, we neglect any impact
the ice reservoir, which is interpreted as ice lenses, may have on
hydraulic properties (e.g., local hydraulic barrier
effect). Simulations with the ice reservoir parameterization are
called RE/PF/IceR hereafter.
The implementation of the ice reservoir parameterization in the
SNOWPACK source code is publicly available (see Code and Data
Availability section).
ResultsInsights from the observation datasetOverview of the winter season
At WFJ, winter 2016/2017 started with a shallow snowpack of
approximately 30 cm at the beginning of November, followed by
a extended period of calm weather, forming a base layer made of depth
hoar crystals . This layer was covered by new snow
at the end of December, and several small snow storms led to a
maximum snow depth of 205 cm on 10 March, i.e., slightly lower
than the average maximum snow depth. The snowpack had entirely melted
on 14 June. Overall, this winter was characterized by a lower snow depth
than the long-term averages in the region.
Figure represents the evolution of grain types
within the snowpack as observed in traditional snow profiles. Two
layers of particular interest are tracked during the whole
winter. Layer 1 corresponds to surface hoar formed at the end of
January. This layer is continuously identified as buried surface hoar
(as primary or secondary grain) until the end of March with a grain
size substantially larger than the layer above (consisting of rounded
grains or faceted crystals), thus constituting a capillary barrier
with a classical fine-over-coarse structure
e.g.,. Ice is observed above this barrier from
28 March either as a homogeneous layer or as ice lenses mixed with
melt forms (red in Fig. ). Thicknesses between 0.5
and 1 cm were reported. The presence of nearby slopes (NNW of
the snow profile corridors in Fig. ) may suggest a water
input through lateral flow along the capillary barrier. Simulations
(not taking into account lateral flow) may provide complementary
insights to determine whether vertical preferential flow was
sufficient to form an ice layer. Layer 2 corresponds to surface hoar
appearing in mid-February and forming a capillary barrier once
buried. An ice layer is observed at that level in most profiles after
28 March.
Visual observations of grain shapes at WFJ during winter 2017. Colors, symbols and codes are defined following the grain shape classification of , fuchsia crosses represent surface hoar as secondary grain shape, and cyan crosses represent ice lenses. Rectangles highlight layers 1 and 2 with dashed lines before ice formation and solid lines afterwards.
Water percolation and snowpack runoff
Figure represents lysimeter measurements of the
snowpack runoff, together with the height of the water front estimated
from the upGPR measurements, from 1 March to 15 April, i.e., the
transition period from dry to isothermal snowpack. Shown underneath
are the snow temperature measurements in the lowest meter of the
snowpack at fixed intervals of 20 cm. The height of the water
front could only be derived until 8 April due to technical
issues. Before 30 March (first dashed line), no snowpack runoff was
observed, the water front was always higher than 1 m, and
temperatures in the lowest first meter of the snowpack were all below
0 ∘C. Between 30 March and 9 April (second dashed
line), the water front remained high (mostly higher than 1 m), and
a small amount of snowpack runoff was observed (less than
3 kgm-2d-1). Snow temperatures gradually increased
to reach 0 ∘C by the end of the period in the lowest
meter of the snowpack. From 9 April, all temperatures in the lowest
meter reached 0 ∘C, and snowpack runoff
increased. Although after 9 April no more water front estimates were
available from the upGPR data, it reached the lowest value
(85 cm) on 8 April. The snowpack runoff is yet low compared to
the more significant runoff starting in mid-May, as shown in
Fig. ; the first 20 kgm-2 of total
snowpack runoff was reached on 10 April, while the first day with a
daily snowpack runoff higher than 10 kgm-2d-1 was
13 May.
(a) Evolution of the snow depth (black line), height of the water front (blue line) and daily snowpack runoff (blue bars) from 1 March to
15 April 2017 at WFJ. (b) Measured snow temperatures at different heights
above the ground are from the same period and same location. Dashed lines indicate 30 March
and 9 April.
Total snowpack runoff from 15 March to 15 June 2017 at WFJ. Dashed lines indicate 30 March and 9 April.
These measurements give insights in the timing of water percolation in
the snowpack. Before 9 April, the bottom of the snowpack was cold and
dry, while the water front was still mostly above 100 cm. The
low snowpack runoff values were thus likely due to preferential flow
paths reaching the ground. After 9 April, the lowest meter of the
snowpack reached an isothermal state at 0 ∘C, and
snowpack runoff increased markedly.
Capillary barriers and ice layers
To study the formation of ice through preferential flow in subfreezing
snow, we focus on the lowest meter of the snowpack. In this part, the
manual snow profiles enable us to identify three main capillary barriers
(Fig. ): the two layers of buried surface hoar where
ice forms at the end of March (defined as layers 1 and 2 previously)
and the top of the depth hoar base layer, where higher water content
is observed in April and May. Layers 1 and 2 are marked by grain size
heterogeneity with the overlying layers: on 28 February, 1 mm
over 2.5 mm for layer 1 and 0.5 mm over 2 mm for
layer 2.
Daily SMP measurements of penetration resistance (mm, averaged values) at WFJ from 1 February to 19 April with one representative profile per day. Values higher than 2 N are shown with the same color. Rectangles highlight the approximate location of layers 1 and 2 with dashed lines before ice formation and solid lines afterwards.
Daily SMP measurements enable us to more clearly identify the temporal
and spatial variability of ice formation. Figure
represents the evolution of penetration resistance from 1 February to
19 April with a scale from 0 to 2 N to highlight variations
in dry snow. The deep MFcr is visible in the middle of a low-resistance depth hoar layer at approximately 20 cm. In
February and March, the highest values in the middle of the snowpack
correspond to dense layers of faceted crystals
(Fig. ). In March, the buried surface hoar of layer
1 is marked by a lower penetration resistance than the surrounding
faceted crystals. Layer 2 exhibits less heterogeneity with surrounding
layers. Overall, the penetration resistance increases substantially
from the end of March onwards with the progressive wetting, particularly at
the top of the snowpack where many melt-freeze crusts
form. Figure a represents penetration resistances higher
than thresholds of 5 and 10 N below 100 cm and includes
all daily SMP measurements. These values were chosen after a comparison
of matching traditional and SMP profiles to better highlight crusts
and ice forms. Except the deep MFcr mostly visible until the end of
March, two high resistance layers can be identified from 29 March;
they match the visual identification of ice layers 1 and 2
(Fig. b). They can be tracked until mid-April but are
not present in all SMP profiles.
(a) Height of SMP penetration resistances between 5 and 10 N (red) and higher than 10 N (cyan) below 1 m from 1 February to 19 April at WFJ. (b) Visual observations of melt-freeze crusts (MFcr), ice layers (IFil) and ice lenses at the same period and location. Daily manual measurements of snow depth in solid black lines. Rectangles highlight layers 1 and 2.
The penetration resistance measured at these two layers was tracked in
the SMP profiles. To identify these layers, all SMP profiles were
superimposed on the traditional profile observed at the closest
date. Penetration resistances were associated with observed snow layers
given their grain type and hand hardness index. To identify consistent
patterns, the SMP profiles of a given day were also compared among
each other and with those of the previous and next days. The value of
SMP penetration resistance in these layers was then visually
picked. Figure shows an example of this procedure for the
two SMP profiles of 4 April 2017.
Two samples of SMP profiles of 4 April 2017 (black line) superimposed on the traditional snow profile of the same date. Colors referring to the grain shape and hand hardness index are defined accordingly to the classification of . Cyan crosses indicate ice lenses. Dashed rectangles highlight layers 1 and 2. Black crosses indicate the penetration resistance picked for each layer.
The evolution of the penetration resistance of the two tracked layers
is plotted in Fig. from 14 March to 19 April. For
layer 1, the penetration resistance remains very low (less than
1 N) until 24 March. It corresponds to the observed layer of
buried surface hoar. On 29 March, all seven SMP measurements show
penetration resistances higher than 10 N, indicating the
continuous presence of ice. Afterwards, values alternate between low
resistance (less than 5 N) and very high resistance (more than
10 N), as visible in Fig. on 4 April. This is
consistent with the visual observations reporting a layer in which
pure ice and melt forms are both observed. These observations suggest
that water ponding at the capillary barrier did not freeze everywhere
on the study plot where we performed these measurements. For layer 2,
very low penetration resistances are also measured until 24 March,
corresponding to the observed buried surface hoar. After 29 March,
values increase (mostly higher than 5 N, often more than
10 N), indicating the formation of ice. After 9 April, no more
high resistances are measured but rather low resistances corresponding to
a wet layer of melt forms. The evolution of penetration resistances
for layer 2 show more temporal consistency than layer 1, suggesting
that the ice layer disappears totally after 9 April.
Evolution of the penetration resistance of (a) layer 1 and (b) layer 2, manually tracked in the SMP profiles, from 14 March to 19 April. Thresholds of 5 and 10 N are indicated in red and cyan, respectively, accordingly to Fig. .
Assessment of snowpack simulationsAssessment with different water transport schemes
Simulations of winter 2017 were first performed with the three water
transport schemes existing in SNOWPACK (BA, RE and RE/PF). For the
RE/PF simulation, Fig. shows the grain shape and liquid
water content in the PF domain for the month of February. Buried
surface hoar of layer 1 (represented in fuchsia and at approximately
80 cm) is well simulated at the beginning of February. The
capillary barrier of layer 2 is also well initiated in mid-February on
the surface. However, a thick melt-freeze crust forms at layer 1 on
10 February (represented in hatched red, Fig. a). It is
associated with some melting close to the surface leading to
preferential water flow refreezing at the capillary barrier of layer 1
(Fig. b). The water transferred for refreezing in the
matrix domain is spread homogeneously, which forms a crust with a
density of approximately 320 kgm-3. The transfer spreads
vertically due to the issues mentioned in
Sect. . This thick melt-freeze crust was not
observed in the manual profiles nor the SMP measurements. Other
melt-freeze crusts form close to the surface from mid-February. They
were observed (Fig. ) but were thinner than the
simulated ones. A little surface melting is simulated on 1 February
and leads to preferential flow (Fig. ). Contrary to later
simulated melting in mid-February, it was not observed; the measured
snow surface temperature remained slightly under melting point. This
simulation error is likely due to excessive surface turbulent flux
inputs. New simulations were run without this melt water input with no
effect on the later snowpack structure due to the limited melting
amount.
SNOWPACK simulation RE/PF in February 2017 at WFJ: (a) grain shape according to the classification of , and (b) liquid water content in the PF domain. Rectangles highlight layers 1 and 2.
Figure shows the grain shape and the liquid water
content in the matrix domain from 15 March to 15 April, i.e., the
period of transition from dry to ripe snowpack when ice layers formed
(Sect. ). No ice layer forms except at the
snowpack basis, which is probably due to a boundary effect at the
interface between snow and soil. The thick melt-freeze crust is still
present at layer 1, and thus no ice layer forms
(Fig. a). However, a higher water retention is
simulated (Fig. b). Due to the excessive formation of
melt-freeze crusts, the simulated snow microstructure at layer 2 does
not reproduce the observed snow microstructure and the capillary
barrier, and thus no ice layer forms. Matrix flow reaches the ground, and
the snowpack is entirely isothermal on 31 March
(Fig. b), i.e., 9 d before the observations
(Sect. ). On 31 March, the water front was
actually observed at around 100 cm
(Fig. ), hence a premature simulated water front
progression.
SNOWPACK simulation RE/PF from 15 March to 15 April 2017 at WFJ: (a) grain shape according to the classification of , and (b) liquid water content in the matrix domain. Rectangles highlight layers 1 and 2.
A sensitivity study on the parameters of the dual-domain approach
(Θth and N) was performed but could not resolve
the issues about ice formation highlighted here. Simulations were also
analyzed in terms of snowpack runoff confirming earlier findings
. The BA scheme underestimates the
snowpack runoff, the RE scheme overestimates it, and the addition of
preferential flow increases this overestimation (not shown). The onset
of snowpack runoff is delayed compared to observations for BA and RE
because preferential flow is not simulated, but RE/PF simulations show
the onset of snowpack runoff that is too early.
Assessment with ice reservoir parameterization
Simulations were also performed with the ice reservoir
parameterization (RE/PF/IceR) to assess its ability to improve the
simulation of ice layer formation compared to the previous
simulations. Figure shows the grain shape and liquid
water content in the PF domain for the month of February. Similar to
the RE/PF simulation, the fine-over-coarse grain structure leads to
water ponding in the PF domain at layer 1. Contrary to the RE/PF
simulation, no melt-freeze crust forms at layer 1
(Fig. a); the water leaving the PF domain and refreezing
has a quantity that is too low to be considered representative of the mean
state of the snowpack in this layer. It is thus stored in the ice
reservoir. The fine-over-coarse grain transition forming a capillary
barrier is preserved. Note that the liquid water content in the PF domain
(Fig. b) is almost not modified compared to the RE/PF
simulation (Fig. b). Liquid water transport is similar,
in particular the vertical spreading of water ponding, but ice in
the reservoir is concentrated at the capillary barrier. The ice kept
in the reservoir has indeed no effect on water transport and
microstructural changes in the matrix. At the end of February, less
melt-freeze crusts are formed than in the RE/PF simulation even
though the ones surrounding layer 2 are also thicker than observed.
SNOWPACK simulation RE/PF/IceR in February 2017 at WFJ: (a) grain shape according to the classification of , and (b) liquid water content in the PF domain. Rectangles highlight layers 1 and 2.
Figure shows the grain shape and the liquid water
content in the matrix domain from 15 March to 15 April. A basal ice
layer forms similarly to the RE/PF simulation. Contrary to the RE/PF
simulation, the capillary barrier of layer 1 is still present
(Fig. a). Melt forms appear at the layer transition
from 22 March, and an ice layer forms in the matrix domain on
24 March, i.e., 4 to 5 d earlier than observed
(Sect. and ). At this date, ice
is transferred from the ice reservoir to the matrix domain
(Fig. ). The ice layer formed is 43 mm
thick with a dry density of 821 kgm-3 and a significant
volumetric liquid water content of
θmatrix=7.8% and
θPF=1.7% (on 24 March
13:00 UTC). Similar to the RE/PF simulation, no ice forms at layer
2 due to the presence of melt-freeze crusts. However, fewer melt-freeze
crusts are simulated in the snowpack, which is more in accordance with
the observations. The matrix water flow reaches the ground on 30 March
(Fig. b), i.e., 10 d before the observations
(Sect. ). The water front progression occurs
too early, which is similar to the RE/PF simulations. The ice reservoir does
not modify the snowpack runoff compared to the RE/PF simulations (not
shown).
SNOWPACK simulation RE/PF/IceR from 15 March to 15 April 2017 at
WFJ: (a) grain shape according to the classification of , and (b) liquid water content in the matrix domain. Rectangles highlight layers 1 and 2 with dashed lines before ice formation in the matrix domain and solid lines afterwards.
SNOWPACK simulation RE/PF/IceR from 15 March to 15 April 2017 at WFJ: volumetric ice content in the cumulated ice reservoir. Rectangles highlight layers 1 and 2 with dashed lines before ice formation in the matrix domain and solid lines afterwards.
Simulations over several winter seasons
To assess the impact of the ice reservoir parameterization on ice
formation in the SNOWPACK model, simulations at WFJ are performed over
17 winters from 1999/2000 to 2015/2016 using the RE/PF and the
RE/PF/IceR configurations. Only traditional snow profiles are
available for evaluation, which is similar to . Ice layers
at the snow–soil interface are not taken into account because of the
changing snowpack base in the observations over the winters (wooden
board, gravel) and possible boundary effects in the simulations. Only
simulated ice layers are verified against observations to calculate
hits (number of simulated ice layers matching an observation) and
false alarms (number of simulated ice layers that do not match any
observation). A height difference of 20 cm is used for
matching simulations and observations, which is similar to
. We assume that the formation date of an observed ice layer is between the last snowpack profile without an observed ice layer and the first profile where it is indicated. If the
simulation date is more than 1 month away from the observed
formation time interval or if the height difference is more than
20 cm, the event is considered a false alarm. Missed events
(observed ice layers that are not simulated) are not counted. Indeed,
attributing fortnightly visual ice observations to a unique observed
ice layer can be very ambiguous and contrary to simulated ice layers that
persist in time. Results of this multiyear evaluation are summarized
in Table .
Hits (HI) and False Alarms (FA) of simulated ice layers for RE/PF and RE/PF/IceR simulations at WFJ for 17 winter seasons from 1999/2000 to 2015/2016. HI (height and date): simulated ice layers that match an observed one formed at less than 20 cm of height difference and in the same time interval. HI (height only): simulated ice layers that match an observed one formed at less than 20 cm of height difference and less than 1 month away from the observed time interval. FA: simulated but not observed ice layers (or more than 1 month away from the simulated formation).
HIHI(height and date)(height only)FARE/PF336RE/PF/IceR5101
Overall, the addition of the ice reservoir parameterization to the
RE/PF scheme enables it to form more ice layers with a higher number of
hits (15 against 6, with a 1 month tolerance) and a lower number of
false alarms (1 against 6). The simulated ice formation date is on
average 22 d earlier than the observation interval for the
RE/PF scheme and 4 d earlier for the RE/PF/IceR scheme. The
premature ice formation with the RE/PF scheme is consistent with the
overestimation of simulated early season snowpack runoff from
preferential flow, as suggested by . It is logically
delayed in RE/PF/IceR simulations because the ice transits through the
ice reservoir before being transferred to the matrix. The RE/PF/IceR
configuration also mitigates the number of unobserved early
melt-freeze crusts compared to the RE/PF configuration (not shown), as
already highlighted for the 2016/2017 season (Figs. and
). However, a very high number of ground ice layers were
simulated (17 for the RE/PF/IceR scheme against 8 for the RE/PF
scheme). This number is probably excessive and due to possible
snow-soil boundary effects.
Discussion
This detailed study of ice layer formation at Weissfluhjoch enables us to
assess both a comprehensive observation dataset and state-of-the-art
1-D snowpack simulations for monitoring a complex process. We discuss
hereafter the relevance of these methods and results.
First, the combined use of traditional snow profiles with measurements
of higher temporal resolution like the SMP provides a suitable
observation framework to study the transition period from dry to
isothermal snowpack when ice formations appear due to preferential
flow. Snowpack runoff measurements associated with snow temperature
sensors and an upGPR-derived water front gave insights into the
homogeneous wetting of the snowpack and the period when the bottom of
the snowpack was primarily affected by preferential flow. SMP profiles
of penetration resistance showed clear signals of the presence of ice, when compared to visual observations, with values higher than 10 N
(Fig. ), while penetration resistances in dry snow were
mostly lower than 2 N (Fig. ) and melt-freeze
crusts were characterized by intermediate penetration resistances
(usually between 5 and 10 N;
Fig. ). The identification of ice layers with several SMP
profiles regularly spaced also offers a more quantitative estimate of
ice heterogeneity than a subjective visual observation. However, the
temporal and spatial variabilities may be complex to distinguish, as
shown for layer 1 (Fig. ). The visual layer tracking
of SMP profiles is also a source of
uncertainties. developed a method matching
several hardness profiles to synthesize them into one representative
profile. This method was not considered relevant for the present study
which focuses on the local heterogeneity of ice layers. Moreover, when ice
layers are too thick, the SMP cannot go through them, as often happens
in spring. For winter 2017 at WFJ, no complete SMP profile could be
performed after 19 April. Finally, the difficulty in identifying the
exact date of ice formation or attributing isolated, fortnightly ice
layer observations in traditional snowpack profiles to a unique ice
layer (Sect. ) highlights the added value of the more
comprehensive observation dataset used for winter 2016/2017. Overall,
this comprehensive high-resolution dataset also including
detailed measurements of the density and specific surface area of the
snow; provides valuable information for a thorough
validation of current and future snow-cover models.
The 1-D SNOWPACK simulations provide complementary insights into
the observation dataset despite the spatial heterogeneity of ice
layer formation due to preferential flow. The addition of an ice
reservoir enables us to parameterize the local formation of ice at
capillary barriers; it may thus be considered representative of the
volumetric content of ice lenses at a given layer. These local
specificities are not taken into account in the matrix domain, which
is the mean state of the snowpack, until they become homogeneously
spread. This parameterization, which delays microstructural changes in
the matrix due to liquid water flow, is consistent with the recent
findings of who showed that preferential flow
paths migrate and thus gradually affect the original snow
microstructure. Several limitations may be noted. The matrix flow
modeled by the Richards equation occurs too early and leads to an
excessive snowpack runoff, which is even more enhanced by preferential
flow. This may explain the premature formation of ice layer 1; the
matrix water front reaches this level on 24 March in simulations,
while it is observed higher than 1 m on 24 March, and the
surrounding layers are still dry on 28 March in the observations when
the first ice lenses are observed. In addition, the performance of the
RE/PF water transport scheme strongly depends on a good representation
of the snow microstructure by SNOWPACK and particularly on the grain
radius and snow density. For instance, no ice or water ponding are
simulated at layer 2 during winter 2017 because the observed capillary
barrier structure (rounded grains and faceted crystals above surface
hoar) is not adequately represented (unobserved melt-freeze crusts
above layer 2). Finally, the implementation of the ice reservoir is
meant to improve the representation of ice formation within the 1-D
framework of the RE/PF dual-domain approach, but it does not mitigate
the limitations of the preferential flow parameterization. In
particular, the vertical spreading of water flowing back from the
preferential to the matrix domain is not solved; its effect on ice
formation is only mitigated with the cumulated ice reservoir. Advances
in preferential water flow modeling in the snowpack have recently
been developed by to tackle the
capillary hysteresis effect and capillary pressure overshoot. They
could be considered as improving the representation of preferential flow
in the SNOWPACK model through a more accurate determination of
capillary pressure at the tip of the preferential flow path with
effects on the water transfer from preferential flow to the matrix domain.
Despite the limitations inherent to 1-D simulations of preferential
flow, the dual-domain approach combined with the ice reservoir
parameterization in SNOWPACK provides relevant information concerning
deep ice layer formation. The ice reservoir limits the formation of
unobserved early melt-freeze crusts and, overall, enables us to simulate
more observed ice layers. For the detailed study of winter 2017, it
gives complementary insights on the formation of ice layer 1;
according to the simulations, the vertical preferential flow was
sufficient to form the ice layer even though a possible contribution
of lateral flow cannot be totally excluded.
Conclusions
We presented here a detailed study of deep ice layer formation in the
snowpack due to preferential water flow at Weissfluhjoch, a
high-altitude alpine site. Monitoring deep ice layers is of particular
relevance for many applications but is challenging in natural snow
conditions. This research proposed an approach based on the combined
use of a novel comprehensive observation dataset at high temporal
resolution and detailed snow-cover modeling with improved ice
formation representation.
Weekly traditional snow profiles, snowpack runoff and temperature
measurements, as well as upGPR-derived water front height, enabled us to
better monitor the dry-to-wet transition period between mid-March and
mid-April 2017. In particular, the first days of measured snowpack
runoff could be attributed to preferential water flow, and the exact
date of the first isothermal snowpack with matrix water flow reaching the
ground could be identified. Daily penetration resistances measured
with a SnowMicroPen (SMP) gave more accurate insights on ice layers,
complementing the traditional visual observations. Through
comparisons with the visual observations, penetration resistance
thresholds of 5 and 10 N in SMP profiles could be defined for
the identification of melt-freeze crusts and ice layers,
respectively. Ice formation could be monitored at a higher temporal
resolution, and the use of several profiles per day gave more
quantitative information on the spatial discontinuity of ice. The daily
succession of profiles also enabled us to track the two main capillary
barriers where ice formed, providing additional information on the
evolution of the layers.
The 1-D SNOWPACK simulations, including a parameterization of
preferential flow, showed an overall good representation of the
snowpack structure but a premature matrix wetting that is associated with an excessive snowpack runoff. The observed ice layers were not simulated
due to the early formation of thick melt-freeze crusts, explained by
limitations of the preferential flow scheme. We developed an ice
reservoir parameterization to mitigate these limitations, with
freezing water transferred from the preferential flow domain to an ice
reservoir. The ice was included in the matrix domain when the layer
could be considered to be continuous. This parameterization improved
the simulation of winter 2017 with a limited number of unobserved
early melt-freeze crusts and the formation of one ice layer. However,
the early transition to a wet snowpack was not improved as the water
transport was not modified. The ice reservoir scheme also showed
improvements for the simulation of ice layers over past seasons.
These simulations highlighted the relevance of detailed snow-cover
models for the modeling of complex phenomena like deep ice layers
formed by preferential water flow since an accurate representation of
the snow microstructure is necessary. Recent advances in preferential
flow observations and modeling could contribute to strengthening water
transport representation. This study also underlined the importance of
comprehensive observation datasets for the validation of complex snow
models. Collecting high-resolution data over more winter seasons will
further improve the understanding of deep ice layer formation,
particularly concerning their density, their impermeability and their
evolution in the late melting season. Ice reservoir simulations also
call for further experiments on large snowpack samples, similar to
, focusing on the formation of discontinuous ice
lenses due to preferential water flow.
Code and data availability
The SNOWPACK model is available under a LGPLv3 license at https://models.slf.ch (last access: 14 October 2020). The version used in this study, including the ice reservoir parameterization, corresponds to revision 1867 of /branches/dev. The observation dataset, including SMP profiles, traditional snowpack profiles, water front and snow temperature measurements, and initialization files for SNOWPACK simulations, is available at 10.16904/envidat.170. Meteorological data driving the SNOWPACK model, as well as snowpack runoff measurements, are available at 10.16904/1.
Author contributions
CF and AvH designed the study, carried out the field measurements and were responsible for the maintenance of the equipment. LQ was responsible for the modeling strategy, analyzed the measurements and simulations, and wrote the paper. DL processed the upGPR data to estimate the location of the water front. CF, AvH and NW helped to analyze measurements and simulations. All authors contributed to the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank all the people from the WSL Institute for Snow and Avalanche Research SLF involved in the measurements at Weissfluhjoch. We would like to mention in particular Jean-Benoît Madore who helped out in the field during the dry-to wet transition of the snowpack. We thank Mathias Bavay for his help with the SNOWPACK model. We also thank Francesco Avanzi and the anonymous reviewer for their detailed comments which helped improve the paper.
Review statement
This paper was edited by Guillaume Chambon and reviewed by Francesco Avanzi and one anonymous referee.
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