Introduction
The study of permafrost in mountain regions has become relevant in
view of ongoing climate changes
.
Although permafrost warming and increasing active layer thickness (ALT) has been observed worldwide
,
in mountain areas the complexity of topography, ground surface type,
snow cover distribution, subsurface hydrology and geology strongly influence
the thermal regime of mountain permafrost , altering
the response to changing environmental conditions.
For monitoring the huge spatial variability of mountain permafrost,
a number of monitoring sites has been established through the Alps
during the last years e.g.,. At present the collection
of temperatures in boreholes provides the best direct evidence of
permafrost state and evolution. Nevertheless,
the combination of geophysical methods and thermal monitoring is
particularly suitable for long-term monitoring of mountain permafrost
because it provides crucial information on ground ice/water content and structure
e.g.,.
The site of Cime Bianche has been designed with the main objective of monitoring the
spatial variability of mountain permafrost. Moreover Cime Bianche site is a permanent
observatory in the southern side of the European Alps, a region where permafrost
observations are more sparse and younger compared to the northern side e.g.,,
and where significant climatological differences occur e.g.,.
At Cime Bianche, the spatial variability of ground surface temperature (GST)
is measured because it has crucial implications on the initialization, calibration and validation of numerical models
e.g.,, and it is often used as an indicator of permafrost occurrence.
One of the main challenges in the study of GST variability is the quantification of the thermal
effect of snow cover given the influence that it can have on thermal regime trough different
processes .
On gentle slopes, snow cover mostly causes a net increase of mean annual ground temperature due to the insulating
effect during winter, but timing of onset and melt-out, duration, thickness
and interaction with ground surface characteristics strongly control the local magnitude of this effect
.
Although a number of studies focused on snow–GST interaction exists e.g.,for a review,
little is known on its spatial and temporal variability especially over complex alpine terrains.
Beside the GST, the active layer thickness is also measured at Cime Bianche.
The World Meteorological Organization recognizes permafrost and active layer
as one of the essential climate variables selected for quantifying the impacts of climate change e.g.,.
In the Alps, the active layer is of particular interest because it directly affects slope processes
e.g., and infrastructures stability e.g.,.
The active layer dynamics are controlled by
a number of variables such as air temperature, solar radiation, topography, ground
surface characteristics, ground ice/water content and the timing, distribution
and physical characteristics of the snow cover
.
As a consequence, the active layer thickness has an high spatial and temporal variability
which in the Alps may occur at very small scale.
Compared to the active layer which responds more to short-term
variations like seasonal snow and air temperature conditions, the deep (10 to 200 m) thermal regime
of permafrost reacts to long-term changes in climate .
The deep permafrost temperature regime is a sensitive indicator
of the long-term climate variability and changes of the surface energy balance
. The trend analysis of deep temperature time series
allows the detection of signals of past and ongoing changes of permafrost
e.g.,.
Overview of the Cime Bianche monitoring site.
The overall objective of this paper is to provide a first synthesis on the state
and recent evolution of permafrost at Cime Bianche. In particular we present (i) the spatial
and temporal variability of GST and its relation with snow cover (ii) the small-scale (30 m)
ALT differences and (iii) the warming trend of deep permafrost temperature.
Data and methods
Site description
The Cime Bianche monitoring site is located in the Western Alps
at the head of the Valtournenche valley (Valle d'Aosta, Italy,
45∘55′ N–7∘41′ E) on the Italian side of
the Matterhorn, at 3100 m a.s.l. (Fig. ). The
site is located on a small plateau slightly westward, with degrading
characterized by terracettes, convexities and depressions that result
in a high spatial variability of snow cover thickness during winter.
The bedrock lithology is homogeneous, mainly consisting of
garnetiferous micaschists and calcschists belonging to the upper part
of the Zermatt–Saas ophiolite complex . The
bedrock surface is highly weathered and fractured, locally resulting
in a cover of coarse-debris deposits with a thickness ranging from few
centimeters to a couple of meters. The presence of small landforms
like gelifluction lobes (between 0.6 and almost 5 m in length)
and sorted polygons of fine material (with diameters ranging between
0.6 and 3.4 m) suggests the presence of permafrost.
The climate of the area is slightly continental. The long-term mean
annual precipitation is reported to be about 1000 mm yr-1
for the period 1931–1996 while the in situ
records show a mean of 1200 mm yr-1 for the period
2010–2013. The mean annual air temperature (MAAT) is about
-3.2 ∘C (mean 1951–2011). Mean monthly air
temperatures are positive from June to September, while February and
July are, respectively, the coldest and the warmest months. The site is
very windy and mainly influenced by NE–NW air masses. The wind action
strongly contributes to the high spatial variability of snow cover thickness.
Permafrost research in the area started in the late 1990s
with repeated campaigns of measurements of the bottom temperature of
snow (BTS) and glaciological observations showing that the
monitoring site was probably ice covered during the climax of the
Little Ice Age. In 2003, as a preliminary investigations for site
selection, the potential permafrost occurrence in the area was
assessed using results from BTS, vertical electrical soundings
and ERT (electrical resistivity tomography) and the application of
numerical models like Permakart and Permaclim .
Instrumentation
The site instrumentation started in 2005 and has been progressively
upgraded during the following years. The current setting is nearly
unchanged since August 2008 and consists of two boreholes, a spatial
grid of ground surface temperatures measures and an automatic weather station (AWS).
Boreholes
A deep (DP) and a shallow (SH) borehole, reaching a depth of 41 and
6 m, respectively, located at a distance of about 30 m
(Fig. ), have been drilled in 2004 with core-destruction
method. Both boreholes are 127 mm in diameter with
a 60 mm sealed PVC pipe for sensor housing. The boreholes are
equipped with thermistor chains based on resistors type YSI 44031
(resolution 0.01 ∘C, absolute accuracy
±0.1 ∘C). The entire setup (thermistor chains attached to the data logger)
have been calibrated by the manufacturer before the installation. Sensors depths in meters from the
surface are 0.02, 0.3, 0.6, 1, 1.6, 2, 2.3, 2.6, 3, 3.3, 3.6, 4, 4.6,
5.9 for SH and 0.02, 0.3, 0.6, 1, 1.6, 2, 2.6, 3, 3.6, 4, 6, 8, 10,
12, 14, 15, 16, 17, 18, 20, 25, 30, 35, 40, 41 for DP. In each
borehole, the shallower sensors (0.02 and 0.3 m) are cabled on
two independent chains and are used to measure the ground surface
temperature outside the PVC tube in order to avoid the thermal
disturbance of the casing. Temperatures are sampled every
10 min and recorded by a Campbell Scientific CR800
data logger. The system is equipped with a GPRS module for daily remote
data transmission.
Ground surface temperature grid (GSTgrid)
A small grid (40 m × 10 m) is used for monitoring the spatial
variability of GST. The grid consists of
five nodes: four at the corners and one in the center
(Fig. ). Each node is equipped with two platinum
resistors,
PT1000 (resolution 0.01 ∘C, accuracy
±0.1 ∘C), buried in the ground at depths of
0.02 and 0.3 m (according to ). Ground
temperatures are recorded hourly by a Geoprecision D-Log12 data logger.
For the analysis, GST measured at the two boreholes is also
included; thus data from seven nodes are used for the analysis. Ground surface at each
node is mainly characterized by coarse debris with a fine matrix of
coarse sand and fine gravel. At each node, the sensors are placed in
the matrix thus local ground conditions are nearly homogeneous between
all nodes. In contrast, snow cover depth and duration sharply differ across
the grid nodes. For this reason, based on field observations and
temperature time series analysis the data set
is divided in snow-free and snow-covered nodes.
The first group includes three nodes characterized by shallow or intermittent winter snow
cover, while the latter group includes four nodes that clearly show a long
lasting deep snow cover damping temperature oscillations
during winter (Fig. ).
Automatic weather station
An AWS has been installed just above the
borehole SH since 2006. Air temperature and relative humidity,
atmospheric pressure, wind speed and direction, incoming and outgoing
short- and long-wave solar radiation and snow depth are recorded every
10 min by a Campbell Scientific CR3000 data logger. The system
is equipped with a GPRS module for the daily remote data
transmission. In September 2011 a second snow depth sensor was
installed in the surroundings of the DP borehole. Finally, solid and
liquid precipitation has been measured since January 2009 by an OTT
Pluvio2 system.
Data analysis
This section reports a short description of the methods used for the
calculations of synthesis parameters considered in this study.
MAGT is the mean annual ground temperature at a specific depth (m)
(e.g., MAGT10).
MAGST is the mean annual ground surface temperature.
ALT is the active layer thickness defined as the maximum depth (m)
reached by the 0 ∘C isotherm at the end of the warm
season. It is calculated considering the maximum daily temperature at
each sensor depth and interpolating between the deepest sensor with
positive value and the sensor beneath. The maximum of the resulting
vector and the corresponding day are named ALT and ALTday,
respectively. This procedure is applied on the warmest period
of the year, here fixed from 1 August to 30 November.
The uncertainty of ALT estimation is evaluated considering the
amplitude of thermistors noise (inferred from calibration of the manufacturer)
and the interpolation distance between the sensors. Considering these factors,
the uncertainty of ALT estimation is ±0.15 m in borehole SH and
±0.2 m in borehole DP.
Example of detection of snow cover duration from GST time series with the method of .
OD is the on-set date of snow; MD is the melting date of snow. The periods
used for the calculation of MAGST, SD and MAATsf are represented by the scheme on top.
TTOP is the MAGT at the top of the permafrost table
. It is calculated by interpolation of the MAGT at
depth of the ALT that is considering the first sensors above and
below the ALT.
THO is the thermal offset within the active layer and is computed as
TTOP-MAGST .
Zero annual amplitude oscillation (ZAA) is the depth beneath which there is almost no annual fluctuation
(AF)
in ground temperature, nominally smaller than 0.1 ∘C
. The AF is calculated
at each sensor depth as the difference between annual maximum and
annual minimum of the mean daily temperatures. The ZAA is calculated
by interpolation between the deepest sensor with AF greater than
0.1 ∘C and the sensor beneath. When necessary,
a moving average, with a window of 360 days, is applied on deep nodes
data (below 8 m) before daily aggregation to remove electrical noise
(±0.01 ∘C).
All the parameters listed above, with the exception of ALT,
are computed considering the hydrological year
(beginning 1 October) as a reference period. All the analyses are performed with the free
statistical software R . When appropriate, the variability of
the results is expressed in terms of standard error
(se = sd/n,
where se is standard error, sd is standard deviation
and n is the sample size).
Snow cover duration and snow-free days
In order to investigate the effect of snow cover duration and air temperature on
MAGST,
the method of is applied on snow-covered nodes
using the sensors at 0.02 m.
This method allows us to infer the date of snow onset (OD)
and the date of snow melt (MD) from the amplitude of ground temperature oscillation. Subsequently, starting from OD and MD, it is possible to calculate
(i) the duration of snow cover (SD, Fig. ) as the number of days between OD and MD
and (ii) the number of snow-free days as the sum of remaining days of autumn and summer.
The latter period is used as reference for calculating the mean annual air
temperature of snow-free days (MAATsf, Fig. ).
Methodological steps of trend analysis. Step 1: monthly aggregation (thin black line with circles).
Step 2: seasonal detrending (thick black line). Step 3: trend fitting (dashed red line). Vertical dashed
lines represents 1 October, materializing the limits of the hydrological years.
Trend analysis
In order to look for linear trends that might reflect warming, two
non-parametric methods are applied to borehole temperatures:
Mann–Kendall test (MK) and Sen's
slope estimator (SS) . These methods are commonly used
to assess trends and related significance levels in
hydro-meteorological time series such as water quality, stream flow,
temperature and precipitation
e.g.,. The reason for using
non-parametric statistical tests is that they are more suitable for
non-normally distributed data and are not sensitive to outliers or
abrupt changes.
The procedure chosen includes (i) the pre-whitening of the data to
remove the lag-1 autocorrelation components as recommended by
(see also and ),
(ii) the fitting of the trend's slope with SS and (iii) the testing of trend significance
level (p value) with MK. Such a procedure is implemented in the
R package zyp .
Given the short climatological time span of the borehole
observations, a seasonal detrending is recommended, as suggested by
, for better discerning the long-term linear trend
over time. Thus, a seasonal decomposition based on loess smoother
is applied to the monthly
aggregated time series of each borehole before applying SS and MK
(Fig. ). Such a seasonal detrending method is
implemented by the R function stl .
Geophysics
At the end of summer 2013, two geophysical surveys have been realized
with the objective to assess the composition of the
subsurface. A first, explorative geoelectric (ERT) profile was
performed on 16 August 2013 and repeated on 9 October 2013 in combination with one refraction seismic tomography
(RST) along the same line (see Fig. ). Combining
refraction seismic and ERT measurements enables us to unambiguously
identify the subsurface materials in the ground. Due to very different
specific resistivities, ERT is best suited to differentiate between
ice and water, whereas the distinction between air and ice can more
easily be accomplished by RST because of large contrasts between
their respective p wave velocities.
Electrical Resistivity Tomography
A 94 m long electrode array composed of 48 electrodes with
2 m spacing was installed along a straight line less than 2 m away
from the two boreholes (Fig. ). Current was
injected using varying electrode pairs, and the resulting potential
differences were automatically measured by a Syscal (Iris Instruments)
for each quadrupole possible with the Wenner–Schlumberger
configuration (529 measurements, 23 depth levels). The electrode
locations were marked with spray paint and a number of electrodes were
left on site to facilitate further measurements.
The measured apparent resistivity data sets were then inverted using
the RES2DINV software with the following
setup. A robust inversion constraint was applied to avoid unrealistic
smoothing of the calculated specific resistivities. Additionally, the
depth of the model layers was increased by a factor 1.5 and an
extended model was used to match the model grid of the corresponding
seismic inversion. Note that for geometric reasons, the two lower
corners of the resulting tomograms have very low sensitivity to the
obtained data and should not be over-interpreted. Finally,
a time-lapse inversion scheme was applied to the two ERT data sets,
yielding the percentage of resistivity change from the first
measurements to the second. Here, an unconstraint inversion was
chosen, meaning that the ERT measurements were inverted independently.
Mean annual ground surface temperatures at depths of 0.02 m (red) and 0.3 m (blue).
Star symbols indicate snow-covered nodes, while bullets indicate snow-free nodes.
The horizontal lines indicate the mean MAGST for each year and each depth. Black rectangles are
used to highlight the min–max envelope to facilitate the inter-annual comparison.
Refraction seismic tomography
The measurements were conducted using a Geode seismometer (Geometrics)
and 24 geophones placed with 4 m spacing. A seismic signal was
generated in-between every second geophone pair by repeatedly hitting
a steel plate with a sledge hammer. To improve the signal-to-noise
ratio, the signal was stacked at least 15 times at each location. Two
additional offset shot points were measured (3 m before the
first geophone and 6 m beyond the last one) in order to
maximize the spatial resolution and match the ERT profile length and
depth of investigation.
The first arrivals of the seismic p wave were manually picked for
each seismogram using the software REFLEXW
. A simultaneous iterative reconstruction technique
algorithm was then used to reconstruct a 2-D tomogram of
p wave velocity distribution based on the obtained travel
times. Starting from a synthetic model, the travel times are calculated
and compared to the measured ones. The model is then updated
iteratively by minimizing the residuals between measured and
calculated travel times.
Scatterplots of SD (a) and MAATsf (b) against MAGST.
The solid line represents the linear fit while the dotted lines are the confidence
intervals. The metrics of the fitting are also reported.
Results
Ground surface temperatures
Figure shows MAGST at 0.02 and 0.3 m on the seven GST nodes.
Some years (e.g., 2009, 2011, 2013) show a MAGST spatial variability, evaluated as the range of MAGST measured in all
nodes and
greater than 3 ∘C, that clearly exceeds the inter-annual variability.
In general, considering all 7 years, we observed that mean spatial variability
(2.5 ± 0.1 ∘C) is greater than mean
inter-annual variability (1.6 ± 0.1 ∘C).
The results are similar at both depth. The difference between MAGST
measured at 0.02 and 0.3 m is, on average,
0.4 ± 0.1 ∘C, with deeper sensors usually warmer
than the shallower ones. On average, the thermal offset due to snow cover
is about 1.5 ± 0.2 ∘C with snow-covered nodes
being warmer than snow-free nodes. These observations confirm that
the warming and cooling effects of, respectively, a thick and thin snow
cover can coexist over short distances
(< 50 m) and lead to high spatial variability of the GST.
The duration of snow (SD) on snow-covered nodes
is on average 270 ± 6 days with a mean range of spatial
variability of 28 days and a mean range of inter-annual variability of 48 days.
To disentangle the influence of snow and air temperature on surface
temperature in snow-covered nodes, we tested the relationship
between MAGST and MAAT and between MAATsf and SD. We found no significant correlation
between MAGST and MAAT. Figure shows the scatterplot
comparing SD (A), MAATsf (B) and MAGST: MAGST is significantly correlated to both SD (p < 0.05) and MAATsf
(p < 0.001), with the latter explaining the higher portion of variance (R2 = 0.39).
Being computed on snow-free days, MAATsf is mainly controlled by air
temperature but partially also by the duration of snow cover, therefore
integrating the relative contribution of both components (snow duration
and air temperature) on MAGST.
Fluctuations of snow cover thickness (Hs) and ground temperatures (daily mean) at selected depths in
the active layers of Cime Bianche from 1 October 2010 to 30 September 2013 determined from
borehole temperature data. Lines type: dashed is for SH, solid is for DP. Colors: red is for shallower
temperatures (1.6 m), blu is for deeper temperature (3 m), grey is for snow.
Active layer
Table summarizes the active layer parameters observed in
the two boreholes. Since August 2008 data are available at both SH and
DP boreholes, results of ALT can be compared over 6 years
while MAGST, TTOP and THO over 5 years (shaded rows in
Table 1). Missing values (column % NA) in both boreholes are lower
than 4 % in all years.
ALT is the parameter showing the greater difference between the two
boreholes with a mean of 2.7 ± 0.3 m in SH and
4.7 ± 0.2 m in DP. The mean inter-annual difference of ALT
between the two boreholes is 2.0 ± 0.1 m, while the mean
absolute inter-annual variability of ALT at borehole level is
1.0 ± 0.1 m. In both boreholes the maximum ALT has been
recorded in 2012 and the minimum in 2010. ALT (date) is normally
anticipated in DP (except 2013) with differences ranging from a few days
(e.g., 2009) to more than 3 weeks (e.g., 2012). The MAGST is on average
slightly lower in SH, which normally shows a thinner winter snow cover
compared to DP (Fig. ). The TTOP values are very
similar, around -0.9 ∘C. The THO is negative in both
boreholes (except 2013) with a mean value of about
-0.5 ∘C in DP and -0.3 ∘C in SH.
Synthesis parameters of active layers recorded in the two boreholes of Cime Bianche.
The average values (Avg.) are calculated over the period 2009–2013 where data from both boreholes are available.
Acronyms: hydrological year (H. Y.), active layer thickness (ALT), mean annual ground surface temperature (MAGST),
mean annual temperature at the top of permafrost (MAPT), thermal offset (THOFF), missing data (NA),
shallow borehole (SH), deep borehole (DP).
H. Y.
ALT (m)
ALT (date)
MAGST (∘C)
MAPT (∘C)
THOFF (∘C)
% NA
SH
DP
SH
DP
SH
DP
SH
DP
SH
DP
SH
DP
2006
3.1
–
11 Oct
–
–
–
–
–
–
–
–
–
2007
2.4
–
14 Oct
–
-0.3
–
-0.8
–
-0.4
–
0
–
2008
1.9
3.9
27 Sep
25 Sep
-2.1
–
-1.8
–
0.3
–
4.38
–
2009
3.0
4.9
24 Oct
20 Oct
-0
0
-0.7
-0.8
-0.6
-0.9
3.28
3.28
2010
1.9
3.8
18 Oct
8 Oct
-1.1
-1.2
-1.2
-1.3
-0.1
-0.1
1.37
1.37
2011
3.3
5.1
8 Nov
23 Oct
-0.5
0.1
-1.1
-1
-0.6
-1.1
0.27
0
2012
3.6
5.4
30 Oct
4 Oct
-0.4
-0.3
-0.8
-0.7
-0.4
-0.5
2.74
3.01
2013
2.0
4.6
13 Oct
13 Oct
-1.3
-0.7
-1
-0.6
0.3
0.1
3.6
3.59
Avg.
2.7 ± 0.3
4.7 ± 0.2
24 Oct
13 Oct
-0.7 ± 0.2
-0.4 ± 0.2
-1 ± 0.1
-0.9 ± 0.12
-0.3 ± 0.2
-0.5 ± 0.2
2.25 ± 0.62
2.25 ± 0.68
The values of Table show that all the active layer
parameters are very similar between the two boreholes with the only
exception of ALT, which in DP is nearly double than in SH. To better
understand the causes of this difference, the daily mean temperatures
at selected depths within the active layer of both boreholes and the
corresponding snow cover thickness are compared in
Fig. . Although a consistently thinner snow depth is
recorded on SH compared to DP (mean difference
∼ 41 ± 14 cm during the winter seasons 2012 and 2013), the
duration of the insulating snow cover is similar
(250 ± 16 days for SH vs. 254 ± 17 days for DP)
and effectively does not determine a large difference in MAGST
(Table ). Consequently, the snow cover regimes of the
two boreholes can be considered equivalent.
For these reasons we hypothesize that ALT difference may be related to
a greater ice/water content in SH compared to DP. This is
revealed by the geophysical survey (see Sect. and
Fig. ) and can be inferred by temperatures at greater
depth. At 1.6 m (red lines, Fig. )
a pronounced zero-curtain effect can be observed in SH (dashed lines)
twice per year, (i) from snow melt to mid-summer and (ii) from the
snow onset to mid-winter, while a similar behavior is missing in
DP. The occurrence of the zero-curtain reflects a large consumption of
energy, both for ice melting during summer and water freezing during
winter, resulting in lower temperatures of SH. Deeper down, the summer
heat wave in SH is further delayed if compared to DP: at 3 m
in SH (dashed blue lines) the zero-curtain effect is almost continuous
from late summer to early winter (e.g., in 2010 and 2011), and it is not
possible to see a breaking point between melting and freezing
processes. Such a behavior is totally missing in DP. It is also
interesting to observe that freezing zero-curtain ends nearly
contemporary at 1.6 and 3 m and is followed by a rapid temperature drop.
In conclusion, the ALT at Cime Bianche shows a pronounced spatial
variability probably caused by the variability of ice/water content in
the sub-surface and associated energy consumption resulting from
freezing and melting processes.
Permafrost temperature and warming trend
Due to the small depth reached by the borehole SH, the analysis of
permafrost temperature is limited to the borehole
DP. Looking at temperature profiles with depth
(Fig. ), the permafrost layer at Cime Bianche has
a thickness greater than 40 m and a mean temperature of about
-1.2 ∘C. The ZAA varies across years and during the
observation period ranged from a minimum of 14.2 m in 2011 to
a maximum of 16.2 m in 2013 (Table ). During the
observed years, both minimum (solid lines) and maximum (dashed lines)
temperature profiles (deeper than 6 m) tend to progressively
shift toward warmer temperatures (Fig. ). The only
exceptions are represented by the 2011 maximum and the 2009
minimum, with the latter only above 10 m of depth.
The observed temperature shift is also quantitatively supported by the
trend analysis. The analysis was conducted at all
depths, but only deeper temperatures (below 8 m)
show significant trends (Kendall's p value < 0.01).
Figure reveals that a pronounced
warming rate ranging from 0.1 ∘C yr-1 at 8 m to
0.007 ∘C yr-1 at 41 m can be observed. The upper
boundaries of the confidence intervals are systematically
unbalanced toward higher values and the lower boundaries are always
above zero. This means that, at all depths, the statistical
distribution of all possible fitted trends is positively skewed.
Based on this analysis, it is concluded that permafrost at Cime Bianche
is warming because significant positive warming rates are reported below 8 m.
Minimum (solid lines) and maximum (dashed lines) temperature profiles in the borehole DP
below 6 m of depth for the period 2009–2013.
Geophysics
Figure shows the final distribution of specific
resistivity for the two ERT measurements, the percentage of change in
the model resistivity between the two time steps and the p wave
velocity distribution over the same subsection. Additionally, the
surface characteristics and a detailed analysis of the geophysical
properties at the two borehole locations (SH and DP,
Fig. ) are included in the analysis.
The overall characteristics of both ERT profiles are very similar
(Fig. a and b) and can be divided into
three main zones: a low resistive layer directly below the surface,
varying between 2.5 m thickness at the top of the slope and
7 m thickness at the bottom; two high resistive
areas with values exceeding 20 000 Ωm, located below
the superficial layer (from the start of the subsection to the
superficial borehole: 0–34 m and 40–52 m);
and a less-high resistive area on the lower part of the profile below
5 m depth.
Interpolated depth of zero annual amplitude oscillation (ZAA) and
corresponding mean temperatures in the borehole DP.
H. Y.
ZAA (ΔT = 0.1 ∘C)
Depth (m)
Temp. (∘C)
2009
15.5
-1.3
2010
15.2
-1.2
2011
14.2
-1.3
2012
15.3
-1.2
2013
16.2
-1.2
Avg.
15.3
-1.2
Warming rate calculated over the period 2009–2013 below 8 m of depth in the borehole DP as
a function of depth. Black dots represent linear trends as ∘C yr-1.
The uncertainty of trend values is represented by the dashed bars, which indicate the lower and upper
boundaries of the 95 % confidence interval of the fitting model (see Sect. for
details).
Comparing the two ERT data sets (cf. also the time-lapse image in
Fig. c), one can observe a clear increase of the
uppermost low resistive layer between August and October which is
coherent with a thickening of the active layer observed in the
borehole temperature during this period. Another main difference
between the two measurements is the apparition of two low resistive
zones at 34 and 60 m, visible down to 10 and
15 m depth, respectively. These areas can also be seen in
the ERT tomogram from August but much less developed and limited to
a few meters. In addition, the very high resistive area located in the
upper part of the profile is much smaller and displaced by about
5 m towards the lower part of the profile in the second measurement.
Tomograms of the specific resistivities for both ERT measurements:
(a) 16 August 2013, (b) 9 October 2013, (c) percentage
change in model resistivity between the two dates and (d) seismic velocities.
The location of SH and DP is figured with vertical black lines of respective length.
A rough description of the surface aspect along the profile is also shown (e).
These changes are clearly visible in blue (increase) and red
(decrease) colors in Fig. c. As stated before, the two
data sets were inverted independently within the time-lapse
scheme. A constrained inversion (results not shown here) would yield
very similar overall distribution of resistivity changes; the only
difference is a much smaller range of values. The large area of
resistivity increase, located just above the superficial borehole
location and reaching down to the bottom of the profile, corresponds to
the displacement of the high resistive area observed in the ERT tomograms.
The RST tomogram exhibits much less lateral variations than the ERT
results (see Fig. d), pointing to the influence of
liquid water in the ERT results. One can clearly see a relatively slow
layer with velocities between 300 and 1500 m s-1 (red and
dark red colors) just below the surface, with varying thickness between
3 and 5 m. This layer is thickest in the vicinity of SH and thinnest at DP (64 m). Below this
first layer the velocities increase steadily until reaching the
maximum (around 6400 m s-1). The rate of velocity increase
is strongest around 40 m and there is a clear distinction
between the upper part of the profile (until 45 m) and the
lower one. At depth the high velocity zone is present in the upper
part and not in the lower part of the profile. Conversely, the
velocities at the surface are much higher in the lower part
(especially around DP) than in the upper part.
Vertical distribution of specific resistivity and P wave velocity at
the borehole locations, extracted from the tomograms shown in Fig. , as
well as borehole temperatures for the dates of the ERT and RST measurements. The
horizontal lines represent the active layer thickness at the respective time periods.
Both geophysical profiles show clear differences in the subsurface
properties as well as surface composition at the borehole locations (Fig. e).
The upper part of the profile (until 50 m) is more or less homogeneously covered
by medium size blocks and has the deepest layer of coarse-debris deposits, whereas the
granulometry in the lower part is much more variable at the surface and the debris layer is thinner.
The boreholes are located in very different conditions: DP is located in-between two zones composed
of big blocks (from pluridecimetric to metric), whereas SH is located at the junction between
medium size blocks (from pluricentimetric to decimetric), mixed and non-mixed with soil.
To relate in detail the results
yielded by the geophysics and the measured temperature, the vertical
distribution of specific resistivity, seismic velocity and ground
temperature at SH and DP are shown in Fig. .
Discussion
Ground surface temperatures
In this study both the inter-annual and the spatial variability of
MAGST within a restricted area has been analyzed and compared: the
results show that at Cime Bianche, the mean range of spatial
variability (2.5 ± 0.1 ∘C) far exceeds the mean
range of observed inter-annual variability
(1.6 ± 0.1 ∘C). Given the comparatively homogeneous
characteristics of the ground surface at the sensors locations, such
a variability is essentially caused by the heterogeneity of the snow
cover thickness both in space (effect of wind redistribution and
micro-morphology) and time (effect of variable weather conditions and
precipitations). In particular, the combination of snow cover duration and
air temperature during the snow-free period is the main factor controlling
MAGST values. This is true not only for snow-free nodes but also
for nodes experiencing long-lasting (270 days) yet highly variable (28 days)
snow cover.
The thermal effect of snow cover on ground surface temperature has
been extensively analyzed e.g.,. In
recent years, with the advances of minilogger technology, the number
of field experiments aimed at the characterization of the spatial
variability of GST has grown. Recently observed
a spatial variability of more than 2.5 ∘C within
a number of square homogeneous areas of 10 × 10 m. In Norway, report that MAGST varied
by 1.5–3.0 ∘C over distances of 30–100 m
in a region characterized by mountain permafrost.
observed ranges exceeding 4.3 ∘C between adjacent
loggers (< 50 m), although this value includes
inhomogeneities of surface characteristics. Similar results were
obtained by , who observed a variability of the MAGST
of up to 6 ∘C within heterogeneous areas of 0.5 km2.
The inter-annual variability of MAGST caused by snow is also well
known and documented by a number of studies
but has rarely been explicitly analyzed and quantified. An exception
in the Alps is represented by , who reported an
inter-annual variability of ±2.7 ∘C measured
during two seasons on eight mini-loggers with different surface
characteristics in the Murtèl–Corvatsch area. Our results thus
report a more robust quantification of the mean inter-annual GST
variability (1.6 ± 0.12 ∘C), based on a longer time
series (7 years).
The obtained results are very similar at both measurement depths. Given
such a small difference and the agreement of temperature fluctuations
between 2 and 30 cm, it is arguable that to describe the
spatial variability of GST and run long-term GST observations,
measurements at two or more depths are not needed.
Active layer
In this study, both ALT and temperature fluctuations within the
active layer of two adjacent boreholes have been compared. Such
experimental design provides direct evidence of the small-scale
spatial variability of the ALT and allows to evaluate the effect of ice/water
content on sub-surface temperature.
From 2009 to 2013 the ALT at Cime Bianche varied within 2.0 and
5.5 m with a mean inter-annual variability of
1.0 ± 0.1 m. These ranges and
the observed inter-annual variability of ALT are comparable to
those recorded in other alpine sites
In the Swiss Alps, the thickness of the active layer typically varies
between 0.5 and 8 m depth .
ALT in the borehole SH is systematically lower than in DP (mean
difference 2.0 ± 0.1 m) even though all the active layer
parameters (MAGST, TTOP, THO see Table 1) are very similar between the
two boreholes. On one hand, such a similarity suggests that snow cover
regimes above the two boreholes are nearly equivalent; thus snow
probably plays a major role only on the inter-annual variability of
ALT. On the other hand, the pronounced spatial variability of ALT is
probably caused by the variability of ice/water content in the
sub-surface and associated variation of energy consumption resulting
from freezing and melting processes. confirms this hypothesis by observing,
in a tundra lowland landscape, that ALT is related mainly to ground properties (ice content),
whereas snow physical properties have greatest influence on the ground surface temperatures.
Probably the different ice/water content between SH and DP is caused by snowmelt and meltwater
infiltration along preferential discontinuities (a borehole acts a discontinuity itself).
observed at Schilthorn (Swiss Alps) a similar
situation between two boreholes 15 m apart, ascribing the
lower ALT of one borehole to the higher moisture contents (and related
freezing) caused by preferential water flow paths from the surrounding
slopes. analyzed the thermal regime of four
adjacent boreholes drilled on differing material (coarse debris, fine
debris and bedrock) at Murtèl–Corvatsch (Swiss Alps) and
recognized meltwater and ice content as mainly responsible for the
observed ALT spatial variability.
The different amount of available water in the active layer of the two
boreholes is also reflected by the occurrence of the zero-curtain in
the borehole SH and its absence in the borehole DP. In the upper part
of the active layer, a pronounced zero-curtain can be observed two
times per year: (i) from snow melt to mid-summer (spring zero-curtain)
and (ii) from the snow onset to mid-winter (autumn
zero-curtain). Recently, deeply analyzed
similar behaviors on a sample of 10 boreholes in Switzerland, observing
that, on average, the duration of the spring zero-curtain is usually
shorter than the autumn one and is strongly dependent on snow depth at
the end of the winter. At Cime Bianche, such a distinction between spring and autumn zero-curtain
is not always possible in the deeper part of the
active layer. As also observed by , it may
happen that, below a certain depth, the ground temperature does not
become positive because the energy from the summer heat wave is not
sufficient to melt all ice before the onset of the subsequent winter
season. This continuous zero-curtain is more probable when a higher
amount of meltwater is available and can
occur at a different depth from year to year, strongly influencing the
resulting ALT.
Permafrost temperature and warming trend
In order to look for trends that might reflect warming, two
non-parametric methods were applied to borehole temperature
time series. The detected linear trends are statistically significant
(Kendall's p value < 0.01) only at depth below 8 m. Probably,
in the first meters, the seasonal and inter-annual variability of temperatures is so strong
that significant trends are not detectable, although a seasonal detrending
being applied to remove such high-frequency oscillations
(see also Sect. ).
The detected trends span the range 0.1–0.01 ∘C yr-1,
suggesting that at Cime Bianche permafrost is warming.
As also discussed by , the detection of
trends on time series covering a short time span requires caution and
adoption of specific criteria. Moreover, the estimation of
uncertainties and significance levels is also fundamental for
facilitating the comparisons of trends between differing sites and for
reproducing trend detection methods on others data sets.
Permafrost warming trends have been observed worldwide, both at high
latitude
and at lower latitude in high mountains .
Recently in the Alps, detected trends
on daily temperature time
series of two boreholes in the Muot da Barba Peider ridge (Eastern
Swiss Alps). For the deep frozen bedrock between 8 and 17.5 m,
a general warming trend was found with significant
(p value < 0.05) values ranging from 0.042 to
0.025 ∘C yr-1. At Cime Bianche a similar range
of warming rate was found between 16 and 20 m. The substantial
difference between the two sites is that the Swiss boreholes are
drilled at the top of a NW-oriented ridge with a mean slope of
38∘ and thus have a strong 3-D thermal effect induced by
topography . In the mountains of Scandinavia,
reported warming trends between 20 and
60 m of depth ranging from about 0.05 to
0.005 ∘C yr-1 over three sites,
while found an increase in mean ground
temperature between 6 and 9 m of depth at two sites, with
rates ranging from about 0.015 to 0.095 ∘C yr-1. Recently at Tarfala mountain
station (Sweden), found trends over
11 years (2001–2011) ranging from 0.047 to
0.002 ∘C yr-1 between 20 and 100 m of depth.
The absolute values of warming rates are difficult to compare because
of different site characteristics, geographical regions and
methods used for trend detection. Nevertheless, some
similitudes exist between our and the above-mentioned case studies:
(i) trends are difficult to detect at shallower depth because of the
higher seasonal variability of temperatures; (ii) warming trends are
mainly significant below 8–10 m of depth; (iii) warming
trends exponentially decrease with depth; (iv) there is no evidence of
negative (cooling) trends at any depth in recent literature.
Geophysics
Given the relatively high resistivity and p wave velocities along
the profiles, the presence of permafrost observed in the borehole data
is confirmed by the geophysics over the whole profile length
(Fig. ). Moreover, a clear discrepancy between the upper
part of the profile where SH is located and the lower one with
borehole DP can be seen in both the ERT and the RST data.
At DP, the comparatively high p wave velocities indicate the presence of weathered
bedrock close to the surface, whereas at SH a layer of coarse-debris
deposits in the uppermost 5 m is confirmed by very low
p wave velocities. Conversely, p wave velocities at depth are
higher for SH (around ∼ 6000 m s-1) than for DP (around
∼ 5000 m s-1, see also Fig. ). This
difference, also seen in the resistivity data (around
17 000 Ω m at SH and 13 000 Ω m at
DP), would indicate that a larger ice content is present in the
upslope part of the profile than in the lower part. This is in good
agreement with the spatial variation of ALT highlighted in
Sect. and the zero-curtain phase observed only at SH
(see Fig. ).
The low-resistivity and low-velocity layer near the surface, the
thickness of which increases visibly between August and October in the ERT
data, is considered to be the active
layer. Figure compares the vertical distribution
of specific electrical resistivity, p wave velocity and temperature
for both boreholes and dates. At first glance, there seems to be
a mismatch between resistivity and temperature regarding ALT for
SH. However, borehole temperatures at SH in August show constant
values at the freezing point between 1 and 3 m depth (between
2 and 4 m in October), the deeper level being the depth of the
sharply increasing resistivity values. As resistivity is sensitive to
the liquid water content, its values will not increase significantly
before most of this liquid water has been frozen, coinciding with
a temperature increase to values below the freezing point
e.g.,. Due to the higher water/ice content in SH,
this phenomenon (∼ vertical zero-curtain) is only seen in SH and
not in DP.
The two low resistive areas (34–40 and 53–60 m), already
visible in August and more pronounced on the second ERT
profile in October, are interpreted as the preferential water flow
path. Since the melt water cannot infiltrate through the two ice-rich
(high resistive) bodies close by (at 20–33 and 40–52 m
horizontal distances), it is forced to follow a preferential path in-between. The lower infiltration area (53–60 m) is constrained in the upper part
by the ice-rich zone and in the lower part by the presence of bedrock
near the surface.
Finally, the displacement of the high resistive area observed near SH
(blue zone at depth on the time-lapse tomogram) is most likely an
inversion artefact (overcompensation) due to the appearance of the low
resistive area in the second ERT profile cf..