In north-western Tibet (34.0
In the Aru Mountain range on the western Tibetan Plateau, the entire ablation
zone of an unnamed glacier (termed here Aru-1) spontaneously collapsed on 17 July 2016.
This occurred despite its low slope angle of only 13
Applying satellite image analysis and glacier mass balance modelling, Kääb et al. (2018) explored the long-term behaviour of the two Aru glaciers prior to collapse. They show that the two glaciers started a surge-like instability around 2010, probably in response to both increasing precipitation and temperature in the region and related positive mass balances. Their preliminary analysis of the two-dimensional (2-D) thermal glacier regime shows a polythermal structure for the two glaciers. Such a structure would likely have provided resisting forces against whole-glacier sliding, but would have promoted englacial drainage to the bed in the lower temperate part of the accumulation zone, with possible local sliding and contributing to swelling or inflation of the glacier toe above and behind the frozen part. Facing the enigma of two neighbouring glaciers undergoing similar catastrophic behaviour close in time that is otherwise almost globally unique, Kääb et al. (2018) also point out the possible role that soft bedrock lithologies and glacier till production played in the instabilities.
In this study, we significantly extend the numerical analysis of the Aru glacier instabilities and discuss in detail the mechanisms leading to the collapses. We use a three-dimensional (3-D) full-Stokes thermo-mechanical model in order to (i) reconstruct the bedrock topography, (ii) analyse the thermal regime of the glaciers in 3-D, (iii) infer the evolution of basal friction prior to the collapse, and (iv) quantify the stress distribution that led to the final collapses. We then combine the modelling results with field investigations to further elaborate on the role of bedrock lithology and discuss the related origin of the twin collapses. Finally, we summarize key characteristics to recognize on other glaciers, and lithologic and thermal regimes that are similar to the Aru glaciers to help identify new potential collapses in the future.
The Aru Range is located on the remote western Tibetan Plateau
(34
Different DEMs used in this study produced from satellite data.
We use seven different DEMs derived from different satellite missions between 2000 and 2016 (see Table 1). The SRTM C-band radar DEM from mid-February 2000 (Farr et al., 2007) is used as the steady-state reference of the two glaciers for reconstructing bedrock topography. A Pléiades optical satellite stereo DEM from 1 October 2016, after the collapse, allows us to evaluate the modelled bedrock reconstruction over the detachment zone. We compute ice emergence velocities by differencing pre-collapse high-resolution DEMs from TanDEM-X, Spot7, and WorldView data and correct these for mass balance following the approach described in Gilbert et al. (2016) (Fig. 2). The effect of uncertainty linked to radar penetration in the TanDEM-X data should be minimized when comparing same wavelength data (X-band) at similar times of the year. Changes in penetration depth between the TanDEM-X data of 2011 (early June) and 2013 (mid April) due to different snow wetness should also be limited because surface melting in the accumulation area of the Aru glaciers only occurs from around mid-June (Kääb et al., 2018). X-band penetration into glacier ice (i.e. the Aru glaciers ablation areas) is very limited anyway (Dehecq et al., 2016). Comparing Spot7 (2015) and TanDEM-X (2014) elevations likely introduces uncertainty from TanDEM-X penetration in the accumulation area leading to higher apparent emergence velocities in this part (visible in Fig. 2). This problem only influences our friction reconstruction in the upper parts of the glaciers but not in the detachment areas. Details on DEM accuracies and acquisition methods can be found in Kääb et al. (2018).
Mean emergence velocities obtained by differencing elevation changes from repeat DEMs and modelled mass balances during different periods prior to the collapses. Steady-state velocities in the first panel are modelled. Orange dashed lines indicate the detachment outline.
We investigated glacier till properties by analysing samples collected from the Aru-1 avalanche deposit in the gorge close to the former glacier tongue. We collected these samples 1 year after the collapse on 18 July 2017. Rainy conditions on that day highlighted the behaviour of the surrounding lithology that quickly turned into a soft and unstable slurry in the presence of water. Additional information about our samples can be found in Figs. S1–S3 in the Supplement.
Our mass balance model for the two Aru glaciers is based on a degree-day model described in Gilbert et al. (2016). It has been calibrated for the Aru glaciers by using satellite-derived glacier mass balances and is fed by ERA-Interim climate reanalysis (Kääb et al., 2018). The model output, taken from Kääb et al. (2018), provides the spatio-temporal distribution of surface mass balance, firn thickness, and available surface meltwater for percolation/refreezing in the firn to constrain the thermo-mechanical model below.
Our thermo-mechanical ice-flow model is based on the Stokes equation coupled with an energy equation using the enthalpy formulation (Aschwanden et al., 2012; Gilbert et al., 2014). Changes in the glacier geometry are computed using a free surface equation (Gilbert et al., 2014). We adopt a pure viscous isotropic ice rheology following Glen's flow law (Cuffey and Paterson, 2010). The model is solved using the finite-element software Elmer/Ice (Gagliardini et al., 2013). Parameters and variables of our model set-up are summarized in Table 2.
Variables and parameters of the thermo-mechanical model.
We adopt a linear friction law as a basal boundary condition for the Stokes
equation:
The surface boundary condition is set as a stress-free boundary for the
Stokes problem and a Dirichlet condition for the enthalpy equation. In order
to take into account water percolation and refreezing within the firn, we
follow the approach by Gilbert et al. (2015), using a 6-month time step.
Latent heat due to refreezing is released every year during the summer time
step. The firn-thickness distribution is estimated from the mass balance
model following Gilbert et al. (2016) and the firn density is computed using
a linear density profile set to the following:
The lateral boundary condition is set to a no-flux condition for both the
Stokes and enthalpy equations. We assume a basal heat flux of
The first step of modelling the dynamics and thermal regime of the Aru glaciers is to establish a steady-state glacier as the initial condition for 1970 (start of the climatic reanalysis used). Landsat satellite images of the glacier area and the mass balance model indicate that the two glaciers were close to equilibrium from 1970 to 1995 (Kääb et al., 2018). We therefore assume that the surface topography measured in February 2000 by the SRTM mission (oldest available DEM) is representative of the glaciers, being in equilibrium with the mean climate over this period, although the positive mass balance between 1995 and 2000 probably thickened the glacier by a few metres in the accumulation area. We use the mean mass balance between 1980 and 1995 as an equilibrium mass balance considering that modelled mass balance is close to steady state during this period, before becoming positive from 1995 to 2008 (Kääb et al., 2018).
We first run the model on a 2-D flow line until a steady state is reached, deriving bedrock topography in the detached parts from a post-collapse Pléiades DEM and by reconstructing the bed at the upper glacier parts, assuming a constant basal shear stress (plastic approximation; Cuffey and Paterson, 2010). This initial step allows for the first approximation of the steady-state thermal regime which we presented in Kääb et al. (2018). We then use the 10 m depth temperature modelled with the flow line model to define the steady-state surface enthalpy as a function of elevation, which includes meltwater percolation and refreezing effects. This relationship is used to define a Dirichlet surface boundary condition for enthalpy in order to solve the steady-state thermal regime of the glaciers in 3-D in the bedrock inversion procedure (Sect. 3.3.1). Because the effects of meltwater percolation and refreezing are already included in the surface enthalpy value, there is no need to solve for these effects in diagnostic runs. The final 3-D steady-state glacier solution is obtained by running a transient simulation using the inverted bedrock topography and solving water percolation and refreezing until surface topography and the enthalpy field reach equilibrium with the imposed climatic condition.
Using constant climatic conditions associated with the balanced glacier
conditions corresponding to the SRTM DEM, we determined the bedrock
topography, allowing the best match between modelled and observed (i.e. SRTM
DEM) surface topography (van Pelt et al., 2013). For this purpose, we ran a
3-D transient simulation assuming no sliding, fixed surface topography (SRTM
DEM), and constant surface forcing (mass balance and enthalpy). The
no-sliding assumption is likely a good assumption in 2000 since the glacier
was not surging at this time (Kääb et al., 2018). Mesh horizontal
resolution is set to about 50 m with 15 vertical layers. The evolution of
the free surface is taken into account by varying the basal mesh elevation
instead of the surface elevation. The mesh surface topography thus remains
constant, while the bed topography is updated by solving the equation
Pléiades image of Aru-1 (left) and Aru-2 (right) glaciers after collapse, with topographic profiles 1 to 6 plotted for both glaciers (Copyright CNES 2016, Distribution Airbus D&S). The topographic profiles 1 to 6 show the measured surface topography in 2000 (SRTM, in red) and 2016 after the collapse (Pléiades, in yellow). These profiles are compared with the modelled bedrock (in purple) and surface (in green) topographies. The coloured dots on the Aru-2 glacier show the location of specific points of the profiles in the Pléiades image. Those points correspond to locations where our reconstruction matches the Pléiades DEM and where bedrock should thus be visible on the Pléiades image (no ice debris). Grey shading indicates the detached parts according to the Pléiades DEM compared to SRTM.
We use the opportunity provided by the exposed detachments to compare the
reconstructed bedrock topography with the measured Pléiades DEM from
after the collapses (Fig. 3). On the Aru-2 glacier, the points at which
bedrock is clearly visible in the Pléiades images match well with the
locations where our reconstructed bedrock topography matches the
Pléiades DEM (dots in Fig. 3). Elsewhere in the Aru-2 glacier
detachment zone, the modelled bedrock is deeper than the observed surface
elevation; this is likely due to the remaining ice debris overlying the
actual bedrock, so the Pléiades DEM elevations are expectedly higher.
This is confirmed by the good continuity between the ground topography
measured outside of the former glacier tongue and the one inferred from our
bedrock reconstruction (see Fig. 3, profile 6). On the Aru-1 glacier, the
reconstructed bedrock on profiles 2, 3, and 4 is systematically deeper than
the Pléiades DEM, even on the steep side, close to the margin of the
glacier where no ice remained after the collapse. This means that ice flow
is not accurately modelled in this part, likely due to the premise of no
sliding, which is probably not accurate considering that the glacier may
have been temperate at its base here (see Sect. 4.1). The error in the
modelled bed topography of the Aru-1 glacier is, however,
The Aru glaciers are representative of a cold and semi-arid climate regime,
and thus would normally show little dynamic behaviour under mostly cold-ice
conditions (below the pressure melting point). The steady-state equilibrium
line is located around 5750 m a.s.l. (minimum glacier elevations around
5200 m a.s.l., and maximum elevations around 6100 m a.s.l.) with a
maximum accumulation of 0.6 m.w.e. yr
Modelled steady-state horizontal surface velocities
As previously concluded by Kääb et al. (2018), our results show that the main branches of the two glaciers are characterized by a polythermal structure with a cold accumulation zone above 5900 m a.s.l. and a temperate-based ablation area surrounded by cold ice (Fig. 5). However, through the more accurate bedrock topography derived in this study and the 3-D approach, we show here that the temperate zones likely extended into significantly larger areas beneath the detachments than previously thought. Temperate ice forms in the lower part of accumulation zones due to a significant amount of percolation and refreezing of meltwater, which increases the temperature of the near-surface firn. This warmer ice is then advected into the ablation zone contributing, together with basal heat flux, to temperate basal conditions in the lower parts of the two glaciers. Cold surface conditions due to absence of water percolation in the ablation zone (cold impermeable ice) lead to a significant cold surface layer that eventually reaches the glacier base in the shallowest zones of the glacier tongues (Fig. 5). The western branches of the two glaciers have a significantly smaller temperate area with an ablation zone that is almost entirely cold-based (Fig. 5). This thermal structure may explain why the western branches remained stable after the collapses even though each branch lost its downstream supporting buttress formed by the detached glacier tongues. The modelled spatial extent of the temperate basal ice under steady-state conditions coincides with the detached areas and indicates that friction changes leading to the collapse occurred in temperate ice rather than being produced by a change from cold to temperate thermal conditions at the glacier beds. However, the large amount of cold ice, especially along the side of the gorge, could have provided significant lateral drag that built up high driving stress, which was able to balance gravitational force under the frictional change at the temperate parts of the beds.
Modelled steady-state temperature on the Aru-1 and Aru-2 glaciers.
Left panel shows basal temperature with black hatched lines showing
temperate areas. The inset highlights temperate-based (red) and cold-based
(blue) areas. Orange dashed lines indicate the detachment outline. Panel
The surge-like behaviour of the two glaciers identified from DEM comparison in Kääb et al. (2018) documents a change in the glacier dynamics during the 5 years prior to the twin collapses. By removing the elevation change due to surface mass balance we quantify the emergence velocity for constraining the basal friction parameter (Gilbert et al., 2016) for different periods: 2011–2013, 2013–2014, 2014–2015, and September–November 2015 (Table 1, Fig. 2). Our results highlight contrasting behaviour between the Aru-1 and Aru-2 glaciers, where friction decreased progressively in magnitude through time in both glaciers but over significantly different areas (Fig. 6). Frictional changes over the 5 years prior to collapse are also more significant on the Aru-1 glacier, resulting in a higher increase in surface velocity than on the Aru-2 glacier (Fig. 7). Similarly inferred friction for the Aru-2 glacier for annual means (2011–2013 and 2013–2014) and a 2-month mean (September–November 2015) indicates low seasonal variability of the basal condition. Similarly, modelled surface velocities on the Aru-1 glacier in September–November 2015 correlate well with those measured for January–April 2016 using satellite image correlation (Kääb et al., 2018) (Fig. 7f), also indicating low seasonal variability.
Friction coefficient
To evaluate how resisting forces acted and evolved to balance the driving forces, we compute the mean basal shear stress during different periods from the inverse method. We therefore assume that basal shear stress is mainly constrained by the global stress balance and should not be influenced by the sliding law that we used (Eq. 1) (Joughin et al., 2004; Minchew et al., 2016). The steady-state condition shows a basal shear stress between 100 and 200 kPa in both glaciers with mean shear stresses of 137 and 150 kPa for the Aru-1 and Aru-2 glaciers (Fig. 8a). In comparison, mean driving stresses are 152 kPa (Aru-1) and 213 kPa (Aru-2), indicating that 10 % (Aru-1) and 30 % (Aru-2) of the driving force are accommodated by normal force along the sidewalls. These levels of driving stress are at the higher end of the observed range of driving stresses on mountain glaciers (Cuffey and Paterson, 2010) and reflect the presence of strong resisting forces due mainly to cold-ice conditions combined with the resistance of the valley walls.
Modelled basal shear stress at steady state
The inversion of mean elevation changes between September and November 2015 (Fig. 8b) reveals that basal shear stresses on the Aru-1 glacier decreased to only 20 to 10 kPa in large areas, and basal resistance was mainly achieved by a few sticky spots (Stokes et al., 2007) in the detachment zone where shear stresses exceeded 250 kPa. Along the left bank of the glacier, close to the terminus of the Aru-1 glacier, shear stress was about 6–7 kPa and was not more than 15 kPa at the terminus. In comparison, the Aru-2 glacier behaved differently with more localized friction changes that produced a smaller change in the distribution of the basal shear stress during the same period (Fig. 8c).
The analyses of the dynamics and force-balance evolution on an area restricted to the detachment zone (dashed red lines in Fig. 8) reveal both similarities and differences between the two events (Fig. 9). Further references below to “lateral stress” apply to the detachment zone and not to the whole glacier and refer to the stress provided by the shearing interface between the stable and the unstable part of the glacier (visible in Fig. 9d). On the one hand, as already highlighted in Fig. 7, the mean detachment velocity prior to collapse behaved differently for the two glaciers (Fig. 9a). While the Aru-1 glacier detachment significantly accelerated, following behaviour typical for slope failure (Voight, 1990) over several years (blue dashed line in Fig. 9a), the Aru-2 glacier showed very little acceleration. On the other hand, force balances evolved similarly in the two detachments with a large increase in lateral stresses along the detachment margin due to both an increase in the driving stress and reduction in basal friction (Fig. 9b). Interestingly, lateral resistance overcomes basal resistance in both detachments with a delay time (81 days) close to the actual delay between the two final collapses (66 days) (Fig. 9c). On the Aru-2 glacier, it seems that smaller changes in friction were compensated by a higher change in driving stresses resulting in a similar increase in stress at the detachment margin compared to the Aru-1 glacier (Fig. 9b). The difference in surface velocity response to these similar stress transfers was a consequence of different basal drag repartitions in the two glaciers. Basal drag decreased uniformly across the entire detachment of the Aru-1 glacier with the appearance of localized sticky spots, whereas basal drag decreased only in the higher part of the detachment of the Aru-2 glacier. This led to more intense bulging and a lower velocity increase (Kääb et al., 2018) due to the high-friction patch remaining in the tongue (Fig. 6).
To evaluate the impact of the friction change on the mechanical property of the ice, we compute the maximal principal Cauchy stress and compare it with a threshold value set to 0.1 MPa (Krug et al., 2014) to identify the damage production (crevasse opening) (Krug et al., 2014; Pralong and Funk, 2005) (Fig. 10). The modelled stress fields clearly highlight zones where a progressive intensification of cracks opened around the detachment zone of the Aru-1 glacier (Fig. 10c) as observed on satellite images (Kääb et al., 2018); these fractures led to its final collapse. In comparison, the Aru-2 glacier again behaves differently, with less damage (cracks) that only affect the upper part of the detachment (Fig. 10c). This means that damage at the shear margin would have occurred suddenly in the Aru-2 glacier in 2016, which is confirmed by the observed sharp crack surrounding the detachment only a few days before the collapse (Kääb et al., 2018). In sum, the Aru-1 and Aru-2 glaciers underwent similar stress transfers, transitioning from basal drag to lateral shearing in their respective detachments, but showed different responses in terms of damage (i.e. crack production) and sliding speed due to different basal drag repartition. The Aru-1 glacier progressively evolved towards collapse, whereas the Aru-2 glacier accumulated stresses until a sudden release led to collapse. This indicates that critical stress transfers, precursory to such collapses, may occur without observable phenomena (i.e. surface velocity increase, crevassing) in the preceding years.
Maximum principal Cauchy stress excess above damage initiation
threshold at steady state
The modelled thermal regime is sensitive to basal heat flux, which is poorly
constrained. However, sensitivity tests (see Supplement, Fig. S4) show that the temperate area remains stable for a basal heat flux
between
The uncertainty in the reconstruction of basal friction mainly depends on
the accuracy of measured elevation changes, which is generally higher over
longer time periods (increased signal-to-noise ratio), making the 2011–2013
reconstruction the most reliable one. The measured September–November 2015 elevation
change is subject to a lower signal-to-noise ratio and is thus poorly
resolved in the accumulation area (see Fig. 2). However, it leaves the
reconstruction reliable on the detachment area where elevation changes are
much more significant. A similar conclusion applies to the 2014–2015
reconstruction, where the upper part of the glacier is affected by
penetration of the X-band signal, leading to an overestimation of the
emergence velocities (see Sect. 2.1). However, the influence of this
uncertainty on the modelled mass balance used to compute emergence velocity
is also low in the detachment zone, since elevation changes due to surface
mass balance are relatively small compared to the dynamical height changes
linked to the surge-like instability (
Using emergence velocities to constrain basal friction is not a commonly
used method and has been successfully tested only on a slow-flowing ice cap
by Gilbert et al. (2016). We therefore provide additional validation of this
method in the Supplement (Figs. S7–S9) by inverting the
friction on both glaciers using horizontal velocities inferred from offset
tracking obtained from repeat TerraSAR-X data in December 2013. This test
reveals good agreement between our emergence-based approach and the more
common method based on horizontal velocities. In particular, sliding zones
are similarly localized in both methods. Using the inversion based on
horizontal velocities as a reference, we estimate a sliding speed magnitude
accuracy of 0.036 m day
Our results indicate that low friction below the Aru glaciers was not linked to seasonal variability of water pressure, which is often observed on glaciers elsewhere (Bartholomaus et al., 2008; Vincent and Moreau, 2016). Rather, it is likely associated with sustained change in the basal conditions caused by an accumulation of liquid water over several years prior to the collapse. Over a hard bed (Cuffey and Paterson, 2010), this would likely result in the existence of a subglacial lake, which is very unlikely here because the low friction zone on the Aru-1 glacier extended to the tongue and the lake should have drained in such a case. Furthermore, in temperate ice, high water pressure conditions are unstable over long time periods because they lead to channel formation that can efficiently drain water and decrease the pressure (Schoof, 2010). High water pressure in a cavity network would be also difficult to maintain in the Aru cases, since increasing sliding speeds tend to increase cavity size and decrease water pressure. These arguments suggest that basal friction under the Aru glaciers was probably controlled by processes associated with soft bed properties (Cuffey and Paterson, 2010).
Comparison between sliding speed evolution and modelled basal steady-state temperature reveals a good correlation between the zones of sliding and temperate ice conditions and shows that the size of sliding areas remains similar over time (Fig. 11). This confirms that friction reduction since 2011 mainly occurred within zones that were already temperate areas, and that friction reduction is therefore not linked to a simple change from cold to temperate basal conditions. However, contrary to the Aru-1 glacier, Aru-2 appears to have been affected by a high-friction zone under its lower tongue, which the modelled basal temperatures are not able to explain as they indicate temperate, not cold conditions (Fig. 11). This zone of high friction explains the different behaviour observed from the two glaciers in terms of surface velocities and glacier advance. Indeed, a few months before the collapse, the Aru-2 glacier velocities were still low compared to the Aru-1 glacier (Fig. 7) and, while the Aru-1 glacier has advanced almost 200 m since July 2015, the front of the Aru-2 glacier remained stationary until the collapse (Kääb et al., 2018). Although the high-friction zone may have delayed the collapse of the Aru-2 glacier, it did not prevent it.
Field observations after collapse and inspection of the detachment zones
showed no presence of a hard-bed lithology beneath the glaciers, and no
large boulders were observed in the forefields and avalanche deposits.
Rather, extensive deposits of soft, unconsolidated and fine-grained
lithologies were identified (Figs. S1–S3). We collected till samples from
the Aru-1 glacier avalanche deposit and measured their grain-size
distribution (Fig. 12). Mean values over the four samples in the avalanche
path (Fig. 12) indicate till consisting of 14 % clay, 24 % silt, 44 %
sand, and 18 % gravel. These samples are representative of the material
found in the deposit and are likely also representative of the glacier till
on which the glacier rested. We also observed a rather smooth-surfaced
failure interface (i.e. detachment plane) suggesting a low bedrock roughness
at the macro-scale (
Grain-size distribution measured in four glacier till samples collected in the Aru-1 deposit area (numbers 1 to 4 in the right panel). Background image from Google Earth.
These findings confirm that the Aru glaciers rested on a soft bed, which
likely played an important role in controlling the behaviour of the glaciers
from the surge initiation to the collapse. For such bed types, basal motion
is not controlled by ice flow around bedrock bumps (Weertman, 1964;
Lliboutry, 1968) but rather by deformation in till (Truffer et al., 2001).
The sustained very low basal drag under the Aru glaciers (
One likely scenario for the development of the now collapsed Aru glaciers is that they grew in the past (pre-industrial climate) in colder conditions with low melting rates in summer, allowing for rigidity in the basal till to support high driving stress (see Sect. 4.3). The low water pressure meant that there was likely very little sliding and therefore very little production of till at that time. Upon commencement of some sliding, which may have occurred gradually over an increasing area of the bed during the past century, till production increased and the local glacier deformation regime tended to adapt to the distribution of till and liquid water reaching the bed. At this stage, percolation into the glaciers and accumulation of meltwater beneath the glaciers increased so rapidly in recent years (Kääb et al., 2018) that the glaciers could not keep balance with the changing conditions at the bed. Sliding may also have contributed to an increase in the water pressure in a positive feedback by destroying any efficient drainage system (Clarke et al., 1984). The contributory role of soft-bed lithology in the collapses is therefore likely threefold by (i) deforming with a plastic rheology when shear strength is reached, (ii) providing for low roughness at the ice-bed contact, and (iii) maintaining high water pressure, while sliding speed increases; a known process that accounts for surge behaviour (Clarke et al., 1984; Raymond, 1987; Fowler et al., 2001). High content in clay and silt probably also leads to low hydraulic conductivity favourable to higher water pressure in the till (Fowler et al., 2001).
We suggest that the existence of a high friction area under the Aru-2 glacier tongue prior to collapse is due to both higher basal normal stresses (Fig. S10), which increased the till strength, and higher lateral drag along the west side of the detachment which decreased the basal shear stresses compared to the Aru-1 glacier (Fig. 9). In this way, and contrary to the Aru-1 glacier, basal shear stress under the tongue of the Aru-2 glacier only approached the ultimate shear strength of the till just before the final collapse in response to both decreasing resistance by the lateral margin (due to crevassing) and increasing driving stresses (due to bulging).
The Aru collapses, and in retrospect the Kolka Glacier collapse, define a newly recognized type of avalanching glaciers characterized by an underlying failing substrate. These ice slides could occur on glaciers with fairly low angles, therefore involving potentially large volumes of material and presenting serious consequences in terms of hazard potentials. The high sensitivity of the ultimate shear strength of the substrate to pore water pressure, combined with low bed roughness, allows for a dramatic and sustained change in basal friction conditions capable of driving this kind of instability.
The Kolka event in 2002 in the Caucasus Mountains is probably another example of this type of instability in which the maximum shear strength of the till is exceeded by a sudden increase in basal shear stress at constant effective normal stress. Indeed, during the few weeks before this collapse, significant mass was added on top of the glacier by rock and ice-fall activity, increasing basal shear and normal stresses (Huggel et al., 2005; Evans et al., 2009). This could change the surface slope, which reached the till friction angle and triggered the failure. If the till was saturated with water and had low hydraulic conductivity, increasing pore water pressure could also have compensated for the rising normal stress, keeping the normal effective stress constant. Once maximum shear strength is exceeded, failure is triggered (Evans et al., 2009). This hypothesis is plausible since Kolka Glacier is known to be a surging glacier, able to store large amounts of liquid water, and high water content and pressure were observed before its 2002 collapse (e.g. unusual ponds observed on the glacier prior to collapse) (Kotlyakov et al., 2004).
However, changes in till strength in response to changing in water pressure are likely also involved in the surge mechanisms of temperate glaciers without the large majority of surges turning into gigantic collapses. This renders sudden changes in till strength as a necessary but not fully sufficient condition, for collapses controlled by till strength. The necessary secondary condition for catastrophic failure is a sustained high driving stress with low bed roughness, coincident with weakening till. This means that the glacier has to grow over time, atop of a more stable substrate capable of supporting higher driving stresses. In particular, freezing conditions allow for the development of relatively thick glaciers on slopes that would otherwise be unable to support such high shear stress under the presence of liquid water. This makes the spatio-temporal interplay of soft-bed characteristics and the polythermal glacier regime a prerequisite of the Aru collapses, whereas for the Kolka Glacier the additional loading over a short time should have caused a fast increase in shear stress, significantly exceeding the glacier's normal conditions.
In many of the world's glacierized regions, ongoing atmospheric warming increases surface melt and the amount of water reaching glacier beds, thereby modifying the till shear strength. This development is therefore in theory capable of driving more till-strength-controlled instabilities and collapses. The most impacted glaciers would be those flowing on soft and highly erodible bed lithologies at high driving stress, particularly those with heterogeneous thermal structure (polythermal glaciers). Such glaciers are mostly localized in cold and dry climates, where a small increase in temperature results in a relatively large change in melting conditions such that the amount of water reaching the glacier base can significantly increase instability. In reality, however, an array of factors and their specific (and to this point rare) interplay in time and space are necessary to catalyse glacier collapsing as observed for the Aru and Kolka glaciers.
In summer 2016, one of the most spectacular glacier disasters ever observed occurred in western Tibet. The collapses of the twin Aru glaciers set a new reference in terms of size and mobility of glacier instabilities and required a reassessment of assumptions and conditions that more typically drive hazards and impacts linked to mountain glaciers. Using 3-D thermo-mechanical modelling together with satellite and field observations we conclude that the Aru twin collapses were driven by increasing meltwater reaching the bed in the temperate area of the polythermal structure of the glaciers, leading to the weakening of the underlying till and sediment.
Our steady-state simulation reveals that both glaciers were likely polythermal, with predominant temperate basal conditions over the detachment areas. Using satellite-observed elevation change and modelled surface mass balance, we reconstructed the frictional and shear stress regimes at the glacier base that occurred during the 5 years prior to collapse. We show that both glaciers experienced a stress transfer in their detachment area, transitioning from basal drag to lateral shearing at the detachment margin, likely beginning around 2012. However, the different spatial repartitions of basal drag in the two detachment zones led to visibly different behaviour. As early as 2015, basal drag in the Aru-1 glacier was very low over the whole detachment zone with a few remnant sticky spots where stress was concentrated. In contrast, basal drag of the southern Aru-2 glacier was distributed between a low-friction area in the upper half of the detachment zone and a high-friction area in the lower half. These circumstances led to a progressive destabilization of the Aru-1 glacier with a significant acceleration in ice flow in the detachment zone over several years prior to collapse, whereas stresses accumulated in Aru-2 until a sudden break of the shear margin occurred only a few days before the collapse.
We interpret that the change in friction was due to glacier till reaching its ultimate shear strength in response to increasing pore water pressure. This assumption is supported by field observations that revealed soft and erodible material with high clay and silt content underneath the glaciers. Plastic rheology of the till underlying the Aru glaciers combined with low bedrock roughness and polythermal glacier structure seem to be the basis of the collapses. The polythermal structure enabled the glaciers to grow at high driving stress on a partially frozen substrate, while temperate areas facilitated the water reaching the bed. Increasing water pressure in temperate areas led to failure in the till and thereby to increasing shear stresses on localized sticky spots and along the detachment margin. Due to the low bed roughness, the nature of these sticking spots seems purely thermal (cold patches). They are therefore mechanically susceptible to failure and can be affected by thermal effects such as intense deformational heat or latent heat release.
Under climatic change and related increases in surface melt rates, polythermal glaciers underlain by soft and erodible substrate are likely to destabilize more readily than hard-bed glaciers. Lower bed roughness of the former and plastic rheology of such till promotes instability, while hydrological feedbacks with high till shear rate destroying efficient drainage components (canals) lead to increasing pore water pressure and weakening substrate strength. The cases of the Aru glaciers highlight the most extreme glacier behaviour when bedrock roughness and/or frozen zones are unable to sustain global stability while the substrate is failing, leading to the catastrophic failure of large glacier sections.
The list of satellite data used is included in the
Table 1. We also used Sentinel data in addition to compute velocities.
Sentinel-1 and 2 are publicly available from
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AG designed the study, performed the modeling and collected the till samples. SL processed the TanDEM-X data and obtained the DEMs and velocity maps. AnK processed the Spot7, SRTM and Worldview data. EB and SG processed the Pléiades data. TY organized the field trip and provided pictures and feedbacks from field observations. JK, GL and AlK proposed the idea that glacier bedrock lithology played a role in the collapse. All authors contributed to the discussion of the results. AG led the writing of the paper and all co-authors contributed to it.
The authors declare that they have no conflict of interest.
We would like to thank Irina Rogozhina and Martin Truffer for their valuable and constructive review comments. We thank the editor, Arjen Stroeven, for his careful work. We are further grateful to the satellite data providers: CNES for Pléiades, Airbus/CNES for Spot 7, DLR for TanDEM-X, and Digital Globe for WorldView. Andreas Kääb and Adrien Gilbert acknowledge the Univ. Oslo EarthFlows initiative and funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC grant agreement no. 320816, and Andreas Kääb also acknowledges the ESA projects Glaciers_cci (4000109873/14/I-NB) and DUE GlobPermafrost (4000116196/15/IN-B). Simon Gascoin and Etienne Berthier acknowledges support from the French Space Agency (CNES) and the Programme National de Télédétection Spatiale grant PNTS-2016-01. Tandong Yao acknowledges support from the CAS Strategic Priority Research XDA20000000. Adrien Gilbert acknowledges the Institute of Tibetan Plateau for organizing a field trip in summer 2017, and Mufak Said Naoroz at the Department of Geosciences, University of Oslo, for his support with the sample analyses. Jeffrey Kargel thanks NASA (High Mountain Asia Team) for support.Edited by: Arjen Stroeven Reviewed by: Irina Rogozhina and Martin Truffer