There have been rapid increases in both the number and
expansion of the proglacial lakes across High Mountain Asia. However, the
relationship between proglacial lakes and glacier dynamics remains unclear
in the Himalayan region. Here we present the surface elevation, flow-velocity changes, and proglacial lake expansion of Thorthormi and Lugge
glaciers in the Lunana region, Bhutanese Himalaya, during the 2000–2018
period using photogrammetry and GPS survey data. The lake expansion and
surface lowering rates and flow-velocity field of Lugge Glacier, a
lake-terminating glacier, have remained approximately constant since 2000.
Conversely, there have been accelerated proglacial lake expansion and a
2-fold increase in the thinning rate of Thorthormi Glacier since 2011, as
well as a considerable speed-up in the flow-velocity field
(>150 m a-1). We reveal that the lake formation and transition of Thorthormi
Glacier from a land- to lake-terminating glacier have triggered glacier
speed-up and rapid thinning via a positive (compressive) to negative
(extensional) change in the emergence velocities. This study provides the
first evidence of dynamic glacier changes that are associated with
proglacial lake formation across the Himalayan region.
Introduction
A recent deglacial trend has been reported for numerous glaciers across High
Mountain Asia (HMA; e.g. Brun et al., 2019; Maurer et al., 2019; Shean et
al., 2020), with these glaciers exhibiting spatially heterogeneous thinning
patterns (Bolch et al., 2012; Kääb et al., 2012; Brun et al., 2017).
There has been a rapid increase in both the number and expansion of the
proglacial lakes across HMA owing to this deglacial trend (Zhang et al.,
2015; Nie et al., 2017; Shugar et al., 2020), which has been particularly
pronounced across the eastern Himalayas (e.g. Gardelle et al., 2011; Chen
et al., 2021). Proglacial lakes form via the coalescence of supraglacial
lakes near the glacier terminus (Quincey et al., 2007); their formation
suggests the final phase of retreat for these contracting glaciers (Sakai
and Fujita, 2010; Benn et al., 2012). The increasing number and evolution of
proglacial lakes have led to a rise in the hazardous potential of glacial
lake outburst floods (GLOFs). GLOF hazards can be triggered by either
unstable terminal moraines or snow/rock avalanches (e.g. Fujita et al.,
2008; Westoby et al., 2014), and they can cause significant damage to hydropower
stations, bridges, and buildings that exist downstream of proglacial lakes
(Richardson and Reynolds, 2000).
Proglacial lake formation accelerates glacier mass loss via thermal
undercutting and calving at the glacier terminus (e.g. Benn et al., 2007;
Sakai et al., 2009). Previous studies have analysed the interaction between
proglacial lakes and glacier dynamics using in situ measurements and
remote-sensing methods across HMA (e.g. King et al., 2018; Haritashya et
al., 2018; Wei et al., 2021). Recent high-resolution satellite and aerial
photogrammetry techniques have led to improved glacier and proglacial lake
studies. For example, Watson et al. (2020) acquired in situ measurements and
unmanned aerial vehicle (UAV) photogrammetry across Thulagi Glacier in the
Nepal Himalaya, and they estimated the calving volume at the terminus based on
iceberg size. Furthermore, previous studies have also reported the retreat
and thinning of lake-terminating glaciers in their catchments to a broad
regional scale (e.g. Song et al., 2017; Zhang et al., 2019; Maurer et al.,
2019). King et al. (2019) reported that the mass loss of lake-terminating
glaciers was greater than that of land-terminating glaciers across broad
Himalayan regions, with an observed increase in mass loss after 2000. Pronk
et al. (2021) analysed the surface flow velocities of more than 300 glaciers
in the Himalayan region and determined that the flow velocities of
lake-terminating glaciers were twice as high as those of land-terminating
glaciers on average. The existence of a proglacial lake might be a factor
enhancing the glacier flow velocity, retreat, and thinning of HMA glaciers.
The response of lake- and land-terminating glaciers can fluctuate with
different patterns even if they are located near each other and/or exist
under similar climatic conditions (Liu et al., 2020). Therefore, advancing
our understanding of lake-terminating glacier fluctuations is essential for
making robust future predictions of the HMA glacier response.
Numerous proglacial lakes have an exceptionally high-risk potential for
GLOFs throughout the Bhutan Himalaya (e.g. Fujita et al., 2013; Zheng et
al., 2021), and lake expansion appears to continue unabated (Ageta et al.,
2000; Komori, 2008). Previous lake-terminating glacier studies have been
conducted across the Bhutan Himalaya using either in situ measurements or
satellite remote-sensing methods to assess their dynamics and evolutions
(e.g. Suzuki et al., 2007; Fujita et al., 2008). Tsutaki et al. (2019)
revealed contrasting fluctuations between two neighbouring glaciers in the
Lunana region using in situ GPS measurements, satellite remote-sensing data,
and numerical modelling. They reported a greater thinning rate along
lake-terminating Lugge Glacier than along land-terminating Thorthormi
Glacier during the 2004–2011 period, which was attributed to their
contrasting terminus conditions. They also projected that the thinning rate
and flow speed of Thorthormi Glacier could be accelerated if the current
land terminus changed to a lake terminus. The terminus of Thorthormi Glacier
is now detached from the terminal moraine and has evolved into a
lake-terminating glacier. The associated changes in glacier dynamics due to
proglacial lake formation have been studied worldwide (e.g. Boyce et al.,
2007; Tsutaki et al., 2013); however, no such study has been undertaken in
the Himalayan region to date. Therefore, this study aims to (1) update the
fluctuations of two glaciers that have been affected by proglacial lakes in
the Bhutan Himalaya and (2) evaluate the changes in glacier dynamics
associated with the transition from land- to lake-terminating conditions. We
analysed past in situ measurements and satellite and airborne remote-sensing
datasets to achieve this goal.
Study site
Our target glaciers, Thorthormi and Lugge, are located in the Lunana region
of northern Bhutan (Fig. 1; 28.06∘ N, 90.18∘ E).
Thorthormi Glacier covers 11.6 km2 based on the GAMDAM (Glacier Area
Mapping for Discharge from the Asian Mountains) glacier inventory (Nuimura
et al., 2015; Sakai, 2019) and the 2017 terminus position. Its elevation
range spans 4400–6900 m above sea level (a.s.l.). Thorthormi Glacier had
been in contact with the terminal moraine before 2011 and then detached
from the terminal moraine and transitioned into a lake-terminating glacier
(Tsutaki et al., 2019). Lugge Glacier is located to the east of Thorthormi
Glacier and covers an area of 10.0 km2 based on its 2017 terminus
position, with its elevation range spanning 4500–6900 m a.s.l. Lugge
Glacier Lake has expanded since the 1960s (Komori, 2008), with a maximum
lake depth of 126 m reported in 2002 (Yamada et al., 2004). This lake caused
an outburst flood in October 1994 and damaged the downstream areas (Fujita
et al., 2008; Maurer et al., 2020). Both glaciers are debris-covered and
have been reported to be experiencing long-term mass-loss and thinning
trends (Bajracharya et al., 2014; Maurer et al., 2016).
Details of the study site. (a) Location of the Lunana region
(inset) and helicopter photogrammetry (HP) orthoimage of Thorthormi and
Lugge glaciers (acquired on 24 March 2018). (b) Surface elevation map
generated from the HP-DEM using ground control points (GCPs) for terrain
data processing (open circles) and 2011 GPS tracks (dots). (c, d) Aerial photographs of Thorthormi and Lugge glaciers. Red arrows in panel (a) indicate the directions from which the aerial photographs were taken. The
dashed box in (b) shows the domain of (a). The green GPS track in (b) was
not used for the DEM accuracy check or elevation change analysis.
Observations and analysis methodsDGPS and aerial photogrammetry survey
We used the global positioning system (GPS) dataset of Tsutaki et al. (2019), who conducted a kinematic survey with a differential GPS (DGPS;
GEM-1, GNSS Technologies) across the on- and off-glacier terrains during the
19–22 September 2011 field campaign. The base station for this survey was
installed to the west of Thorthormi Glacial Lake (Fig. 1a). These GPS data
points were used to validate the satellite and photogrammetry digital elevation
models (DEMs) and compute the surface elevation changes (Sect. 3.2 and 3.3).
We conducted a helicopter photogrammetry survey on 24 March 2018. Four
action cameras (GoPro HERO5 Black) were attached to the skids of a
helicopter and acquired 4000×3000 pixel images in 1 s shooting
mode. The shutter speed, focal length, and ISO were fixed at 1/1250 s, 28.3 mm, and 100, respectively. We obtained ∼7500 photos in total
and cropped each photograph by preserving the central 2500×2500
pixel area to eliminate the “fisheye effect” of the GoPro camera lens
(Girod et al., 2017). We finally employed 3560 images based on the image
quality and glacier coverage. These images were processed in Agisoft
Metashape Professional Edition 1.7.1 (Agisoft LLC), and the sky view was
masked for the terrain data processing.
Terrain data processing
We extracted ground control points (GCPs) from a Pléiades panchromatic
orthoimage (0.5 m resolution), which was acquired on 7 November 2017
(Berthier et al., 2014), and its DEM (2.0 m resolution) for the
photogrammetry terrain data processing. We first generated a GPS-derived DEM
(GPS-DEM) to assess the vertical accuracy of the Pléiades-derived DEM
(PL-DEM). The 2011 GPS data points (UTM Zone 46N, WGS84) were interpolated
using the inverse distance weighted method and then exported to the same
grid size as the PL-DEM in ArcGIS (Tshering and Fujita, 2016; Sato et al.,
2021). We employed the standard deviation (SD; σ) of the elevation
difference between the PL- and GPS-DEMs on the off-glacier stable terrain as
the vertical accuracy of the PL-DEM. We did not use the grid cells with
steep slopes (>30∘; Fujita et al., 2008; Nuimura et
al., 2012). We then eliminated the validation points that were greater than
±3σ from the mean elevation difference as extreme outliers
(Mertes et al., 2017). Berthier et al. (2014) reported that the vertical
accuracy of the PL-DEM was improved by shifting the DEM horizontally. We
therefore shifted the PL-DEM by ±2 pixels (±4 m) in the
northing and easting directions, computed the elevation difference against
the GPS-DEM, and confirmed that there was no improvement in the vertical
accuracy. Finally, the PL-DEM vertical bias (mean elevation difference: MED)
was assessed for 12 009 grid cells, yielding a mean value of 0.26±3.86 m (MED ± SD). We extracted the GCP coordinates from the
orthoimage and bias-corrected PL-DEM. Specific topographic features (e.g.
boulders, river bending points, and dense vegetation spots) on the stable
ground were used as GCPs for the photogrammetry terrain data processing.
We used the structure from motion (SfM) software in Agisoft Metashape to
generate orthoimages and a DEM from the helicopter photogrammetry data
(hereafter HP-ortho and HP-DEM, respectively). We overlaid the 77 GCPs that
were extracted from the Pléiades products onto the helicopter
photogrammetry images (Fig. 1b) and generated both the HP-ortho and HP-DEM
at a 0.5 m resolution (Fig. 1a and b). We employed the same approach used
in the PL-DEM evaluation to evaluate the vertical bias and accuracy of the
HP-DEM by re-generating a 0.5 m resolution GPS-DEM. The vertical accuracy of
the HP-DEM was 0.25±3.70 m (N=25474 GPS-DEM grid cells; Fig. S1a in the Supplement); we also applied an elevation change correction (Sect. 3.3) to correct
for the vertical bias of the HP-DEM.
Changes in the glaciers and glacial lakes
We calculated the surface elevation change rates (dh/dt) by comparing the
2011 GPS-DEM and 2018 HP-DEM (both at 0.5 m resolution). We used 9491 and
15 604 grid cells to calculate dh/dt for Thorthormi and Lugge glaciers,
respectively (red tracks in Fig. 1b). We then compared our results with
previous elevation change studies. Tsutaki et al. (2019) calculated dh/dt
from the overlapping 2004 and 2011 DGPS data; they also computed the spatial
distribution of dh/dt for the same period using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived DEMs. We also
employed the long-term dh/dt data from Brun et al. (2017) and Maurer et al. (2019) to assess the thinning trends of these two glaciers. Brun et al. (2017) computed dh/dt for the 2000–2016 period over the Hindu Kush Himalaya
region using ASTER-based DEMs, and Maurer et al. (2019) calculated dh/dt for
the 1975–2000 and 2000–2015 periods using satellite-based DEMs. These
datasets (hereafter RS-based dh/dt) are provided as 30 m resolution raster
data. We extracted the dh/dt data from our DEMs at the same positions for a
comparative analysis.
We computed the surface velocity field using the ImGRAFT (Image
GeoRectification and Feature Tracking) open-source feature tracking toolbox
in MATLAB (Messerli and Grinsted, 2015). The normalized cross-correlation
algorithm (NCC; Heid and Kääb, 2012) in the feature tracking toolbox
(Templatematch) identifies the displacement patterns of the glacier surface
features and computes their magnitude from a pair of images. We selected a
Sentinel-2 image pair that was acquired on 16 November 2016 and 11 November 2017 (post-monsoon seasons). After visual trial and error, we chose a 20×20 template size (200×200 m) and 75×75
search window size (750×750 m) to compute the surface feature
displacements and calculate the annual flow velocity. We set the correlation
value for image matching (r>0.6) and signal to noise ratio (SNR <0.7) and eliminated the low-quality pixels, all of which served
as a confidence level threshold for successful image matching. We then
estimated the uncertainty of the glacier surface velocity and corrected
systematic error by checking the stable-ground (off-glacier) displacement
(e.g. Liu et al., 2020). We calculated the stable-ground surface
displacement (slopes <20∘; Quincey et al., 2009) and
set the corrected median values of Vx (east–west component) and Vy
(north–south component) to zero. The flow speed V (m a-1) is calculated
as follows:
V=Vx2+Vy2.
The mean and median stable-ground V values were 2.2 and 1.6 m a-1,
respectively, after the displacement correction. The velocity profiles were
extracted along the glacier central flowlines every 10 m, and the pixel
values where the flow directions differed by >90∘ from
the flowlines were eliminated. We also eliminated the velocity data along
the upper section of Lugge Glacier (>5100 m elevation) because
of its heavy snow cover, which can cause incorrect image matching via
feature tracking (e.g. Nuimura et al., 2017). We extracted the surface
velocity from the regional velocity product derived from the
ITS_LIVE (Inter-Mission Time Series of Land Ice Velocity and
Elevation) project (Gardner et al., 2019), which covered the entire HMA
region. The ITS_LIVE velocity product is generated from the
Landsat series with the auto-RIFT feature tracking processing chain yielding
a 240 m spatial resolution (Gardner et al., 2018). We extracted the annual
velocity data along the central glacier flowlines from the
ITS_LIVE product (2010–2018) to trace the temporal changes
in flow velocity; the ITS_LIVE velocities possess mean
uncertainties of 1.0 m a-1 (maximum of 3.1 m a-1) and 0.6 m a-1 (maximum of 6.0 m a-1) along Thorthormi and Lugge glaciers,
respectively. We also employed the velocity data produced by Tsutaki et al. (2019), which were calculated annually from the ASTER-derived optical
satellite images at 15 m resolution during the 2002–2011 period.
We delineated the glacial lake area from Landsat 7 and 8 (ETM+: Enhanced Thematic Mapper Plus; OLI: Operational Land Imager) images
with false-colour image composites that were acquired between November 2012
and November 2018 (30 m resolution). We then combined the proglacial lake
polygons before 2012 (Tsutaki et al., 2019) and traced the annual lake area
changes for the entire 18-year study period. The total lake area
uncertainties were estimated to be ±0.14 and ±0.08 km2 for Thorthormi and Lugge glaciers, respectively, depending on the
user-induced error and satellite image resolution (Paul et al., 2013). We
removed the DEM and velocity data where the glacier surface turned into the
lake surface in successive images. We compared the recent lake expansion
rates with a glacial lake inventory (High Mountain Asia Glacier-lake
inventory: Hi-MAG; Chen et al., 2021), which was generated for the entire
Himalayan region using data from the 2008–2017 period. We finally chose the
2011 and 2017 proglacial-type lakes (n=832) and calculated the expansion
rates between 2011 and 2017 in the eastern Himalaya region (including the
Lunana region).
Emergence velocity and ice flotation of Thorthormi Glacier
We calculated the emergence velocities along Thorthormi Glacier to evaluate
the change in glacier dynamics since its detachment from the terminal
moraine. We estimated the emergence velocity of a given section (Ve, m a-1) from the ice fluxes along the upper and lower boundaries of the
section as follows (e.g. Nuimura et al., 2011; Vincent et al., 2016; Brun et al.,
2018):
Ve=qin-qoutW‾⋅dx,
where qin and qout are the ice fluxes (m3 a-1) along the
upper and lower boundaries, respectively, and W‾ and dx are an averaged
glacier width (m) and length (200 m in this study) for the analysed Ve
section, respectively. The ice flux q (qin or qout, m3 a-1)
is calculated as follows:
q=W⋅h⋅Vc,
where W, h, and Vc are the glacier width (m), ice thickness (m), and
depth-averaged ice velocity (m a-1), respectively. We then applied a
simplified assumption that the glacier width is constant such that Eq. (2)
can be rewritten as follows:
Ve=hup⋅Vc,up-hlow⋅Vc,lowdx,
where hup/low are the ice thicknesses (m) and Vc,up/low the
depth-averaged ice velocities (m a-1) along the upper/lower boundaries.
We assumed that both the glacier thickness and width were constant in the
transverse and longitudinal directions, respectively, to calculate the
emergence velocities along the central flowline. The ice thickness h along the
central flowline was calculated from the HP-DEM-derived glacier surface
elevation and estimated bedrock elevation in Tsutaki et al. (2019). Tsutaki
et al. (2019) estimated the glacier-bed topography using the Farinotti et al. (2009) ice thickness model and tuning a model parameter based on the
observed lake depth (ice thickness). Tsutaki et al. (2019) simulated that
the basal velocity reaches 90 % of the surface velocity of Thorthormi
Glacier. We therefore calculated the emergence velocity using two
assumptions regarding the surface velocity: the depth-averaged velocity is
90 % of the surface velocity based on Tsutaki et al. (2019), and 100 % is
assumed for a floating condition after terminus detachment. We then used the
surface velocity component of the same vector in the central flowline
direction. The sections without flow velocities (2520–3020 m from the 2002
terminus) were linearly interpolated using the surface velocities of the
surrounding upglacier and downglacier sections. We calculated the emergence
velocity for a 200 m section by shifting the section in 50 m increments and
obtained a mean emergence velocity around the current terminus (2400–3500 m
from the 2002 terminus). We also calculated Ve in 2011, when Thorthormi
Glacier was still a land-terminating glacier, to compare the land- and
lake-terminating conditions. We estimated the ice thickness from the glacier
surface elevation of the ASTER-derived DEM acquired on 6 April 2011 and the
glacier-bed elevation along the central flowline. The depth-averaged ice
velocities were calculated from the surface velocities in Tsutaki et al. (2019) (Sect. 3.3).
We evaluated the ice flotation potential of the Thorthormi Glacier terminus
based on the ice flotation thickness (hf, m), which was calculated as follows
(Boyce et al., 2007; Watson et al., 2020):
hf=ρwρid,
where ρw is the density of water (1000 kg m3), ρi is
the density of ice (917 kg m3) (e.g. Boyce et al., 2007; Carrivick et
al., 2017), and d is lake depth (m). We then defined an index of potential ice
flotation as follows:
Pf=hfh×100,
where Pf is the potential ice flotation (%). The glacier can attain
flotation when the glacier ice thickness reaches hf such that Pf is
≥100%. We extracted the lake surface elevation (4415 m a.s.l.) from
the HP-DEM-derived lake perimeter and estimated the lake depth from the
glacier-bed elevation (Tsutaki et al., 2019). We then calculated Pf in
100 m intervals in 2011 and 2018 along the glacier central flowline in the
terminus region (up to 3500 m from the 2002 terminus).
Temporal variations in the spatial extents of (a) Thorthormi and (b) Lugge proglacial lakes. (c) Cumulative changes in lake
area for Thorthormi and Lugge glaciers relative to 2000. The background
images in (a) and (b) are Sentinel-2 satellite images that were acquired on
11 November 2017. The 2000–2011 lake outlines are from Tsutaki et al. (2019). The dA/dt values in (c) are the 2000–2011 (upper left) and
2011–2018 (lower right) lake expansion rates.
ResultsLake expansion
We traced the lake expansion for the 2000–2018 period (Fig. 2a and b).
The proglacial lake areas at the termini of Thorthormi and Lugge glaciers
were 3.05 and 1.58 km2 in 2018, an increase of 2.01 (193 %) and 0.48 km2 (44 %) from the 2000 lake areas, respectively (Fig. 2c). Both
lakes have expanded throughout the study period, and the lake expansion
rates (dA/dt) were calculated via a linear regression of the
cumulative areas during the 2000–2011 and 2011–2018 periods (Fig. 2c).
Lugge Glacial Lake steadily expanded during the 2000–2018 period, with
0.03 and 0.02 km2 a-1 observed before and after 2011,
respectively. However, there has been accelerated expansion of Thorthormi
Glacial Lake since 2011, with 0.07 km2 a-1 observed before 2011
and 0.13 km2 a-1 observed after 2011. A comparison of these
observations with the Hi-MAG data (Chen et al., 2021) indicates that the
expansion rates are in the upper 2.5 % (Thorthormi) and 10 % (Lugge) of
the observed proglacial lakes across the eastern Himalayas.
Comparison of the measured surface elevation change rates
of Thorthormi and Lugge glaciers from various studies.
Rate of surface elevation change (m a-1) PeriodThorthormi GlacierLugge GlacierReference1975–2000-0.16-1.20Maurer et al. (2019)2000–2016-1.30-3.50Maurer et al. (2019)2000–2016-1.29-3.81Brun et al. (2017)2004–2011 (DGPS)-1.40±0.27-4.67±0.27Tsutaki et al. (2019)2004–2011 (ASTER)-1.61±2.75-2.24±2.75Tsutaki et al. (2019)2011–2018-2.78±0.62-2.87±0.62This studyThinning rates
The dh/dt values of both glaciers were calculated from the 2011 GPS-DEM and
2018 HP-DEM, with the 2002 terminus position used as the base position for
the comparison. We also extracted the calculated dh/dt values from previous
studies that had focused on different time periods (Fig. 3 and Table 1). The
thinning rate of Lugge Glacier was more than 3 times greater than that
of Thorthormi Glacier for the 2004–2011 period, when Thorthormi Glacier was
a land-terminating glacier, and was then comparable (-2.78 m a-1) to
that of Lugge Glacier (-2.87 m a-1) for the recent 2011–2018 period,
when Thorthormi Glacier had evolved into a lake-terminating glacier (Fig. S1). There was a 2-fold increase and 0.61-fold decrease in the thinning
rates of Thorthormi and Lugge glaciers between the 2004–2011 analysis by
Tsutaki et al. (2019) and our presented 2011–2018 analysis. The RS-based
dh/dt values for the 2000–2016 period (-3.81 to -3.50 m a-1; Brun
et al., 2017; Maurer et al., 2019) are similar to the Tsutaki et al. (2019)
values (Table 1). The RS-based dh/dt values of Thorthormi and Lugge glaciers
are -0.16 and -1.20 m a-1 for the 1975–2000 period, respectively,
which suggests that the lower section of Thorthormi Glacier experiences
minimal thinning before 2000 (Maurer et al., 2019). The spatial distribution
of dh/dt along Thorthormi Glacier exhibited a decreasing trend in the
upglacier direction during the 2011–2018 period, whereas the dh/dt values
during the 2004–2011 period were almost constant across the same region.
The thinning rate was >4 m a-1 (dh/dt of less than -4 m a-1) in the upglacier area (>2500 m from the 2002 terminus)
during this later period. The dh/dt profiles obtained in previous studies do
not reveal such a remarkable trend; however, similar dh/dt plots are
independent of the distance from the terminus (Fig. 3a). The results of this
study reveal a large spatial variability compared with the RS-based dh/dt
distributions of previous studies (Brun et al., 2017; Maurer et al., 2019),
which is likely due to the differences in the spatial resolution of the
data. Tsutaki et al. (2019) reported that Lugge Glacier has a heavily
crevassed, bumpy surface. We consider that the high-resolution photogrammetry
data (0.5 m) identified the displacements due to these steep surface
features.
Surface elevation change rates (dh/dt) along (a) Thorthormi and (b) Lugge glaciers (based on the distance from the 2002
glacier terminus). Each panel shows the elevation change rates for
1975–2000 and 2000–2016 (Maurer et al., 2019; JM19), 2000–2016 (Brun et
al., 2017; FB17), 2004–2011 (Tsutaki et al., 2019; ST19), and 2011–2018
(this study). HP-DEM and GPS-DEM are resampled to a 30 m resolution for
comparison with the dh/dt datasets from previous studies.
Flow velocity
We calculated the surface velocity field between November 2016 and November 2017 and extracted the velocities along the central flowline (Figs. 4 and S2). We also plotted the ITS_LIVE product (2010–2018) and
Tsutaki et al. (2019) velocity profiles. We found fast flow velocities
(>200 m a-1) from the 2017 terminus to the middle part
(∼4200 m from 2002 terminus) of Thorthormi Glacier. The
ITS_LIVE velocity also exhibited a similar flow-velocity
magnitude near the 2017 terminus; however, the ITS_LIVE
velocities decreased more rapidly in the upglacier direction than our
calculated results. We are able to confirm the large displacement of a
surface feature (∼200 m a-1) from the Sentinel-2
satellite images at ∼3500–4000 m from the 2002 terminus
(Fig. S3), which suggests that the flow-velocity profile for Thorthormi
Glacier that is calculated in this study should be more reliable than the
automatically derived ITS_LIVE flow-velocity profile. A
comparison of our velocity profile (2016–2017) with the 2002–2010 velocity
profile (Tsutaki et al., 2019) reveals a substantial 2–4-fold increase at
∼2400–4000 m from the 2002 terminus. The ITS_LIVE flow-velocity profiles indicate that until 2016 the Thorthormi Glacier shows
similar flow velocity profiles to the 2002–2010 average, and
remarkable increases are observed in 2017 and 2018 (Fig. 4a). Tsutaki et al. (2019) projected the flow velocities of Thorthormi Glacier under the
assumption of a “lake-terminating condition” (dashed line in Fig. 4a).
Although the magnitude is ∼100 m a-1 less than that in
this study, the increasing flow velocities toward the calving front are
similar to the trend in the recent velocity profile.
Central flowline velocities of (a) Thorthormi and (b) Lugge
glaciers. Dashed vertical lines indicate the glacier terminus positions in
2002, 2011, and 2017. The red lines represent the flow velocity that was
calculated in this study (2016–2017). Interannual velocities (2010–2018)
are extracted from the ITS_LIVE velocity product. The black
lines and grey shaded regions represent the mean and standard deviation of
the flow velocities for the 2002–2010 period, respectively (Tsutaki et al.,
2019). The thick dashed line in (a) denotes the simulated surface velocities
with a lake-terminating assumption (Tsutaki et al., 2019).
Conversely, the calculated surface velocities of Lugge Glacier are
∼50 m a-1 up to ∼2000 m from the 2002
terminus in this study (2016–2017). There also appears to be a gradual
decrease up to ∼2700 m from the 2002 terminus, and it possesses a
similar velocity magnitude/trend to the 2002–2010 mean velocity calculated
by Tsutaki et al. (2019; Fig. 4b). The ITS_LIVE velocity
profiles (2010–2018) show flow velocities of <5 m a-1 for the
entire glacier, which is probably due to the coarser resolution (240 m) of
the velocity field compared with that in this study (10 m) and Tsutaki et al. (2019; 15 m). Although the terminus position of Lugge Glacier has
retreated almost 1 km since 2002, the mean velocity profile appears to have
remained persistent between the 2000–2010 (Tsutaki et al., 2019) and
2016–2017 observation periods.
Comparison of the emergence velocity of
Thorthormi Glacier in 2011 and 2017. The mean values are calculated for the
2400–3500 m section from the 2002 terminus (Fig. S4). Two basal sliding
conditions are assumed, whereby depth-averaged velocity equals either 90 %
or 100 % of the surface velocity.
2011 2017 Depth-averaged velocity90 %100 %90 %100 %Emergence velocity (m a-1)5.20±3.785.78±4.20-0.69±11.65-0.77±12.94Ice emergence velocity and flotation potential of Thorthormi Glacier
We calculated the emergence velocity of the Thorthormi Glacier terminus
under the assumption that the depth-averaged velocity equals either 90 %
or 100 % of the surface velocity. The resultant Ve values are -0.69±11.65 and -0.77±12.94 m a-1, respectively (2400–3500 m from the 2002 terminus; Fig. S4 and Table 2), although there are large
variations depending on the computational area (Fig. S4). The
land-terminating condition yielded Ve values of 5.20±3.78 and
5.78±4.20 m a-1, respectively, for the above-mentioned
depth-averaged velocity assumption (Table 2). These results suggest that the
mean Ve has decreased and become negative after transitioning to a
lake-terminating glacier. Tsutaki et al. (2019) also estimated Ve via
numerical modelling of the lake- and land-terminating conditions, yielding
2.2±1.9 and 6.3±2.2 m a-1, respectively.
We also estimated the potential ice flotation index in the terminus area of
Thorthormi Glacier (up to 3500 m from the 2002 terminus). The mean Pf
values for 2011 (land-terminating) and 2018 (lake-terminating) are 86 %
and 97 %, respectively, with this increase attributed to the surface
lowering of the terminus area during the 2011–2018 period. As a result of
surface lowering, some parts of the ice in the terminus area reached
ice flotation thickness (Pf>100%) by 2018.
DiscussionContrasting temporal changes in the glacier regimes
Thorthormi and Lugge glaciers possess contrasting dh/dt trends and flow
velocities even though they are adjacent to each other. The dh/dt trend
and the flow-velocity magnitude and spatial distribution of Lugge Glacier are
approximately constant between the 2004–2011 and 2011–2018 periods and 2002–2010 and 2016–2017 periods, respectively (Figs. 3b and 4b). Conversely,
remarkable increases in the thinning rate and flow velocity are observed
across Thorthormi Glacier over the same study periods (Figs. 3a and 4a).
Such a drastic velocity increase within a decade has not been reported in
the Himalayas, although the multi-decadal acceleration of glacier thinning
and deceleration of glacier flow have been reported (Dehecq et al., 2019;
Maurer et al., 2019).
Tsutaki et al. (2019) performed finite-element simulations of present
(land-terminating) and future (lake-terminating) Thorthormi Glacier
dynamics. Their simulations reproduced the flow velocities for the
land-terminating condition with a small root-mean-square error (<10 m a-1) using satellite-based flow velocities. Their future prediction
for a lake-terminating condition, which suggested an increase in flow
velocity, is inconsistent with our 2017 velocity analysis (Fig. 4a).
However, Tsutaki et al. (2019) highlighted that changes to the sliding
coefficient and ice thickness parameters could alter the flow velocity
significantly as their sensitivity tests demonstrated that the simulated
flow velocity increased (decreased) by 33 % (51 %) if the sliding
coefficient and ice thickness changed by +30 % (-30 %) for the
land-terminating condition of Thorthormi Glacier. Therefore, the difference
between the observed and simulated velocities is likely due to the
uncertainties in the sliding coefficient, ice thickness, and state of the
terminus position. Despite this underestimation, Tsutaki et al. (2019) have
reasonably demonstrated the change in sliding conditions associated with the
transition from land- to lake-terminating conditions.
Dynamic thinning triggered by terminus detachment
Lateral lakes had formed on both sides of the Thorthormi Glacier terminus
several years before 2011 (Fig. 2a and c). The ice thickness was near
flotation (Pf>85 %) such that the flow velocities could
accelerate near the terminus. However, the flow velocities decreased toward
the terminus, producing a longitudinal compression field and subsequent
surface lowering that might have been less than that of lake-terminating
Lugge Glacier (Tsutaki et al., 2019). The longitudinal stress field regime
changed from compressional to extensional after the terminus detached from
the terminal moraine and transitioned to a lake-terminating condition, and
its flow increased owing to efficient basal sliding. Although the satellite
imagery shows that the glacier terminus detached from the terminal moraine
in 2011 (Fig. 2a), the rapid increase in flow velocity since 2017 suggests
that the glacier terminus was in contact with the terminal moraine
underwater until 2016 and detached between 2016 and 2017 (Fig. 4a).
Furthermore, the lakes that formed on both sides of the glacier terminus may
also have reduced the lateral resistive stresses that prevented glacier flow
(e.g. Adhikari and Marshall, 2012). These factors might have led to the observed
dramatic increase in flow velocities (Fig. 4a). Such an increase in flow
velocities due to proglacial lake formation has been observed in other
regions (e.g. Boyce et al., 2007; Tsutaki et al., 2011; Sakakibara and
Sugiyama, 2014); however, this is the first observation of such a phenomenon
in the Himalayan region.
The rapid increase in flow velocities may have enhanced the ice flux towards
the glacier terminus due to the longitudinal strain. Positive emergence
velocities are distributed up to 2400–3500 m from the 2002 terminus for the
2011 land-terminating condition (Sect. 4.4). However, Ve decreased in
2017 and became negative due to the increase in flow velocities toward the
terminus. The unchanged thinning rate and velocity regime of Lugge Glacier
(Figs. 2b and 3b and Table 1) suggest that any recent climatic changes in
the Lunana region could not have yielded a significant increase in surface
ablation. Therefore, the increased thinning rate of Thorthormi Glacier can
be largely attributed to the decrease in Ve. However, the decrease in
Ve (approximately -6 m a-1; Table 2) seems to be too large to
account for the increased thinning rate from 2004–2011 to 2011–2018
(-1.38 m a-1; Table 1). We quantified the change in emergence
velocity by hypothesizing that this change occurred in the last 2 years
(2017 and 2018), during which time the surface velocity accelerated (Fig. 4a). The weighted average of the emergence velocity for the 2011–2018
period (Ve,2011–2018, m a-1) is described as follows:
Ve,2011–2018=ΔtlandVe,land+ΔtlakeVe,lakeΔtland+Δtlake,
where Ve and Δt are the emergence velocity and
duration of the land- or lake-terminating conditions, respectively. We
obtained a time-weighted mean emergence velocity of 3.52 m a-1 for the
2011–2018 period based on emergence velocities of Ve,land=5.20 and Ve,lake=-0.69 m a-1 (assuming
a depth-averaged velocity that is 90 % of the surface velocity; Table 2)
and periods of Δtland=5 and
Δtlake=2. This means that Ve
decreased by -1.68 m a-1 around 2011, which is consistent with the
2004–2011 to 2011–2018 change in dh/dt of -1.38 m a-1. The highly
variable Ve profiles suggest that there are large uncertainties in the
estimates (Fig. S4 and Table 2); however, our first-order evaluation can
explain the cause of the drastic change in the thinning rate of Thorthormi
Glacier.
We simply assumed a constant glacier width to calculate Ve along the
central flowline. However, this glacier terrain tends to widen in the
downglacier direction, yielding an extensional velocity regime. The lateral
proglacial lakes on both sides of the terminus before it transitioned to a
lake-terminating condition may have further contributed to a more negative
Ve than that estimated along the central flowline. Despite these
favourable conditions to enhance dynamic thinning, the surface lowering of
Thorthormi Glacier has likely been suppressed by the compressive flow regime
of the land-terminating condition. The transition to a lake-terminating
condition should have caused a 2-fold increase in the thinning rate during
such a short period (Fig. 2a and Table 1).
These above-mentioned mechanisms might cause a positive feedback between
glacier thinning and the increase in flow velocity by enhancing each other.
Therefore, increased glacier thinning and surface velocity speed-up will
continue along Thorthormi Glacier in the future. The dynamic thinning of
lake-terminating glaciers has been discussed in other HMA regions (e.g.
Nuimura et al., 2012; King et al., 2018; Liu et al., 2020). However, our
study is the first reported observation of the dynamic changes during the
transition from land- to lake-terminating conditions, which have led to the
enhanced thinning of a Himalayan glacier.
This study employed a modelled ice thickness (lake depth) that was tuned
using point measurement data (Tsutaki et al., 2019) to estimate the dynamics
of Thorthormi Glacier. Previous studies have suggested that the surface flow
velocity of lake-terminating glaciers is sensitive to the terminus ice
thickness and lake water depth (Benn et al., 2007; Pronk et al., 2021).
Therefore, constraints on the lake bathymetry may allow us to better
understand past and current terminus conditions and quantify the dynamic
thinning process.
Conclusions
We presented the surface elevation and velocity changes and proglacial lake
expansion of lake-terminating Thorthormi and Lugge glaciers in the Lunana
region, Bhutanese Himalaya. We analysed satellite and photogrammetry data and
compared our results with those in previous studies to reveal the recent
glacier and proglacial lake changes of Thorthormi Glacier, which are
associated with the transition from land- to lake-terminating conditions.
Whilst the lake expansion and surface lowering rates of Luge Glacier have
been approximately constant since 2000, those of Thorthormi Glacier have
exhibited a continued increase after the terminus reached flotation and
detached from the terminal moraine. There has been a 2-fold increase in
the thinning rate of Thorthormi Glacier since this transition to
lake-terminating conditions. The flow-velocity field of Thorthormi Glacier
has also sped up considerably (>150 m a-1), whereas that of
Lugge Glacier has remained unchanged. We estimate that the rapid thinning
and increased flow-velocity field of Thorthormi Glacier were due to this
transition to lake-terminating conditions. This study provides the first
evidence of the dynamic glacier changes associated with proglacial lake
formation in the Himalayan region and will contribute to advancing our
understanding of the dynamics of lake-terminating glaciers, as well as their
potential evolution in the future.
Data availability
The Landsat 7 ETM+, Landsat 8 OLI, and
Sentinel-2 satellite data are distributed by the United States Geological
Survey (https://earthexplorer.usgs.gov/, USGS, 2021).
The ASTER-DEM data are distributed by the National Institute of Advanced
Industrial Science and Technology
(https://gbank.gsj.jp/madas/map/index.html, AIST, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-16-2643-2022-supplement.
Author contributions
KF designed the study. HI and K
conducted the photogrammetry survey. YS processed the photogrammetry data
and analysed the satellite data. YS, KF, and AS wrote the manuscript. All of
the authors contributed to the discussion.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Jamyan Chopel and Shiro Ohmi for
supporting the aerial photogrammetry survey. We are indebted to Shun Tsutaki
and Takayuki Nuimura for providing their data and supporting our data analysis. We
thank Etienne Berthier for providing the Pléiades satellite data. We
appreciate Homa Kheyrollah Pour and two anonymous referees for their insightful and
constructive comments.
Review statement
This paper was edited by Homa Kheyrollah Pour and reviewed by two anonymous referees.
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