Underestimated permafrost landforms – Block and talus slope distribution in the Dry Andes of Argentina

Abstract

There is a lack of explanatory approaches and analytical data on distribution patterns and formative controls of block- and talus slopes in the Dry Andes of Argentina, despite these being widespread and characteristic elements of the extensive periglacial belt. By using geomorphological mapping and statistical evaluation, spatial distribution patterns of these underestimated landforms are captured and analyzed, thus providing the foundation for further analyzes such as geophysical prospection and upscaling.

Full text

https://doi.org/10.52381/ICOP2024.151.1
Underestimated permafrost landforms – Block and
talus slope distribution in the Dry Andes of Argentina
Tamara Köhler1, Anna Schoch-Baumann1, Rainer Bell1, Diana Agostina Ortiz1,
Philipp Reichartz1, Lothar Schrott1 & Dario Trombotto Liaudat2
1Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115
Bonn, Germany
2Geocryology, IANIGLA, CCT CONICET Mendoza, Argentina
ABSTRACT
There is a lack of explanatory approaches and analytical data on distribution patterns, landform characteristics and formative
controls of block and talus slopes in the Dry Andes of Argentina, despite these being widespread and characteristic elements of
the extensive periglacial belt. Their spatial distribution, internal structure and ice content have received far less attention than
rock glaciers, which are included in regional and national inventories and have been studied systematically in the last decades
(e.g., IANIGLA-CONICET, 2018). We extend these inventories by block and talus slopes based on a remote-sensing and field-
based geomorphological mapping approach in the Agua Negra catchment (30°S, 69°W). Three sub-catchments were chosen
for the manual mapping approach, reflecting the overall heterogeneity of the study area and ensuring transferability. Analysis of
the uppermost sub-catchment alone reveals that only 1.6% of the surface are covered by rock glaciers, while block and talus
slopes cover almost 80%. These landforms are by far the most dominant geomorphic features in the basin, typical in this part of
the Andes and probably—in terms of surface area, vertical extension and permafrost distribution—unique in mountain systems
worldwide. By using geomorphological mapping and statistical evaluation, spatial distribution patterns are captured and
analyzed, thus providing the foundation for further analyses such as geophysical prospection and upscaling. Especially in light
of global climate change, understanding the spatial distribution of potentially ice-rich permafrost landforms is imperative to
assess available water resources, water quality and their evolution.
1 INTRODUCTION
The Andean cryosphere is an important hydrological
reservoir that controls runoff and groundwater recharge
through glacier melt, precipitation, snowmelt, and water
releases from the active layer in permafrost areas
(Masiokas et al. 2020; Halla et al. 2021). Especially in
drought-prone areas with limited glaciation, periglacial
landforms are hydrologically significant (Schaffer et al.
2019; Masiokas et al. 2020). Their delayed response to
global warming will even increase their importance in the
future (Schaffer et al. 2019; Arenson et al. 2022). The
observed rapid glacier retreat, the significant decrease in
snowfall and snow persistence as well as the increased
permafrost degradation in the Argentinian Andes due to
global climate change were addressed in 2010 with a
national statute to protect glaciers and the periglacial
environment (Masiokas et al. 2020; Blöthe et al. 2021). One
of the most important outcomes was the creation of a
national glacier inventory, mapping cryospheric landforms
under protection (IANIGLA-CONICET 2018).
Within the Dry Andean periglacial belt, the most visible
expression of mountain permafrost are rock glaciers, whose
distribution and internal structure have been studied
systematically (Villarroel et al. 2018; Blöthe et al. 2021;
Halla et al. 2021). They are recorded in the Argentinian
national inventory, while more widespread permafrost-
affected block and talus slopes have received less
attention. Talus slopes are sheeted and cone-shaped
debris accumulations below free rock faces mainly fed by
rockfall (Lambiel and Pieracci 2008; Kenner et al. 2017).
They are characteristic features of periglacial mountain
regions and have been subject to frequent research (e.g.,
Sass 2006; Lambiel and Pieracci 2008; Scapozza et al.
2015; Kenner et al. 2017). Studies on periglacial talus
slopes in the Swiss Alps found possible ice contents of
about 20–60% (Scapozza et al. 2015). However, there is a
significant data gap in the Andes, where they were mainly
examined as debris sources of talus-derived rock glaciers
(e.g., Halla et al. 2021), or in terms of debris movement
(Pérez 1993) and hydrological characteristics (Caballero et
al. 2002). Even less research on a global scale has been
devoted to blockslopes, which is not least due to varying
terminology and definitions. Blockslopes, often also termed
rectilinear debris-mantled slopes (Kamp et al. 2005;
Trombotto Liaudat et al. 2014; French 2017) or Richter
(denudation) slopes (Augustinus and Selby 1990; Fort and
van Vliet-Lanoe 2007) are straight slopes of in situ
weathered material. They were mainly described in the ice-
free areas of Antarctica (e.g., Augustinus and Selby 1990;
French 2017). Earlier studies mention their vast distribution
in the semiarid to arid Andes (Garleff and Stingl 1983;
Trombotto 2000; Kamp et al. 2005). If these landforms
contain a not yet quantified amount of ice, they gain
hydrological significance due to their large distribution in the
Andes. Thus, our study investigates the spatial distribution
of taluses and blockslopes in a semiarid high Andean
catchment by combining geomorphological mapping and
statistical terrain analysis. Our results are an extension of
the existing national inventory on cryospheric landforms
(IANIGLA-CONICET 2018), to quantify these
underestimated permafrost landforms in the Dry Andes of
Argentina.
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2 STUDY AREA
The Agua Negra catchment (ANC) is located in the
Cordillera Frontal of the San Juan province in the Dry Andes
of Argentina. It is part of the Desert Andes near the southern
transition to the Central Andes (Esper Angillieri 2009),
where we can see a distinct increase in cryospheric
landform cover reaching down to about 3000 m asl (Figure
1). The Desert Andes are characterized by extremely low
mean annual precipitation (< 250 mm) mainly falling during
winter months as snow or sleet above 4000 m asl (Minetti
et al. 1986; Lliboutry 1998). Snow cover is comparatively
thin and of short duration (~3 months) due to the effect of
high incoming solar radiation throughout the year,
controlling surface temperatures and upper ground thermal
regimes (Schrott 1994; Lliboutry 1998). The drier conditions
in this part of the Dry Andes are also reflected by the
distribution of cryogenic landforms in terms of areal
coverage and altitude (Figure 1c). The median 0 °C
isotherm rises by almost 1000 m compared to the Central
Andes, as does the lower limit of glaciers. Rock glacier
distribution is mainly concentrated in an altitudinal band
between 4500 and 5000 m asl. While the areal extent of
glaciers clearly dominates over periglacial forms in the
Central Andes, the importance of the latter increases
significantly in the Desert Andes (Sattler et al. 2016).
Information on the geology is only available in coarse
resolution and indicates that the ANC comprises different
geological units. The Paleozoic basement of marine
sedimentary rocks and local granite intrusions is covered
mainly by Permian to Triassic volcanic and volcanoclastic
rocks of the Choiyoi Group, accounting for almost 64% of
the upland area (SEGEMAR 2019).
Within an area of 1315 km2 the ANC covers an altitudinal
range of 4735 m, with the highest peak reaching 6280 m
asl. The mean precipitation dataset of the Climate Hazards
Group InfraRed Precipitation with Station data (CHIRPS)
from 1981 to 2020 (Funk et al. 2014) shows a mean
precipitation sum of about 53 mm/yr in the lowland parts of
the catchment and up to 131 mm/yr in the upland. A
meteorological station in front of the Agua Negra glacier
reports high global radiation values of ~430 W/m2d-1 and
low mean relative humidity of ~30% (Pitte et al. 2022).
Scattered morainic remnants detectable in the whole ANC
as well as the U-shaped main valley prove the more
extensive glacial imprint in the catchment in the past.
However, no mapping of moraines or stratigraphical studies
indicating Pleistocene glaciations are available for this part
of the Andes. At present, the basin shows regionally
representative cryospheric conditions (Gruber 2012;
IANIGLA-CONICET 2018), enabling the transferability of
the results within the semiarid Argentinean Andes. It has a
low glacial coverage of 1.44% and a large vertical extent of
mountain permafrost of almost 2600 m with respect to the
regional lower permafrost limit at ~3700 m asl (Trombotto
2000; Gruber 2012). In the entire catchment, 59 rock
glaciers are mapped (IANIGLA-CONICET 2018),
comprising an area of 5 km2 or 0.38%. With 667.78 km2
identified as areas with potential permafrost (Gruber 2012;
Figure 1b) there is a clear spatial deviation between the
mapped rock glaciers and the potential permafrost
distribution.
Figure 1. a) Overview of the location and cryospheric composition of the Dry Andes of Argentina, with c) the regional area-
elevation distribution of glacial and periglacial landforms
including the median elevation of the 0 °C isotherm surrounded by
25
–75 % quantiles, calculated from ERA-5 monthly re-analysis data averaged from 1940 to present (Hersbach et al. 2023).
b)
Detailed overview of the study area of the Agua Negra catchment with the manual mapping areas. Permafrost Zonation
Index (very low to high possibili
ties) based on Gruber (2012), cryospheric landforms taken from IANIGLA-CONICET (2018)
.
Landforms in a) were optically enlarged by a 1-pt frame to enable visibility.
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The basin is drained by the Agua Negra River and its
tributaries, mainly fed by the meltwaters of surrounding
glaciers and seasonal to perennial snowfields, precipitation,
as well as contributions from active layer thawing and
permafrost degradation (Halla et al. 2021, Schrott 1996).
Discharge measurements confirmed that water released
from the active layer and seasonally frozen ground may
constitute ~30% of total discharge during the summer
months in the upper ANC (Schrott 1996).
3 METHODS
We conduct a remote sensing and field-based
geomorphological mapping approach of block and talus
slopes in three sub-catchments of the ANC, building on the
existing national inventory of cryospheric landforms
(IANIGLA-CONICET 2018; Figure 1). Our mapping results
are statistically evaluated to analyze the spatial distribution,
characteristics and formative controls of the target
landforms.
3.1 Geomorphological mapping
A geomorphological map of blockslopes, taluses and
bedrock was created based on satellite imagery (Esri,
Google Earth Pro) and a TanDEM-X DEM (12 m resolution).
We used satellite imagery from austral summer months with
minimum snow cover and shading to improve mapping
accuracy. The upper ANC, San Lorenzo catchment (SLC)
and a side valley in the ANC (SV ANC) were selected for
manual mapping to reflect the overall heterogeneity of the
basin. All three sub-catchments lie within the Permafrost
Zonation Index (Gruber 2012, Figure 1a, b). Furthermore,
their altitudinal range, valley orientation, size and ratio of
glacial and periglacial landforms is representative. Rock
glaciers, (debris-covered) glaciers and perennial snowfields
(IANIGLA-CONICET 2018) as well as bedrock (manually
mapped) were used to support mapping accuracy and to
allow for a comparative spatial quantification of all
cryospheric landforms in the sub-catchments. The
inventory-based, restricted rock glacier delineation was
extended to include front and marginal parts (RGIK 2022).
The final output is an extended geomorphological map of
cryospheric landforms (Figure 2), accurately delineating
blockslopes and taluses that serve as input for the statistical
analysis.
3.2 Statistical Analysis
A pixel-based analysis of block and talus slope distribution
based on 15 individual variables reflecting topography,
surface morphology and climatic conditions on different
spatial scales was conducted to determine their spatial
distribution, landform characteristics and formative controls
(Table 1). In addition, the statistical analysis provides
valuable insights into the suitability of the applied variables
for future geostatistical modeling and semi-automatic
mapping of block and talus slopes on a larger spatial scale.
Since there is little information on suitable variables
characterizing the distribution of blockslopes and taluses,
we selected and adopted variables from comparable
landform distribution studies in (periglacial) mountain
landscapes (see references in Table 1). The original and
Table 1. Input variables for the statistical analysis calculated
on different resolutions (12 m original TanDEM-X; *60 m
resampled TanDEM-X using bilinear interpolation).
([1]Blöthe et al. 2021; [2]Brenning 2009; [3]Deluigi et al. 2017;
[4]Groh and Blöthe 2019; [5]Heckmann et al. 2014;
[6]IANIGLA-CONICET 2018; [7]Janke 2013; [8]Cavalli et al.
2013; [9]Otto et al. 2018; [10]Riley et al. 1999; [11]Sattler et al.
2016; [12]Schoch et al. 2018; [13]Weiss 2001).
Variable
Description
Refer
ences
(sel.)
Elevation
Altitude [m asl]
[3] [4]
Slope
Slope gradient [°]
[5] [11]
[12]
Topographic
position
index (TPI33)
Topographic slope position based
on elevation comparison of one
cell to its surrounding
neighborhood in a rectangular
33x33 cell moving window
TPI = z0 - zmean
[1]
[13]
Curvature*
(overall,
planform,
profile)
Overall, planform (vertical) and
profile (horizontal) slope concavity
and convexity
[3] [4] [5]
[7]
Aspect
North-exposedness, cosine of the
aspect
[1] [3] [12]
East-exposedness, sine of the
aspect
[1] [12]
Potential
incoming
solar
radiation
(PISR)
For the entire year 2022 based on
topographic position and relief
(DEM) [MWh/m2]
[3] [11]
Topographic
wetness
index (TWI)
Areas with topography-controlled
water accumulation defined by
slope (ß) and upstream
contributing area based on the D8-
flow algorithm (A)
TWI = ln(A / tan(ß)) [2]
[9]
Topographic
roughness
index (TRI,
TRI60*)
Elevation difference from a center
cell and its surrounding
neighborhood in a 3x3 cell moving
window
[5] [9] [10]
Size of the
contributing
area (SCA)
Size of the contributing area [m2],
calculated using the D8-flow
algorithm, log-transformed
[2] [4] [12]
Mean
roughness
of the
contributing
area (MRCA)
TRI-weighted flow accumulation
divided by unweighted flow
accumulation (D8-flow algorithm)
[5] [8]
Mean slope
of the
contributing
area (MSCA)
Slope-weighted flow accumulation
divided by unweighted flow
accumulation (D8-flow algorithm)
[2] [4] [12]
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the resampled (60 m) TanDEM-X DEM were used to
analyze differences in the distribution and landform
characteristics on several spatial scales.
We applied parameters representing topographic
conditions and surface morphology (e.g., curvature,
topographic roughness) as well as landform characteristics
(e.g., slope inclination, different characteristics of the
contributing area). To represent climatic and moisture
conditions, the elevation as well as the potential incoming
solar radiation (PISR) and topographic wetness (TWI) were
used, since there is no meteorological network to
interpolate climatic variables from measured data. The
coarse resolution of satellite-derived meteorological data is
insufficient for the study purpose and highly uncertain in
high mountains.
Boxplots are used to summarize, examine and compare the
distribution of the target landforms visually for each
variable. Differences in the distribution of explanatory
variables for block and talus slopes were tested for
significant statistical differences (p < 0.05) using the
Kruskal-Wallis and the Wilcoxon rank sum test for non-
parametric data. Here, we present a summary of the
combined analysis, detailed results of the single sub-
catchments can be accessed online
(http://dx.doi.org/10.13140/RG.2.2.24348.67200/1).
4 RESULTS
Block and talus slopes are widely distributed in all three
sub-catchments (Figure 2). Blockslopes are by far the most
dominant landform accounting for more than 60% of the
areal surface. In the Upper ANC and SLC, talus slopes are
the secondary landform with ~12% areal coverage, while in
the more glacier and snow influenced SV ANC they account
for only 2.4%. In the upper ANC, a distinct difference
between the main valley slopes bordering the Agua Negra
River can be observed. West of the small mountain stream
with dominant slope exposures to N and E, blockslopes
have a vast and uniform distribution. On the eastern valley
slopes, the proportion of extensive talus sheets and
coalescing talus cones below (channelized) steep cliffs
increases. More than 60% of the talus coverage in the
Upper ANC is located east of the Agua Negra River. The
dominance of blockslopes on N and E exposed slopes is
also very evident in the SV ANC, as well as southwest of
the San Lorenzo stream in the SLC, though less distinct.
Both landforms exhibit statistically significant (p < 0.05)
differences for all analyzed variables, enabling statements
about the distribution and formation conditions (Figure 3).
As represented by the topographic position index (TPI),
blockslopes mainly cover low relief plateaus and upper to
middle slopes, sparsely dissected by alluvial channels or
bedrock outcrops. Talus slopes primarily occur in lower
slope positions bordering channels and river floodplains.
The elevation differences of the landforms are particularly
notable (Figure 3). Even though blockslopes were mapped
at nearly all considered elevations, they are dominating
between 4900 m and 5500 m asl, with an elevation mean
at 5194 m asl. Talus slopes, however, are distributed in a
narrower interquartile range of 4300 m and 4700 m asl with
a mean elevation of 4540 m asl and an upper limit of
~5300 m asl. Upslope they are limited by bedrock outcrops
feeding the talus that are incorporated in the statistical
analysis due to their higher roughness values. Hence, the
mean roughness values of the contributing area (MRCA)
are higher. Large bedrock outcrops on the middle and
upper slopes are also associated with thick talus formation.
Where the material is additionally channelized, cone-
Figure 2. Geomorphological maps of blockslope, talus and bedrock distribution in the three sub-catchments of the Agua
Negra catchment, with
(from left to right) the Upper Agua Negra catchment (Upper ANC), San Lorenzo catchment (SLC)
and the side valley f
arther downstream the Agua Nera catchment (SV ANC); see Figure 1 for locations. (Debris-covered)
glaciers, perennial snowfields and rock glaciers from
IANIGLA-CONICET (2018). The map shows the extended rock
glacier outline
including frontal and marginal parts (based on RGIK 2022). Note the different scales of the catchments.
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shaped taluses, protalus ramparts and talus-derived rock
glaciers can be found.
Talus slopes are characterized by greater mean slope
inclination around 25–28° of the landform itself, but also of
the contributing area (MSCA). Blockslopes mainly range
from 17–22°. Despite their different slope angles, both
slope types mainly occur horizontally and vertically
elongate, although this pattern is much more distinct for
blockslopes. For blockslopes, all three curvature layers
show mean values of ±0.0, indicating a remarkable slope
linearity in horizontal and vertical direction. Talus slopes,
however, display a tendency towards horizontal and vertical
concavity, indicated by slightly positive profile and slightly
negative planform curvature (Figure 3).
While talus slopes show no clear tie to aspect except for a
certain dominance in southern exposures in the three sub-
catchments, blockslopes occur more frequently on N and E-
facing hillsides, resulting in higher insolation (+ 0.34 MWh
m-2). The topographic roughness indices TRI and TRI60
indicate higher roughness for taluses with the latter one
showing more pronounced differences between the two
landforms. Although the distributional differences between
blockslopes and taluses are statistically significant for all
examined variables, the TWI shows the smallest effect on
their distribution patterns. Both landforms show the same
tendency towards low potential topographic water
accumulation, and differences in contributing area sizes
(SCA) are rather small. Taluses have slightly larger
catchments on average, corresponding to their topographic
positions in the catchments.
Figure 3. Boxplots showing the median and interquartile range for the analyzed environmental variables for both target
landforms
in all three manual mapping areas (further details and abbreviations in Table
1). Block and talus slopes show
statistically significant differences (p
< 0.05) for all examined variables.
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4 DISCUSSION
The statistical analysis of the environmental characteristics
of block and talus slopes point out important factors for their
formation. The distribution of variables allows inferences
about the characteristics and distribution of the landforms.
The exceptionally high insolation associated with the
semiarid, leeward climatic conditions in the Dry Andes of
Argentina seem to greatly favor blockslope formation. Their
apparent distribution on high elevation, low-relief plateaus
and sparsely dissected, preferentially N and E-exposed
upper slopes imply suitable conditions there. Thus, the
development of blockslopes seems to be linked to these
cold and (semi)arid conditions (e.g., Trombotto Liaudat et
al. 2014; French 2017). Their wide distribution at high
altitudes, mainly above 5000 m asl, in combination with
high exposure to incoming solar radiation benefits strong
temperature gradients, effective frost action as well as
evaporation and sublimation. This is confirmed by
Höllermann (1983), who describes their most common
distribution in the upper part of the periglacial or subnival
belt at 30–50°N and 29–35°S, with their greatest vertical
extent at 30°N and 30°S in sunny exposures. Further
variables like a wind shelter index (Veitinger et al. 2016) or
geomorphic protection index (Kofler et al. 2020) might be
useful for interpretation, as strong wind action in
topographically open areas is supposed to contribute to
blockslope formation via debris-removal, wind-drift,
enhancing dryness and in situ weathering (Garleff and
Stingl 1983; Höllermann 1983). The connection of
blockslope formation to dry conditions is further supported
by very low TWI values. However, talus slopes feature
nearly the same TWI values. This is inconsistent with other
variables, e.g., curvature, PISR, TPI and N-exposedness
that indicate talus occurrence on lower slope positions with
higher water-converging planform and profile curvature,
and lower insolation (cf. Etzelmüller et al. 2006). The slight
upward concavity and intersecting channels observed for
talus slopes favor the concentration of water flow and snow
accumulation through wind sheltering, thus suggesting
higher water availability. A stronger topographic shading
matching the smaller PISR can be derived from their higher
occurrence on south-facing, lower slope positions in
rugged, intersected terrain. Associated protalus rampart
and talus-derived rock glacier formation is a clear indication
for a certain water availability and moisture content.
We assume that debris supply from the bedrock cliffs
exceeds the material removal mainly occurring at the lower
talus, since the investigated taluses have a straight to
slightly concave profile curvature and a decreasing slope
inclination between rock wall and talus (cf. Sass 2006).
Thus, sediment storages of considerable size can build up
(Otto 2006). The higher slope inclinations of taluses and
associated contributing areas indicate a higher angle of
repose for talus accumulations beneath steep feeder rock
walls compared to blockslopes. This is in accordance with
our field observations of coarse-grained talus material
compared to finer-grained blockslopes (cf. Otto, 2006). A
higher resolution DEM is necessary to visualize grain size
differences between blockslopes and taluses with the TRI.
The overall higher TRI and TRI60 on taluses result from the
smaller landform extent in combination with their
topographic position bordering large bedrock outcrops,
floodplains, incised channels, protalus ramparts and rock
glaciers. Due to the coarse resolution of the roughness
indices, these are included in the analyses and produce
higher values that are not necessarily linked to landform
characteristics or formation controls, but distributional
patterns. The high range of roughness indices for
blockslopes is largely driven by isolated bedrock outcrops
below the lower mapping limit of bedrock (30 m2), which are
thus included in the blockslope analysis. Apart from these,
blockslopes feature neither evident source areas, nor
indications for dominant erosion and deposition with
respect to curvature, roughness and slope inclination.
Therefore, the presence of a rather thin layer of debris
above in situ weathered bedrock is plausible and supported
by Trombotto (1991), reporting only thin cryoregolith
deposits. The thin debris layer migrates downslope by
gravitational processes like (perma)frost creep (Fort and
van Vliet-Lanoe 2007; Trombotto Liaudat et al. 2014).
Debris supply and debris removal seem to be largely in
equilibrium, resulting in the characteristic slope shape at
repose angles of the underlying bedrock (French 2017).
Their characteristic straight shape without significant
channelization or de-/acceleration of debris is reflected in
the name "rectilinear" slopes (Trombotto Liaudat et al.
2014; French 2017).
Based on the insulating effect of the thick debris layer and
the above-mentioned topographically favorable conditions,
we expect higher permafrost probabilities and ice contents
in talus slopes than in blockslopes (Lambiel and Pieracci
2008). Trombotto (1991) found not only seasonal ice but
permafrost in the taluses of Lagunita del Plata in the Central
Andes (33°S, 69°W). In the Swiss Alps, the presence of ice-
rich permafrost was mainly reported in the lower parts of the
talus slopes with the greatest sediment thickness (Lambiel
and Pieracci 2008; Scapozza et al. 2015). Ground ice
volumes are presumably lower in blockslopes due to the
extremely low TWI, high exposure to insolation and wind as
well as a rather thin insulating debris layer with minor impact
on ground ice protection. In addition, the low sediment
thickness offers smaller storage capacities compared to
thick talus accumulations. Nevertheless, the blockslope
occurrence in cold and (semi)arid environments provides
suitable conditions for permafrost formation (see also
Permafrost Zonation Index, Figure 1) and they have been
reported in areas with continuous permafrost around this
latitude (e.g., Trombotto 2000; Kamp et al. 2005).
The potentially low ice contents must be considered in
context with their large distribution to make accurate
quantifications on different spatial scales.
5 CONCLUSIONS AND OUTLOOK
Block and talus slopes are dominant in the periglacial belt
of the Dry Argentinian Andes, accounting for 77.5% (67%
blockslopes, 10.5% talus slopes) of the mapped sub-
catchments in the ANC. Rock glaciers, by contrast, cover
only 1.5% (IANIGLA-CONICET 2018). We demonstrate
that both landforms provide suitable permafrost conditions
for the presence of ground ice and may gain hydrological
significance due to their large extent compared to other
periglacial landforms. Talus slopes feature more favourable
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conditions for permafrost formation and conservation due to
higher insulation capacity of thick debris accumulations in
sheltered, water converging lower slope positions.
Blockslopes are exposed to higher insolation and frost
action at high altitudes in topographically open upper slope
positions and plateaus, forming a small layer of in situ
weathered debris with potentially lower ice contents. To
further assess their role as runoff contributors in the Dry
Andes of Argentina, we need distribution analyzes at larger
spatial scales and the investigation of their internal structure
and potential ice content. These studies gain additional
importance in the context of global warming and the
depletion of glacial water resources.
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