TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-1531-2018Ground thermal and geomechanical conditions in a permafrost-affected
high-latitude rock avalanche site (Polvartinden, northern Norway)Conditions in a permafrost-affected
high-latitude rock avalanche siteFrauenfelderRegularf@ngi.nohttps://orcid.org/0000-0002-3129-5337IsaksenKetilhttps://orcid.org/0000-0003-2356-5330LatoMatthew J.NoetzliJeannettehttps://orcid.org/0000-0001-9188-6318Norwegian Geotechnical Institute NGI, Oslo, 0806 NorwayThe Norwegian Meteorological Institute, Oslo, 0313, NorwayBGC Engineering Inc., Ottawa ON, CanadaWSL-Institute for Snow and Avalanche Research SLF, Davos, 7260, SwitzerlandFormerly at: Norwegian Geotechnical Institute NGI, Oslo, 0806 NorwayFormerly at: University of Zurich, Zurich, 8057, SwitzerlandRegula Frauenfelder (rf@ngi.no)27April20181241531155029September201615November20168February20185March2018This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/12/1531/2018/tc-12-1531-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/1531/2018/tc-12-1531-2018.pdf
On 26 June 2008, a rock avalanche detached in the northeast facing slope of
Polvartinden, a high-alpine mountain in Signaldalen, northern Norway. Here,
we report on the observed and modelled past and present near-surface
temperature regime close to the failure zone, as well as on a subsequent
simulation of the subsurface temperature regime, and on initial geomechanical
mapping based on laser scanning. The volume of the rock avalanche was
estimated to be approximately 500 000 m3. The depth to the actual
failure surface was found to range from 40 m at the back of the failure zone
to 0 m at its toe. Visible in situ ice was observed in the failure zone just
after the rock avalanche. Between September 2009 and August 2013, ground
surface temperatures were measured with miniature temperature data loggers at
14 different localities, close to the original failure zone along the
northern ridge of Polvartinden and on the valley floor. The results from
these measurements and from a basic three-dimensional heat conduction model
suggest that the lower altitudinal limit of permafrost at present is at
600–650 m a.s.l., which corresponds to the upper limit of the failure
zone. A coupling of our in situ data with regional climate data since 1958
suggests a general gradual warming and that the period with highest
mean near surface temperatures on record ended four months before the
Signaldalen rock avalanche detached. A comparison with a transient permafrost
model run at 10 m depth, representative for areas where snow accumulates,
strengthen these findings, which are also in congruence with measurements in
nearby permafrost boreholes. It is likely that permafrost in and near the
failure zone is presently subject to degradation. This degradation, in
combination with the extreme warm year antecedent to the rock failure, is
seen to have played an important role in the detaching of the Signaldalen
rock avalanche.
(a) Key map showing the location of the Polvartinden rock
avalanche site (red asterisk), the two weather stations, and the permafrost
station used in this study, as well as the location of the Nordnesfjellet
borehole site referred to in the text (all marked with blue dots). (Map
source: Copyright of Norwegian Mapping Authority/Statens kartverk);
(b) Signaldalen rock avalanche as seen from helicopter on
28 July 2009 (Photograph by courtesy of Gunnar Kristiansen, NVE);
(c) runout area of the Signaldalen rock avalanche in September 2011
(Photograph courtesy of Gunilla Kaiser).
Introduction
In the morning of 26 June 2008, a rock avalanche (cf. Hungr et al., 2014 for
nomenclature) detached from the northeast facing slope of Polvartinden (see
Fig. 1 and Sect. 2 for site description). The rock avalanche endangered two
farms and several recreation cabins, and it destroyed a considerable amount
of livestock pastures. A few hours after the event, the first reconnaissance work
was carried out, including failure zone assessment by means of visual
inspection from a helicopter. In situ ice was observed in the failure zone at
different depths during this visual inspection (ca. 20–30 m below the
pre-failure surface and, thus, clearly below the seasonal frost depth;
Fig. 2), i.e. clearly indicating permafrost. This observation, together with
the absence of typical pre-failure events (such as seismic activity,
intensive rainfall, or snow melt) that could have triggered the rock
avalanche, led to the hypothesis that warming or degrading of permafrost
could have played a role in the timing and the magnitude of the event. A year
after the event, a temporary temperature monitoring network was put in place
and repeated surveying of the failure zone and the adjacent slopes by means
of terrestrial laser scanning was initialized.
Visible in situ ice (encircled areas) observed in the rock avalanche
failure zone on 26 June 2008. The photograph was taken a few hours after the
event (Photograph by courtesy of Kjetil Brattlien, NGI).
Scientists have become increasingly aware that atmospheric warming has an
impact on the stability of mountain permafrost (e.g. Arenson and Jakob,
2017; Haeberli et al., 2010; Harris et al., 2009; Huggel et al., 2010; Jin et
al., 2000; Marchenko et al., 2007; Stoffel and Huggel, 2017). It seems that
mountain systems are especially sensitive to a changing climate, due to
feedback effects in connection with snow cover, albedo, and heat budgets,
which amplify the alterations caused by climate change (Gobiet et al., 2014;
Vavrus, 2007; Wang et al., 2014). Several studies from the European Alps show
that temperatures have increased twice as much as the global average since
around 1900 (e.g. Böhm et al., 2001; Auer et al., 2007). There is also
increasing evidence that a link exists between rockfall magnitude and
frequency, and timing and depth of permafrost degradation, the latter ranging
from seasonal increase of active-layer depths to long-term, deep-seated
warming of the permafrost body as a response to atmospheric temperature rise
(e.g. Gruber and Haeberli, 2007; Ravanel and Deline, 2010; Stoffel et al.,
2014). Fischer et al. (2012) collected published material on large rockslide
and rock avalanche events (volumes between 105 and 107 m3),
as well as on more frequent small-volume rockfall events. Their study
concludes that such events occur worldwide in periglacial environments, and
that many of the reviewed studies suggest that the reported events may be
related to ongoing and/or past changes in permafrost and glacierization.
Corresponding events have, for example, been documented for the European Alps
(e.g. Barla et al., 2000; Crosta et al., 2004; Gruber et al., 2004a; Cola,
2005; Deline et al., 2011; Oppikofer et al., 2008; Ravanel and Deline, 2010;
Phillips et al., 2016), Canada and Alaska (e.g. Evans and Clague, 1994;
Geertsema et al., 2006), the Caucasus (e.g. Haeberli et al., 2004), and for
New Zealand (e.g. Allen et al., 2011). The evidence of permafrost-related
destabilisation is not only derived from observation (through abductive
inference), where the relative importance of permafrost degradation remains
difficult to prove, but also supported by (deductive) mechanical studies that
explain the underlying physics and processes of destabilisation (cf., e.g.
Arenson and Springman, 2005; Arenson et al., 2007; Davies et al., 2001;
Dräbing et al., 2013; Dwivedi et al., 1998, 2000; Günzel, 2008; Inada
and Yokota, 1984; Jia et al., 2016, 2017; Krautblatter et al., 2013; Mellor,
1973).
In mainland Norway (i.e. excluding Svalbard), permafrost occurs mainly in
the central mountain chains and in higher latitude areas, such as the
counties of Troms and Finnmark (Gisnås et al., 2017). Early studies,
e.g. King (1986), Ødegård et al. (1996), Etzelmüller et
al. (2003) and Heggem et al. (2005) have shown that permafrost is
discontinuous in the higher mountains of central southern and eastern Norway.
More recent studies show that permafrost temperatures generally are between
-3 and 0 ∘C in the higher mountains of northern Norway (e.g.
Farbrot et al., 2011) and that warming and degradation of the permafrost is
ongoing both at sites with cold permafrost, with marginal permafrost and with
deep seasonal frost (Isaksen et al., 2011).
In western and northern Norway, fjords and valleys are confined by steep
mountain slopes, which leads to a limitation of areas where human
infrastructure can be built; this often results in the infrastructure being
located close to the steep mountainsides. Large-scale rock-slope failures
therefore pose a significant geohazard in these areas (Blikra et al.,
2006), both due to the potential risk of a direct hit in case of a slope
failure, but also due to the potential risk of rock slope failures generating
tsunamis in the fjords or damming up rivers in the valley floors, with the
subsequent risk of major flooding. Mass movements generated from such slopes
and their secondary effects have caused hundreds of fatalities during the
last 500 years in Norway. Understanding of the destabilization processes and
the classification of their risk has thus become a major field of
research (Hermanns et al., 2012, 2013, 2016).
Due to the increased awareness of the potential role of permafrost
degradation to the risk of slope failures, a permafrost and climate
monitoring programme was initiated in 2002, along climate and altitudinal
transects in Troms and Finnmark, the two northernmost counties of mainland
Norway (Isaksen et al., 2008; Farbrot et al., 2013). In 2003, a permafrost
programme was launched in the Gaissane Mountains in the northernmost county
of
Finnmark (Farbrot et al., 2008). In the mountains of Troms two 30–32 m deep
boreholes were drilled in 2004 at altitudes of 786–850 m a.s.l. (Farbrot
et al., 2013). These studies found permafrost to be warm, but widespread in
alpine areas in northern Norway. The existing monitoring network in northern
Norway was more extensively instrumented and extended with new boreholes in
Troms and Finnmark during the third International Polar Year 2007–2009 (cf.,
Christiansen et al., 2010; Juliussen et al., 2010). Through extensive
analyses of these borehole data, Farbrot et al. (2013) found that the
combined effects of snow depth and vegetation cover are the two most critical
factors for the existence of permafrost in northern Norway. They also
concluded that the depth of seasonal frost or active layer in areas underlain
by exposed bedrock was more than 10 m at several sites and was amongst the
deepest reported in the international literature, and that the altitude of
the lower limit of permafrost has probably increased around 200–300 m since
the end of the Little Ice Age (LIA) (Farbrot et al., 2013). This was
supported in a recent study by Myhra et al. (2015) who modelled permafrost
distribution and long-term thermal changes in, among others, a steep rock
wall at Revdalsfjellet. Revdalsfjellet is situated close to the unstable and
extensively monitored Nordnesfjellet – where permafrost has been suggested
as one of the mechanisms controlling the ongoing slope failure (Blikra and
Christiansen, 2014; Blikra et al., 2015) – and 25 km from our Signaldalen
site. Myhra et al. (2015) modelled a bedrock warming at 20 and 50 m depth of
about 1.0 and 0.5 ∘C, respectively, from the end of the LIA and
using a present lower limit of permafrost of about 650 m a.s.l. at
Revdalsfjellet. A new Nordic permafrost map based on modelled results confirm
ongoing degradation of mountain permafrost in the fjord areas in Troms
(Gisnås et al., 2017).
In this paper, we present analyses of a four-year temperature data series
from near-surface temperature loggers, subsequent modelling of the
temperature regime, and basic geomechanical mapping in Signaldalen, northern
Norway, at a high-latitude, high-relief rock avalanche site. The main
objective of our study is to increase our understanding of the permafrost
temperature regime at the site and what role the ground temperatures might
have played in the release of the rock avalanche. Further, our results
contribute to the understanding of the influence of solar radiation on near
surface temperatures in different aspects of steep mountain walls at high
latitudes.
Site description
Polvartinden is a 1275 m high mountain located in the Signaldalen valley,
Troms county, northern Norway (at 69∘10′18′′ N,
19∘57′47′′ E). The failure zone is located approximately 600 m
above the valley bottom, on the western side (facing north-east) of the valley. The larger area around the failure
zone, outside the steepest slope, is characterized by a combination of small
vertical rock outcrops and undulating slopes with an established soil cover.
Figure 1 shows the location of the Polvartinden rock avalanche site, the two
weather stations, and the permafrost station used in this study, as well as
the location of the Nordnesfjellet borehole site referred to in the text.
Bedrock geology and tectonics
The Signaldalen area is mostly composed of metasedimentary rocks of the
Caledonian Nappe sequence. The latter were placed upon the Precambrian
basement in the Early- to Mid-Palaeozoic time period as a result of the
closure of the Iapetus Ocean. Series of flat-lying north-east–south-west striking nappes were
pushed upon the Precambrian basement from the north-east during the Caledonian orogeny
(Andresen, 1988; Fossen et al., 2007). The different nappes represent
different degrees of displacement and metamorphism, with an upward increase
in the degree of metamorphism (Roberts and Gee, 1985; Roberts, 2002; Fossen
et al., 2007). An era of extensional tectonics followed the Caledonian
orogeny and caused the opening of the Atlantic Ocean and development of the
Norwegian passive continental margin (Fossen et al., 2007). Several phases of
extension forces have resulted in different fault systems; those can be
tracked both onshore and offshore (e.g. Bergh et al., 2007; Hansen, 2009).
The western side of the Signaldalen valley, i.e. the side where the rock
avalanche released, is characterized by large-scale faulting. Regionally, the
layers are striking towards the south, and dipping to the north-west. Locally, around the failure
area, the rock boundaries lie almost horizontally (Fig. 1b). In addition,
there is a near surface-parallel surface where the failure has occurred.
According to the 1 : 250 000 geological map of the area
(http://geo.ngu.no/kart/berggrunn_mobil/) the failure zone is located
within a layer of calceous mica schists and calceous silica gneiss.
Glaciation and deglaciation history, postglacial uplift
The present regional morphology, topographic gradient and landscape, together
with the ongoing isostatic uplift, are the result of hundreds of million
years of geological evolution. During each new glaciation, glacier-products
from former glaciations, as well as post-glacial deposits, were modified or
totally removed. The last glaciation, the Weichselian (ca. 118 000 to
11 500 years ago) is therefore particularly important for the morphology of
the study area today. The Last Glacial Maximum (LGM) was reached between ca.
25 000 to 18 000 years ago (Sveian, 2004; Nesje, 2012). During this time,
the ice sheet covered almost all of Scandinavia, and reached the shelf edge
where it deposited great amounts of sediments (Vorren and Mangerud, 2007),
with the larger fjords serving as main drainage pathways (Sveian and Corner,
2004).
Climatic change at the end of the LGM caused the retreat of the ice shield
from the shelf edge. During the first period of the glacial retreat, the ice
shield withdrew from the shelf edge to the outer coast areas, and later into
the fjords (Vorren and Mangerud, 2007). Postglacial uplift in Fennoscandia is
a direct response to the deglaciation after the LGM. The landscape exposed
after the last glaciation was in an unstable condition, and thus particularly
exposed to modification at high denudation rates. Glacially steepened rock
walls and slopes were sites of mass reworking (Ballantyne, 2002). Recent
studies reveal a rapid rock slope instability response to the initial local
decay of the Scandinavian ice sheet followed by a lower and constant
frequency following the climate optimum in the Holocene (e.g. Hermanns et
al., 2017; Schleier et al., 2017). In northern Norway, two periods of major
seismicity (earthquakes on the order of ≥ 6.0 Mw) have been
suggested, due to the stresses following the postglacial uplift: one before
11 000 14C years BP and one between 10 000 and 9500 years BP. These
seismic events probably explain the high number of rock avalanches in the
region (Dehls et al., 2000).
(a) Typical logger installation setup for the M-Log5W
loggers. (b) Map showing temperature measurement locations in
vertical rock faces (red squares), within soil material (yellow triangles),
and within a stone cairn (one blue circle in the valley bottom). The three
black crosses mark the laser scanning locations. Rock avalanche outline in
blue. Map source: Copyright of Norwegian Mapping Authority/Statens kartverk.
MethodsGeomechanical mapping
In order to map the thickness and the geometrical characteristics of the
failure zone, the release area was repeatedly surveyed by terrestrial laser
scanning (TLS). This allowed also for monitoring of eventual continued or
new movement in the slope. In the autumns of 2010, 2011, and 2013, the
failure zone of the rock avalanche was imaged with an HD Optech ILRIS
terrestrial laser scanner (Optech Incorporated, Canada). In addition, the
failure zone and its surroundings were imaged with a Giga-Pan camera in
autumn 2013. The purpose of the laser scanning data collection was to
develop a digital terrain model to enable the quantification of the extent
and volume of the failure zone. The data needed to be of significant
resolution to enable the detection of temporal surface changes, and to
identify zones with differential movement.
The altitude of the failure zone in combination with the slope of the
mountain side resulted in the failure zone being located approximately
800–1100 m away from the nearest possible scanning locations. To produce a
3-D terrain model with a minimal amount of occlusion, data was collected from
three independent locations (identified in Fig. 3). Each location was
selected because it provided an excellent line-of-sight to the failure zone
and reduced the bias in extraction of discontinuity orientations in
accordance with Lato et al. (2010). Data was collected at varying
resolutions. Low-resolution data was collected to enable the visualization of
a large area of the mountainside. Higher resolution data was collected at
specific zones near the failure zone and its surrounding areas to enable a
geomechanical interpretation. This data has a resolution of approximately
0.20 m point spacing (or 25 pts m-2). Given the scanning distance,
this was the highest reasonable resolution of data we could collect at the
time with the available lidar technology. Occluded zones on the rock face
were limited to surfaces parallel to the scanner orientation and regions with
ice cover. The size of these regions varied from 1 to 10 m2. These
regions do not negatively impact the geomechanical interpretation since they
represent less than 1 % of the total model area. The orientation of
structural discontinuities were manually identified and orientation vectors
were converted to dip and dip direction measurements (Lato et al., 2012). The
structural measurements were extracted from a 3-D surface mesh that was
generated from the overlapping point cloud datasets.
Calculating the volume of the failed mass was completed based on
interpolation and estimation. Since a sufficiently high resolution 3-D image
of the area before the failure does not exist, an interpolated surface had to
be used to estimate the volume. The extent of the pre-failure surface was
delineated based on the visual contrast between the appearance of the
pre-failure and post-failure rock mass in the photographs and based on
intensity values in the lidar data set. The volume estimation was completed
using both PolyWorks and ArcGIS software packages. The volume of the 2008
rock avalanche was computed using the 2012 TLS data by delineating a
pre-failure surface based on adjacent slope topography bounding the scarp.
The differential volume was computed using standard
3-dimensional techniques, as presented, e.g. by Lato et al. (2014).
Temperature measurements and ground temperature modelling
In situ measurements of ground surface temperatures and subsequent ground
temperature modelling was employed to get insight into the past- and present
temperature regime at and near Polvartinden peak. Due to the two different
types of small-scale topography found around the failure zone (see Sect. 2
for site description) snow cover and snow depth varies considerably.
Consequently, temperature loggers were installed in both types of terrain,
i.e. in vertical rock outcrops (rock surface temperature, RST) and within
soil material on the more gentle slopes (soil surface temperature, SST). To
obtain a robust estimate of how past and present ground temperatures had
evolved prior to the failure, i.e. in the long- and in the short-term, two
different approaches were used: Firstly, the RST in selected rock outcrops
were linked to long-term changes in regional air temperature by comparing
their statistical relation with temperature data from the official weather
stations Skibotn (5 m a.s.l.) and Ripojavri (502 m a.s.l.); for locations
see Fig. 1. We found that the linkage between those two datasets was
strongest for rock outcrops with little or no snow, i.e. with a fairly direct
link to the atmospheric conditions. Therefore, it was desirable to look
further into the temperature development in the ground in more gentle slope
areas close to the release area with a more established snow and soil cover.
To this aim, the CryoGrid2 model (cf. Westermann et al., 2013) was used.
Since Polvartinden is an alpine peak, with three-dimensional effects that
affect the ground temperature field inside the mountain, the lower permafrost
limit will vary according to different aspects. In order to better understand
the present subsurface temperature field of Polvartinden and to get a better
idea of how the lower permafrost limit is located with respect to the release
area of the rock avalanche, a stationary three-dimensional transient heat
conduction model (cf. Noetzli et al., 2007a, b) was used in addition. It was
natural to install loggers in different compass directions to study the
differences in surface temperature as a result of aspect dependency. As input
data for the analyses of aspect dependency robust estimates of air
temperature were needed at each rock wall logger locality to enable the
calculation of the temperature difference (ΔT) between calculated air
temperature and measured rock face temperatures (surface offset).
Ground surface and air-temperature measurements
Ground surface temperatures were measured at 14 locations employing
miniature temperature data loggers of three different types: M-Log5W loggers
(GeoPrecision GmbH, Germany), redesigned UTL-1 loggers (University of Bern
and University of Zurich, Switzerland, cf. Gruber et al., 2003), and UTL-3
loggers (GEOTEST and WSL Institute for Snow and Avalanche Research SLF,
Switzerland). We followed the installation setup described by Gruber et
al. (2003), hence measuring near-surface rock temperatures at 10 cm depth
(Fig. 3a). The absolute accuracy for all three logger types is
±0.1 ∘C. The resolution of M-Log5W, UTL-3 and UTL-1 is 0.01,
0.02 and 0.27 ∘C respectively. The M-Log5W loggers and the UTL-3
loggers were programmed to measure every 30 min, while the UTL-1 loggers
(having smaller memory capacities than the other two employed logger types)
measured every 2 h. Based on experiences from long-term permafrost
monitoring programs in Norway using such loggers (e.g. Isaksen et al., 2011;
Gisnås et al., 2017) we claim that the bias on the accuracy of the
temperature measurements introduced with the given setups is negligible.
Installation sites for RST were chosen based on the availability of
near-vertical rock outcrops, their closeness to the original failure zone
and, last but not least, the accessibility of the identified locations. Where
possible, a vertical distance of several metres to the flat terrain was
chosen. However, this was not possible at all of the sites, which has some
implications on the interpretation of the results (cf. Sect. 4). The
14 measurement sites finally chosen are plotted on the map shown in Fig. 3b;
they are located along the north-north-west ridge of Polvartinden and in the valley
ground.
Measurements were ongoing from September 2009 to August 2013 for 10 out of 14
loggers. For R09 and R10 only data for 2009–2011 exists, while R12 and R13
where first installed in 2011, thus yielding data for 2011 to 2013. Nine
RST-loggers (type M-Log5W and redesigned UTL-1; identified as R# loggers
in text and figures) were installed in vertical rock faces on rock outcrops
and along small cliffs with different aspects, while five SST-loggers
(standard UTL-1/UTL-3; identified as S# loggers in text and figures) were
placed directly into the soil at ca. 10 cm depth in order to measure SST.
One additional logger was placed in a cairn in the valley floor to monitor
air temperature. For several of the analyses presented annual mean values
were calculated to identify variations in mean annual rock- and soil surface
temperature (MARST and MASST respectively) and mean air temperature (MAAT).
This ensures easier comparison between the monitoring sites and makes it
easier to identify local maxima and minima as well as trends (cf. Isaksen et
al., 2011).
Ground surface temperature modellingLong-term changes in ground temperatures
To study long-term changes in ground temperatures representative for the same
elevation as the failure zone, but for sites with a developed soil cover and
where snow accumulates, we used data series from the transient permafrost
model CryoGrid2 (CG2; cf. Westermann et al., 2013). The physical basis and
operational details of CG2 are documented in Westermann et al. (2013), and
only a brief overview over the model properties is given here. CG2 calculates
ground temperatures according to Fourier's law of conductive heat transfer in
the soil and in the snowpack to determine the evolution of ground temperature
over time. Thus, CG2 can deliver the transient response of ground
temperatures to a changing climate. In addition to conductive heat transfer,
the change of internal energy and temperature in the ground is determined by
the latent heat generated/consumed by soil freezing/thawing. Subsurface
movement of water is not included and only heat flow in the vertical
direction is considered, thus solving an effective 1-D problem and neglecting
lateral heat flow between neighbouring cells. This is justified for grid cell
sizes considerably larger than the extent of the vertical modelling domain
(cf. Westermann et al., 2013).
The model is forced by operational gridded (1 × 1 km) air
temperature (Lussana et al., 2018; Tveito et al., 2000) and snow-depth
(Engeset et al., 2004; Saloranta, 2012). Snow cover data is based on the
seNorge snow model (www.senorge.no) that uses gridded observations of
daily temperature and precipitation as its input forcing, and simulates,
among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk
density (ρ) (Saloranta, 2012). The gridded air temperature for our site
is mainly driven by the nearby Skibotn and Rihpojavri weather stations which
were validated against our local measurements (see Sect. 3.2). The gridded
precipitation for our site is mainly driven by observed precipitation at
Skibotn weather station. Since we have no observations of snow cover and
owing to the large spatiotemporal variability of snow conditions in our
alpine study area, the snow simulations from the seNorge model provide
probably the best estimate of the spatial average 1×1 km snow
conditions for our site (cf., Saloranta, 2012). A grid cell covering
Polvartinden and similar in elevation (665 m a.s.l.) as the failure zone
was selected. In addition, a grid cell covering the valley floor was selected
for validation against our observations. The model parameters for the lower
boundary condition and for ground properties were chosen as in Westermann et
al. (2013). The surface geology was based on the major surface sediment
classification by the Norwegian Geological Survey (NGU, 2010; Thoresen,
1990). For our study sites and the selected grid cells, the sediment
stratigraphy was classified as till and coarse colluvium (class 11 according
to the sediment map by NGU, 2010) and followed default settings in CG2 with
volumetric fractions of the soil constituents and soil type for each layer as
given in Westermann et al. (2013). An interval of snow thermal conductivity
(ksnow) was regarded as parameter uncertainty by Westermann et
al. (2013), and was used as a confining range for the true conditions as a
low (LC, ksnow= 0.3 W m-1 K-1) and a
high (HC, ksnow= 0.5 W m-1 K-1)
conductivity scenario run of CG2. For validation, one year of daily SST data
from the CG2-model was compared with observed SST-data from our valley and
mountain sites. To study long-term changes in ground temperatures and to
avoid dominance of near-surface high-frequency temperature variations we
selected 10 m depth as an appropriate depth. For more details on the CG2
model, please refer to Westermann et al. (2013).
Subsurface temperature field
We employed a transient three-dimensional heat conduction model to get
insights into the general pattern of the present subsurface temperature field
of Polvartinden. The applied model is discussed in detail in Noetzli et
al. (2007a, b) and Noetzli and Gruber (2009) and is basically a finite
element model including Fourier's law and phase change via an apparent heat
capacity (cf., Mottaghy and Rath, 2006). Where possible, the model was fed
with local datasets. The geometry for the simulation was based on a 10 m
digital terrain model for the surface topography of the entire mountain and a
rectangular box of 1000 m height for the subsurface mass. Values defining
the subsurface properties (heat capacity, thermal conductivity, porosity; cf.
Table 1) were obtained from sites nearby (cf. Lilleøren et al., 2012).
Especially for porosity, these values can vary significantly and have quite
an influence on the thermal regime. However, due to the similar geological
setting and proximity of our site to the sites described in Lilleøren et
al. (2012), we assume these values to be fairly representative for our study
location. The geothermal heat flux as the lower boundary condition was set to
60 mW m-2 (Slagstad et al., 2009). The upper boundary condition was
set as a fixed temperature with annual mean values from the distributed
MARST. Distributed MARST was calculated
based on the aspect dependency of MARST. For this, the fit curve from the
first two years of data (October 2009–November 2010, cf. Sect. 4.3) was
calculated. MARST changes in the steeper parts of the mountain were assumed
to have roughly followed changes in MAAT. The
regional LIA glacier maximum is suggested to have occurred about 1900–1910
(e.g. Ballantyne, 1990; Bakke et al., 2005). Therefore, the time-dependent
simulation was started with a steady state temperature field for the MARST in
1900, and we assume a linear MAAT increase of 0.55 ∘C until the end
of the simulation (Lilleøren et al., 2012), i.e. the year of the rock fall
event. Our results are only valid for areas that are assumed not to be
influenced by a snow cover, i.e. the steep rock-faces of Polvartinden.
Values for subsurface properties as used in the stationary
three-dimensional transient heat conduction modelling.
Subsurface propertyValue (value range)Heat capacity850 J kg-1 K-1Thermal conductivity2–2.5 W m-1 K-1Density2800–2900 kg m-3Water content0.2–1 %
The failure zone of the Signaldalen rock avalanche (outlined with
black stippled line) is a complex wedge much deeper to the plane of failure
at its back than at the front. The colours indicate the distance in metres to
the plane of failure.
Lapse rates
To study the surface offset and local influences on air temperature lapse
rates for our RST locations, we studied the inter-annual variability from
2009–2013 in monthly mean lapse rates. In the absence of local air
temperature measurements at higher elevation (similar to the rock wall
loggers), the monthly lapse rates were calculated based on the nearest
mountain weather station located at Rihpojavri (502 m a.s.l., Fig. 1a) and
our local air temperature measurement site in Signaldalen valley
(65 m a.s.l.).
ResultsGeomechanical characteristics
Based on the laser scanning results, the volume of the rock avalanche was
estimated to be approximately 500 000 m3. These results confirm
earlier estimates suggested during the emergency response work initiated
directly after the event NGI (2008). The depth to the failure surface was
found to range from 40 m at the back of the failure zone to 0 m at its toe,
with the failure zone being a complex wedge (Fig. 4). Understanding the main
mechanism behind the failure involves a kinematic evaluation of the failure
scarp. The extraction of the orientation of the basal failure surface and the
orientation of the natural slope (“Pre-Failure Surface”) are determined
directly through measurements using the laser scanning data. Due to the
hazardous rock slope, direct measurements from the failure scarp could not be
obtainable. The basal failure plane has been identified from the terrestrial
laser scanning data and from interpretation of the Giga-Pan photography (cf.
Sect. 3.1). Evaluation of the basal sliding plane showed that it is dipping
perpendicular to the rock slope at an average angle of 40∘, which is
less than the intact rock slope. Based on an approximate friction angle of
30∘, the kinematic setting of the rock slope meets the requirements
of a sliding failure posing a potential rockfall hazard (see e.g. Hasler et
al., 2012; Goodman, 1995).
Mean annual rock and soil surface temperatures (all except A01-SD)
and air temperature (A01-SD) during the period September 2009 to August 2013
shown as simple moving 365-days average for all sites. To ensure that the
temperature variability was not shifted in time, the mean values were
centred (an equal number of days on either side of the mean value). A01-SD
is air temperature in Signaldalen (SD), R03 to R14 are the rock face loggers,
and S02 to S13 are the soil temperature loggers.
Scatter diagrams showing relations between monthly mean temperature
for the main air temperature and rock face temperature series, including
linear regression lines. (a) Relation between air temperature in
Signaldalen and at meteorological station at Skibotn; (b) relation
between air temperature in Signaldalen and air temperature measured at
Rihpojavri weather station (25 km from Signaldalen); (c) and
(d) relation between rock face temperature logger sites R05 and R06,
respectively, and air temperature in Signaldalen.
The surficial change of the exposed rock mass between 2011 and 2013 is mapped
through the comparison of laser scanning data collected at different points
in time. The maximum size of blocks released between 2011 and 2013 ranges
from 1 to 10 m3. In summary, the repeated laser scanning measurements
between 2009, 2011, and 2013 showed little to no rockfall activity, both
within the 2008 failure zone and in the adjacent rock slopes.
Measured mean annual ground surface temperatures
Measured RST and SST data series for the four-year measurement period were
smoothed with a 365-day running mean filter (Fig. 5). There is a slightly
higher variability for MAAT than for MARST and MASST (at ca. 10 cm depth),
but the overall correlation is high. MARST and MASST, measured during
2009–2013, were between -1.4 and +2.2 ∘C, with the highest
temperatures recorded between April 2011 and March 2012 (Logger R05-E). For
the vertical rock face sites, the lowest MARST was recorded at the north
facing site in a 365-day period between September 2009 and September 2010
(Logger R06-N).
The large inter-annual variability found in our temperature measurement
series is in congruence with general climate conditions in Troms and is
confirmed by measurements in nearby mountain slopes (Farbrot et al., 2013).
For the monitoring period 2009–2013, average, minimum and maximum MAAT at
the nearby Skibotn meteorological station (27 km to the north-north-east
from Signaldalen) were 0.0, 1.2 below and 1.5 ∘C above the MAAT for the normal period of
1981–2010, respectively.
During 2010 and 2011 some of the sites were clearly influenced by a snow cover
(cf. Table 2). Based on an analysis of wind direction, wind speed and total
snow accumulation at nearby weather stations (among others Skibotn), we
assume this difference in snow cover to be caused by inter-annual differences
in prevailing wind direction and preferential snow deposition.
We found a very high correlation between our air temperature measurements in
the valley bottom (2009–2013) and air temperature data covering the same
period from the nearest two meteorological stations at Skibotn (R2=0.99; Fig. 6a) and Rihpojavri (R2=0.97; Fig. 6b). Furthermore, we
found a very high correlation between our local air temperature measurements
in the Signaldalen valley floor and the rock wall loggers R05 (R2=0.99; Fig. 6c) and R06 (R2=0.98; Fig. 6d). The temperature series
from loggers R05 and R06 were chosen because they were the least influenced
by snow and they were also the “warmest” and “coldest” loggers,
respectively, in the four-year measurement period. Temperatures at logger
site R05 are about 1.3 ∘C higher than at the north facing series at
logger site R06. This good correlation allowed using the meteorological data
from Skibotn for modelling of the long-term evolution of MARST at R05 and R06
for evaluating the potential permafrost distribution near the original
failure zone on Polvartinden and recent RST changes by coupling our in situ
surface temperature data with regional and large-scale climate data.
Rock wall and soil temperature loggers installed in Polvartinden and
Signaldalen. Snow cover = estimated winter snow cover based on a simple
evaluation of day to day temperature variability and standard deviation
compared to reference logger R05, divided into three categories: (I) snow
cover mostly absent, (II) snow cover over short periods, (III) snow cover
over longer periods.
One year of observed soil surface temperatures (SST) at sites with
an established soil and snow cover compared with the modelled SST from the
transient permafrost model CryoGrid2 (CG2) for high (HC) and low (LC) thermal
conductivity of the snow: (a) time series of SST at site S02 –
placed next to the air temperature measurement location in the valley floor
– compared to the modelled CG2 series for the actual grid cell; (b)
SST series for the soil sites close to the release area (S02, S07 and S08)
compared to the corresponding grid cell with similar elevation as the release
area (i.e. 665 m a.s.l.).
Modelled mean annual ground temperatures
To be able to link our short temperature rock wall series to the larger
regional climate development, we compare it to synthetic temperature time
series based on the regressions presented in Fig. 6 and the CG2 simulations.
The CG2 model results are representative for our soil temperature sites in
the valley floor and for flatter areas at ca. 665 m a.s.l. (which is
roughly the altitude of the failure zone), where snow accumulates and where
our soil temperature loggers were installed.
A one-year comparison of observed daily SST at sites with an established soil
and snow cover and the modelled SST from the CG2 model is shown in Fig. 7. As
seen in Fig. 7a the SST observed in the valley floor is in good agreement
(R2=0.86 to 0.88) with the CG2 model results for a grid cell with
approximately the same elevation as the valley floor. The Signaldalen valley
is dominated by mountain birch forest and is characterized by an open forest
layer with heather and lichen species dominating the forest floor layer. This
type of forest causes snow to accumulate and insulates the ground against
strong cooling (cf. Isaksen et al., 2008; Farbrot et al., 2013), an effect
which can be seen in our CG2 model results. For the SST sites close to the
release area (Fig. 7b) there is a somewhat weaker correlation (R2=0.69
to 0.82) between observed and modelled data. The MASST are about 1 to
1.5 ∘C lower than in the CG2 model for the corresponding grid cell
with similar elevation. The warm bias of the simulations during winter is
mainly explained by the different snow conditions assumed in the model: while
our observational sites close to the release area are affected by
considerably strong snow cover variations induced by wind drift, the snow
model used as input in the CG2-model generally overestimates snow depths in
high mountain areas (cf. Saloranta, 2012). This is also in line with results
of an equilibrium permafrost model used by Gisnås et al. (2013) who found
a better agreement between the CG2-model and validation data when the snow
depth in the snow model data was reduced by 30 % for areas above the tree
line. During summer, on the other hand, our observed SST in the mountains are
well reproduced by the CG2 simulations.
The coupling of our coldest in situ RST data from R06 with the climate data
from Skibotn since 1958 (cf. Fig. 6) suggests that the highest MARST on
record was 1.1 ∘C and occurred in the 12-month period between
March 2007 and February 2008, i.e. ending four months before the Signaldalen
rock avalanche detached (Fig. 8a). A comparison with the CG2 model data run
at 10 m depth for the same period suggests a gradual warming and degradation
of the permafrost and supports our synthetic series with the warmest period
occurring just a few months prior to the failure. Figure 8b shows the recent
10-year period for the synthetic series and the four-year series of the in
situ MARST data for the lowest (R06) and the highest (R05) rock wall
temperatures, covering the range of the measured rock wall temperatures. The
figure also shows the two CG2 model runs. According to Westermann et
al. (2013) the thermal conductivity of the snow is the largest source of
uncertainty in CG2, thus a low (LC) and a high conductivity (HC) scenario run
of CG2 are used for the last 10 years as a confining range for the true
conditions. Note that the synthetic series are slightly warmer than the
series for R06 during the first two years. The deviation is likely due to a
thin snow cover that partly covered the rock face or outcrop, particularly
during the second winter of the monitoring campaign.
(a) Synthetic temperature series since 1958 of the coldest
rock wall site (R06) based on the regression presented in Fig. 6. Also shown
is the transient permafrost model CryoGrid2 (CG2) run that calculates ground
temperatures at 10 m depth according to conductive heat transfer in the soil
and in the snowpack (Westermann et al., 2013). The CG2 model results are
representative for areas at approximately 665 m a.s.l. with slope gradients
allowing for snow accumulation. The grey dotted line shows when the
Signaldalen rock avalanche detached; (b) The recent 10-year period
for the synthetic series overlaid on the respective in situ RST data for the
lowest (R06, blue lines) and highest (R05, red lines) rock wall temperatures.
Also shown are the CG2 model data runs at 10 m depth for high (HC) and low
(LC) thermal conductivity values of the snow.
Since 1958, the CG2 results clearly indicate a ground warming at 10 m depth.
Coincidentally, the period April 2011 to March 2012 was as warm as the
previous record from the early 1990s. On the other hand, our measurements
took place during the coldest period since 1988. Our measurement period
covers, in other words, most of the temperature regime that can be expected
in this region within a multi-decadal perspective. The depth of the fracture
zone varies between 20 to 40 m (Fig. 4). Temperature penetration from the
surface to such depths typically takes between one (20 m) to two years
(40 m) (e.g. Gruber et al., 2004b). Therefore, there is a good temporal
link between the maximum ground temperature at 20–40 m depth (through at
least the last 50–60 years) and the actual timing of the rock avalanche
release.
The general pattern of the transient 3-D-temperature field in the mountain is
illustrated with a 2-D transect from south to north (see Sect. 5). The
curvature of the isotherms inside the mountain stems from the topography and
is more pronounced the steeper the topography is (Noetzli et al., 2007a).
Since the difference in MARST is only on the order of 1.5 ∘C, the
isotherms are a little inclined towards the colder mountain flank. However,
the main temperature change in the subsurface is experienced with a change in
altitude (unlike in, e.g. steep peaks in the Alps, where the main
temperature change is experienced for positions between mountain flanks with
different temperature). Based on this schematic sketch of the subsurface
temperatures, the permafrost body underlies all of the steeper part of the
mountain and is several hundred metres thick at some places. In the area of
the rock fall starting zone more shallow permafrost is simulated.
Local lapse rate
As shown in Fig. 6 our regression analysis shows a high correlation between
the nearest mountain weather station located at Rihpojavri (502 m a.s.l.,
Fig. 1) and our local air temperature measurement site in Signaldalen valley
(65 m a.s.l., Fig. 3b). Figure 9 reveals an annual median lapse rate of
6.1 ∘C km-1, but with substantial seasonal and inter-annual
variability. Lapse rates are smallest (4–6 ∘C km-1) in
late-summer to early autumn, and largest (7–10 ∘C km-1) in
spring. We see the strongest gradients in spring (May).
Inter-annual variability between 2009–2013 of the monthly-mean lapse
rates based on air temperature data from Rihpojavri (502 m a.s.l.) and
Signaldalen (65 m a.s.l.). Boxes show the interquartile range of the
month's lapse rate, horizontal lines inside the boxes show the median values,
and the whiskers show the full range of the data.
The monthly temperature difference (ΔT) between calculated air
temperature and measured rock face temperatures are shown in Fig. 10. For the
sites R03, R04, and R12, periods during which the loggers obviously had been
covered by snow (cf. Table 2) were omitted. Monthly values were calculated in
the same way as used for the lapse-rate calculations (cf. Sect. 3.4, Fig. 9).
The results in Fig. 10 show a clear aspect dependency with a slightly lower
ΔT in northern exposition as opposed to slopes exposed to the south.
The figure also shows the year to year variations on the order of ±0.5 to
1.0 ∘C. There is a clear seasonal dependency, with ΔT near
0 ∘C or even negative during autumn and early winter, and largest
ΔT (1.5 to 5 ∘C) in late spring and early summer.
Figure 11a shows the annual temperature difference (ΔT) between
calculated air temperature and measured rock face temperatures and aspect
dependency as derived from the data. The ΔT for logger R06, which is
the logger facing most towards north (10∘), is +0.6 ∘C
compared to air temperature, while the two loggers facing most towards south
(R05, 90∘ and R09, 208∘) show both a ΔT of
+1.7 ∘C. We, thus find an aspect difference between north and
south facing loggers of 1.1 ∘C. Figure 11b shows the subsurface
temperature field as modelled with the stationary three-dimensional transient
heat conduction model based on rock wall temperature data from 2009–2011.
The figure shows a slice transecting Polvartinden mountain from south (left)
to north (right).
Monthly temperature difference (ΔT) between calculated air
temperature and measured rock face temperatures from 2009-2013. Calculated
air temperature is based on air temperature from Signaldalen (65 m a.s.l.)
and the lapse rates shown in Fig. 9. Boxes show the interquartile range of
ΔT. The red horizontal lines show the monthly-mean temperature
difference between the respective loggers and the north facing (i.e. the
coldest) logger R06. Loggers R03, R04, and R12 were snow covered during winter
and data from snow-influenced months were removed. For R09 and R10 only data
for 2009–2011 exist, for R12 and R14 only for 2011–2013.
Discussion
A uniform and/or constant theoretical lapse rate is often set to
6.5 ∘C km-1 (e.g. International Organization for
Standardization, 1975). However, the theoretical adiabatic lapse rate can
vary considerably (from 3 to 9 ∘C km-1 for surface conditions
at mid-latitudes) due to its dependency on pressure and temperature (Minder
et al., 2010). In valley bottoms, temperature inversions and cold air pooling
can affect lapse rates (e.g. Rolland, 2003), and channelled flow over
mountain passes can result in large local temperature anomalies (e.g.
Steenburgh et al., 1997).
(a) Annual temperature difference (ΔT) between
calculated air temperature and measured rock face temperatures and aspect
dependency as derived from the rock wall temperature data. The points
represent mean values and the black line is the best polynomial fit to the
data (R2=0.84). The grey lines show the polynomial fit to the
interquartile range of ΔT; (b) 3-D transient heat modelling
of the subsurface temperature field based on values identified in
(a). Figure shows a slice transecting the mountain from south (left)
to north (right).
Our study suggests that the high lapse rate values we find in Signaldalen in
spring are caused by the fact that the snow cover is normally depleted in
the valley bottom in late spring, while the higher areas and the northerly
exposed, moderately steep mountain slopes still exhibit an extensive snow
cover. These results are in accordance with international literature (e.g.
Minder et al., 2010) and are likely applicable to other mountainous areas in
northern Norway. Other studies have also shown that seasonal cycles in lapse
rates have similar amplitudes to those found in our study, but that the
phasing of the seasonality varies (Bolstad et al., 1998; Rolland, 2003; Tang
and Fang, 2006; Blandford et al., 2008; Gardner et al., 2009). They also
highlight the importance of local air temperature measurements in
experimental observational networks to reduce uncertainty. Our results
clearly support these findings.
Our measured MARST and MASST values from the period 2009–2013 indicate warm
and marginal permafrost at all temperature logger sites facing
north-northeast, i.e. in the same aspect as the failure zone. Our results
yield an estimated mean lower limit of permafrost at around
600–650 m a.s.l.; this value is in agreement with earlier estimates in the
inner fjord and valley areas of Troms (Farbrot et al., 2013; Gisnås et
al., 2017) and coincides with the upper limit of the failure zone. Since all
rock wall loggers are installed on small cliffs (rather than in vertical,
large rock walls) the snow close and/or at the top of these cliffs can
attenuate normal winter cooling and, thus affect the results during the
winter months. This is visible in Fig. 10, where some of the northeast facing
loggers exhibit clearly higher winter temperatures than what would be
expected when compared with air temperature. The influence of the snow cover
on the rock thermal regime has been studied in steep rock walls (Haberkorn et
al., 2015a, b; Hasler et al., 2011; Magnin et al., 2015). The highly variable
spatial and temporal distribution of the snow cover strongly influences the
ground thermal regime of steep rock faces (Haberkorn et al., 2015a, b; Magnin
et al., 2015). Haberkorn et al. (2015a) found that snow depths exceeding
0.2 m were enough to have an insulating effect on steep, bare bedrock. Such
amounts are likely to accumulate in steep, high rock walls with a certain
degree of surface roughness. As snow reduces ground heat loss in winter, it
has an overall warming effect on both north and south facing rock walls
despite the fact that it provides protection from solar radiation in early
summer (Haberkorn et al., 2017). However, in moderately inclined
(45–70∘) sun-exposed rock walls, Hasler et al. (2011) suggest a
reduction of MARST of up to 3 ∘C compared to estimates in
near-vertical, compact rock due to snow persistence during the months with
most intense radiation. In our study some of the soil logger sites feature
moss or thin vegetation (like the mountainside otherwise), which also affects
the temperature. Together, the loggers (including the soil loggers) reflect
the variation of the snow and surface conditions present around the
Signaldalen rock avalanche site.
Ground temperature time-series at 4 m depth from a nearby
permafrost borehole (Guolasjávri (Gu-B-1), 786 m a.s.l.; more details
can be found in Farbrot et al., 2013). The blue line shows the measured daily
values and the red line shows a simple moving 365-days average, i.e. the
unweighted mean of the previous 365 days. The grey dotted line indicates the
detachment date of the Signaldalen rock avalanche.
The long-term development in the annual mean temperatures from the
instrumental period (the late 19th-century) in northern Norway can be split
into four periods: a cold period in the beginning, a period referred to as
“early 20th century warming” culminating in the 1930s, a period of
cooling from the 1930s to the 1960s, and finally the “recent warming” from
the 1960s to present (Hanssen-Bauer et al., 2015). Regional climate data
since 1958 from Skibotn weather station suggest a general warming of the
greater Signaldalen area. This is in agreement with the general atmospheric
warming (Hanssen-Bauer et al., 2015) and observed permafrost temperature rise
(Isaksen et al., 2007), and the long-term permafrost degradation (Farbrot et
al., 2013) in northern-Norway, with an observed peak during the period
2007–2009 (Christiansen et al., 2010; Romanovsky et al., 2016). The CG2
model results suggest (not shown) an increase of the lower permafrost limit
for snow covered sites from ca. 600 m a.s.l. in the 1960s to about
800 m a.s.l. between 2000–2010 (Sebastian Westermann, personal
communication, 2015). Our modelled data (Fig. 8) and observed ground
temperature data from the nearby Guolasjávri permafrost borehole
(Fig. 12, see location in Fig. 1) suggest that the highest mean near surface
temperatures on record occurred in the period between March 2007 and
February 2008, thus ending only a few months before the Signaldalen rock
avalanche detached.
According to a study by Fischer et al. (2012) on potential triggering factors
at 56 historical rock avalanche and rockfall events in the Alps, it seems to
be the marginal permafrost zones where most of the recent changes concerning
ice content and hydrology have taken place; parameters that are seen as
having an important influence on slope stability (see also Allen and Huggel,
2013, Deline et al., 2011, and Fischer et al., 2013). In laboratory studies,
Davies et al. (2001) demonstrated that the shear strength of an ice-bonded
rock discontinuity significantly reduces with warming and that the minimum
shear stress is reached between ca. -0.5 and 0 ∘C. Krautblatter et
al. (2013) showed that fracture ice influences stability down to a depth of
approximately 20 m. Below this depth, the overburden pressure of the rock
mass becomes too high. According to their study, intact rock bridges and
rock–rock contacts exert additional control on rock stability. While
permafrost increases the uniaxial and tensile strength of such rock bridges
and rock–rock contacts, warming of permafrost decreases these strengths and
could thus trigger rock slope failures. In degrading permafrost,
rock-mechanical properties may control early stages of destabilization and
become more important for higher normal stress, i.e. higher magnitudes of
rock slope failure. Ice-mechanical properties outbalance the importance of
rock-mechanical components after the deformation accelerates and are more
relevant for smaller magnitudes (Krautblatter et al., 2013). In early summer,
the combined effect of hydrostatic and cryostatic pressure can cause a peak
in shear force exceeding high frozen shear resistance (Dräbing et al.,
2014).
Analysing temperature conditions prior to 144 past rockfall events in the
Swiss Alps and the French Mont Blanc massive, Lüthi et al. (2015)
recently showed that small to medium-sized rockfalls (with volumes up to
100 000 m3) mainly occurred during short-term periods of unusually
high temperatures, whereas larger high-elevation rock slope failures occur
all year-round. Hasler et al. (2011) showed that local warming of cold
permafrost may be induced by advection and the related erosion of cleft ice
and that permafrost degradation through thermal advection by running water
can rapidly lead to the development of deep thaw corridors along fracture
zones and potentially destabilise much larger volumes of rock than through
thermal conduction on similar timescales. Dräbing et al. (2016) found
that the rock mechanical regime also was snow-controlled in permafrost rock
slopes at Steintaelli in the Swiss Alps. They found that during snow-free
periods, high-frequency thermal expansion and contraction occurred. Rock
temperature locally dropped to -10 ∘C, resulting in thermal
contraction of the rock slopes. Snow cover insulation maintained temperatures
in the frost-cracking window and favoured ice segregation. Such repetitive
occurrence destabilises the rock slope and can potentially lead to failure
(Dräbing et al., 2016).
A recent synopsis of MARST at 34 locations within the PERMOS network in
Switzerland (Noetzli et al., 2016) shows that MARST are generally higher than
MAAT. In south-exposed near-vertical locations this difference amounts to up
to 10 ∘C. In north-exposed locations, MARST is only slightly higher
than MAAT (Noetzli et al., 2016), including steep alpine rock walls at the
Matterhorn and the Jungfraujoch in Switzerland (Hasler et al., 2011). Similar
values were also measured at the Aiguille du Midi in France (by Magnin et
al., 2015). In Scandinavia, the amount of studies using direct observations
to explore the influence of solar radiation on near surface temperatures in
different aspects of steep mountain walls is limited so far. In Jotunheimen,
southern Norway, north facing rock wall surfaces were on average less than
1 ∘C warmer than the surrounding air temperature, while MARST in
more radiation exposed rock walls was up to 4 ∘C higher than MAAT
(Hipp et al., 2014). Generally, the influence of direct solar radiation is
less pronounced at high latitudes than in mid-latitude mountains. Our results
indicate an altitudinal difference of roughly 200–250 m between northerly
and southerly aspect (cf. Fig. 11b), as compared to 1000–1500 m (or up to
8–10 ∘C) for some sites in the Swiss Alps (Noetzli et al., 2016).
Due to our small sample size, these results should be seen as tentative
estimates. Also, the potential effect of the midnight sun has not been looked
at. In addition, it has to be noted that we lack loggers exposed directly to
the south. Based on the shape of the polynomial fit curve (Fig. 11a), and on
what is known from other studies (e.g. Gruber et al., 2004b; Noetzli and
Gruber, 2009; Magnin et al., 2015; Noetzli et al., 2016), the difference
between northerly and southerly aspects is probably 0.2 to 0.4 ∘C
higher than indicated by our measurements. This would yield an absolute
difference between “warmest” and “coldest” aspects of approximately 1.3–1.5 ∘C, which is still considerably lower than for mid-latitude
mountain ranges and about 1.5 ∘C lower than reported by Hipp et
al. (2014) for a location in southern Norway, thus supporting strongly
decreasing aspect dependency with increasing latitude. Smaller differences in
MARST between north and south facing rock walls eventually result in less
pronounced 3-D effects of the subsurface temperature field as illustrated
with the modelling results of the subsurface temperature field (Fig. 11b).
For alpine peaks with a triangular geometry, isotherms can be nearly vertical
in the uppermost part of mountain peaks in the European Alps (cf. Noetzli et
al., 2007b), and temperatures change mainly with the position between north
and south facing slopes. In our results, isotherms are only minorly inclined,
and the main change in subsurface temperatures is experienced with changing
altitude and not changing exposition.
Conclusions
In June 2008, a rock avalanche detached in the northeast facing
slope of Polvartinden, a high-alpine mountain in Signaldalen, northern
Norway. The volume calculation based on terrestrial laser scanning data
showed that the depth to the actual failure surface was found to range from
40 m at the back to 0 m at the toe. Repeated laser scanning between 2009
and 2013 showed little to no activity in both the 2008 failure zone and the
adjacent rock slopes.
Analyses based on four-year rock and soil surface temperature series suggest
warm and marginal permafrost at several of the investigated sites in Signaldalen,
and yield an estimated mean lower limit of permafrost at around
600–650 m a.s.l., an altitude which coincides with the upper limit of the
failure zone. Regional climate data since 1958 and nearby permafrost borehole
data suggest a general warming and that the highest mean near surface
temperatures on record occurred some months before the Signaldalen rock
avalanche detached. These findings are supported by model results of the
transient permafrost model CG2. Considering that temperature penetration to,
e.g. 15–20 m depth in frozen rock typically takes one year it is likely
that changing rock and ice-temperatures due to the general warming and in
response to the extreme warm previous year have played an important role in
the detaching of the Signaldalen rock avalanche.
Our results give also new insights into aspect dependency of mountain
permafrost in northern Scandinavia, a subject that has been little explored
so far. We found an absolute difference in ground surface temperatures of
approximately 1.3 to 1.5 ∘C between southern and northern exposed slopes,
values considerably lower than reported from studies in mid-latitude mountain
ranges.
The meteorological data analysed in this paper are
available on https://frost.met.no/index.html. All other temperature
data, as well as the lidar data, are available upon request to the first
author.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “The evolution of
permafrost in mountain regions”. It is not associated with a
conference.
Acknowledgements
The work of Regula Frauenfelder and Matthew J. Lato was funded by the
Norwegian Research Council (through its base funding to NGI) and the work of
Ketil Isaksen was funded through the Norwegian Meteorological Institute, all
this funding is gratefully acknowledged. Michael Krautblatter and
three anonymous reviewers are gratefully acknowledged for their thorough and
helpful reviews which greatly improved the final paper. We also express our
thanks to editor Christian Hauck who contributed with important comments and
suggestions to further improve the manuscript. We are indebted to Leif Skogli
(Signaldalen) who let us install loggers on his property and helped with the
logistics during the laser scanning campaigns. In addition, we would like to
thank Steinar Engsted (former employee at Storfjord municipality) for his
interest in our work. Kjetil Brattlien (NGI), Gunnar Kristensen (NVE), and
Gunilla Kaiser are thanked for letting us use their photographs.
Herman Farbrot, Gunilla Kaiser, Kristin Sæterdal Myhra, and Helge
Smebye are acknowledged for
their help in the field. Last but certainly not least, Sebastian Westermann
(UiO) deserves a great thanks for running his CG2 model for us and letting us
use the according results.
Edited by: Christian Hauck
Reviewed by: Michael Krautblatter and three anonymous referees
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