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Abstract and Figures

Mass loss of Himalayan glaciers has wide-ranging consequences such as changing runoff distribution, sea level rise and an increasing risk of glacial lake outburst floods (GLOFs). The assessment of the regional and global impact of glacier changes in the Himalaya is, however, hampered by a lack of mass balance data for most of the range. Multi-temporal digital terrain models (DTMs) allow glacier mass balance to be calculated. Here, we present a time series of mass changes for ten glaciers covering an area of about 50 km2 south and west of Mt. Everest, Nepal, using stereo Corona spy imagery (years 1962 and 1970), aerial images and recent high resolution satellite data (Cartosat-1). This is the longest time series of mass changes in the Himalaya. We reveal that the glaciers have been significantly losing mass since at least 1970, despite thick debris cover. The specific mass loss for 1970-2007 is 0.32 ± 0.08 m w.e. a-1, however, not higher than the global average. Comparisons of the recent DTMs with earlier time periods indicate an accelerated mass loss. This is, however, hardly statistically significant due to high uncertainty, especially of the lower resolution ASTER DTM. The characteristics of surface lowering can be explained by spatial variations of glacier velocity, the thickness of the debris-cover, and ice melt due to exposed ice cliffs and ponds.
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The Cryosphere, 5, 349–358, 2011
© Author(s) 2011. CC Attribution 3.0 License.
The Cryosphere
Multi-decadal mass loss of glaciers in the Everest area (Nepal
Himalaya) derived from stereo imagery
T. Bolch
, T. Pieczonka
, and D. I. Benn
Institut f
ur Kartographie, Technische Universit
at Dresden, Germany
The University Centre in Svalbard, Norway
Geographisches Institut, Universit
at Z
urich, Switzerland
School of Geography and Geosciences, University of St Andrews, UK
Received: 1 December 2010 – Published in The Cryosphere Discuss.: 20 December 2010
Revised: 28 March 2011 – Accepted: 30 March 2011 – Published: 20 April 2011
Abstract. Mass loss of Himalayan glaciers has wide-ranging
consequences such as changing runoff distribution, sea level
rise and an increasing risk of glacial lake outburst floods
(GLOFs). The assessment of the regional and global im-
pact of glacier changes in the Himalaya is, however, ham-
pered by a lack of mass balance data for most of the range.
Multi-temporal digital terrain models (DTMs) allow glacier
mass balance to be calculated. Here, we present a time series
of mass changes for ten glaciers covering an area of about
south and west of Mt. Everest, Nepal, using stereo
Corona spy imagery (years 1962 and 1970), aerial images
and recent high resolution satellite data (Cartosat-1). This is
the longest time series of mass changes in the Himalaya. We
reveal that the glaciers have been significantly losing mass
since at least 1970, despite thick debris cover. The specific
mass loss for 1970–2007 is 0.32±0.08mw.e.a
, however,
not higher than the global average. Comparisons of the re-
cent DTMs with earlier time periods indicate an accelerated
mass loss. This is, however, hardly statistically significant
due to high uncertainty, especially of the lower resolution
ASTER DTM. The characteristics of surface lowering can be
explained by spatial variations of glacier velocity, the thick-
ness of the debris-cover, and ice melt due to exposed ice cliffs
and ponds.
1 Introduction
Recent debate on whether Himalayan glaciers are shrinking
faster than those in other parts of the world (Cogley et al.,
2010) highlighted the lack of knowledge about the glaciers
in this region. The best measure of glacier health is mass bal-
ance, which can be directly linked to climate and compared
Correspondence to: T. Bolch
to other regions. The topographic setting and the glacier
hypsometry modify the way the climatic signal is translated
into glacier mass balance, but this can calculated using a
DTM (Paul and Haeberli, 2008). However, only a few in-situ
mass balance measurements have been made on Himalayan
glaciers, and existing data series are short (Kulkarni, 1992;
Fujita et al., 2001; Wagnon et al., 2007; Dobhal et al., 2008).
Comparisons of digital terrain models for different years can
complement field measurements, and allow regional mass
balance to be estimated (Bamber and Rivera, 2007). How-
ever, to date it has only been applied to some glaciers in the
western Himalaya for 1999 to 2004 (Berthier et al., 2007)
and to four glaciers at Mt. Everest for 1962 to 2002 (Bolch
et al., 2008b). Broader and more detailed knowledge of
glacier mass balance is also needed to decrease the high
uncertainty about the importance of Himalayan glaciers for
water resources (e.g. Immerzeel et al., 2010) and sea level
rise (e.g. Braithwaite and Raper, 2002). Finally, improved
knowledge of glacier recession is needed to better estimate
hazards of glacier lake outburst floods (GLOFs; Richardson
and Reynolds, 2000).
The aims of this study are threefold. First, we aim to eval-
uate the results of the pilot study by Bolch et al. (2008b) us-
ing independent data sets. This study revealed surface low-
ering of 0.33 ±0.24 m a
by analysing a 1962 Corona and
an ASTER DTM generated based on 2001, 2002 and 2003
data but had high uncertainties. The second aim is to present
mass balance estimates for a larger sample of glaciers around
Mt. Everest, including Imja Glacier which is of high interest
due to the proglacial lake which formed in the 1960s and has
rapidly grown since (Bolch et al., 2008a; Fujita et al., 2009).
In addition, the mass balance of the entire Khumbu Glacier
will be presented for the first time. Thirdly, we wanted to pro-
duce the first time-series of glacier volume changes around
Mt. Everest to show the suitability of different optical im-
agery to derive mass balance variability over time and to dis-
cuss the possible causes of the surface changes.
Published by Copernicus Publications on behalf of the European Geosciences Union.
350 T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya)
Fig. 1. Study area; location, names and debris-covered portion of the glaciers in the study area, coverage of the utilized satellite data.
Background: SRTM3 CGIAR, Vers. 4, study area: ASTER DTM; glacier outlines based on Bolch et al. (2008b).
The tongues of nine of the ten studied glaciers are heav-
ily covered by supraglacial debris (Fig. 1), with a debris
area of about 36.3km
and an average debris thickness in-
creasing downglacier (Nakawo et al., 1999; Hambrey et al.,
2008). The glaciers are mainly nourished by snow and ice
avalanches which accumulate cones below the steep head-
walls. Only Khumbu Glacier has an extensive accumu-
lation area (called “Western Cwm”). Glacier equilibrium
line altitudes (ELAs) are roughly estimated to be situated
above 5600m (Asahi, 2001). Ice velocities typically de-
crease downglacier from the ELA and most of the studied
glaciers have extensive stagnant ice in their lower reaches
(Bolch et al., 2008a; Scherler et al., 2008; Quincey et al.,
2009). Between 1962 and 2005 the overall glacier area
loss in the study area was 5% with an increasing debris-
covered area (2.5%) but almost stable terminus positions
(Bolch et al., 2008b).
2 Data and methodology
We used 1970 Corona KH-4B (declassified US spy imagery)
data, 1984 aerial photographs (camera: Wild RC 10) (Al-
therr and Gr
un, 1990) and 2007 Cartosat-1 (Indian Remote
Sensing Satellite, IRS P5) images (Table 1). In addition, we
used previously generated 1962 Corona and 2002 ASTER
DTMs (Bolch et al., 2008b). We did not consider the DTM
data from the Shuttle Radar Topography Mission (SRTM)
due to large data gaps especially at the clean ice area of
Khumbu Glacier and the coarser spatial resolution (90 m)
in comparison to the ASTER DTM (30 m). In addition, us-
ing only stereo optical data results in a methodologically
consistent data set. The SRTM DTM data in contrast are
based on C-band adar beams that penetrateinto snow and can
cause higher uncertainties in the snow-covered accumulation
areas. We applied the Remote Sensing Software Package
The Cryosphere, 5, 349–358, 2011
T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) 351
Table 1. Utilized imagery and derived DTM characteristics based on the ice free area relative to the 2007 master DTM.
Date Sensor Image IDs Spatial Resolution (m) Vertical accuracy (m) Vertical accuracy (m)
before adjustment after adjustment
Imagery Original Mean elev. STDV Mean elev. STDV
DTM diff. diff.
15 Dec 1962 Corona KH-4 DS009050054DF173 173, 7.6 20 53.0 20.9 0.1 19.7
DS009050054DA175 175
20 Nov 1970 Corona KH-4B DS1112-1023DA163 163, 5.2 15 9.0 27.2 0.5 18.8
DS1112-1023DF157 157
20 Dec 1984 Wild RC-10 1440, 1441,1476,1477 0.5 15 8.2 11.2 1.8 7.8
20 Dec 2001 ASTER L1A.003:2005569609 15 30 12.8 29.5 1.5 10.1
21 Nov 2002 L1A.003:2009316881
23 Oct 2003 L1A.003:2018198969
13 May 2007 Cartosat-1 097071200102 (BAND A), 2.5 10 Reference Reference Reference Reference
097071200101 (BAND F)
Fig. 2. Elevation difference of 1970 Corona and 2007 Cartosat DTM before (left) and after adjustment (right).
Graz (RSG, 6.13 for processing
Corona, PCI Geomatica OrthoEngine 10.2 for Cartosat, and
Leica Photogrammetry Suite (LPS) 9.1 for the aerial images.
We used 14 non-differential GPS points acquired in 2006 and
2008 and 200 points from the National Geographic 1:50k
topographic map (Altherr and Gr
un, 1990) as ground con-
trol points (GCPs). The GPS points were mostly measured
along the main trekking routes within a height range between
3900m and 5600m. Their horizontal accuracy is about 7.9m
and their vertical accuracy in comparison to topographic map
height points is about 20.6m. The GCPs based on the map
are almost randomly distributed in accordance with the un-
systematic distribution of significant peaks and other geo-
morphological forms marked as height points. In addition,
these points represent different slope angles and aspects well
(Pieczonka et al., 2011). The RMSE
and RSME
of the
map were computed based on the GPS points to be 20.6 m
and 17.8m, respectively. This matches the results achieved
by Altherr and Gr
un (1990). In addition, we used automat-
ically selected tie points (TPs) to improve the sensor mod-
els. The overall quality of the generated raw DTMs appears
promising as the glacier tongues are almost fully represented
(Figs. 2, 3). Data gaps occur mainly due to snow cover and
cast shadow. The Cryosphere, 5, 349–358, 2011
352 T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya)
Fig. 3. DTM differences of the study area 1970–2007 and 2002–2007.
In order to measure glacier elevation changes as precisely
as possible it is recommended to adjust the DTMs relative
to each other (Nuth and K
ab, 2011). Tilts which occurred
especially in the Corona DTM were corrected using trend
surfaces calculated based on stable non-glacierised areas of
the DTMs (Fig. 2, Pieczonka et al., 2011). We observed
slight horizontal shifts of the generated DTMs although we
used the same GCPs for all images whenever possible. In
order to avoid biases introduced thereby and to improve the
z-accuracy, we choose the Cartosat-1 DTM as the master ref-
erence as it has a high spatial resolution and showed the low-
est mean elevation difference (5.9m) and standard deviation
(18.3m) relative to the SRTM3 DTM. We co-registered the
other DTMs to it by minimizing the standard deviation of
the elevation differences (Berthier et al., 2007). The applied
shifts varied between 5 and 30m. Altitudinal differences
which exceeded ±100 m (usually around data gaps and near
DTM edges) were omitted assuming that these values repre-
sent outliers. Similar assumptions were made by Berthier et
al. (2010). We resampled all DTMs bilinearly to the pixel
size of the coarsest DTM (30m) in order to reduce the effect
of different resolutions.
The relative uncertainties of the DTMs prior to and after
the adjustments were calculated based on the ice free terrain
relative to the 2007 master DTM. The mean difference be-
tween the final adjusted DTMs was in the range 0.1 to
1.8m while the RMSE
was 7.8 to 19.8m (Table 1). To
address the uncertainty of the elevation differences of the
glacierised areas we calculated statistical parameters for the
differences of ice covered and the non-ice covered areas sep-
arately (Table 2). The standard deviation of the non glacier
area (STDV
) or the RSME
can be used as a first esti-
mate of the uncertainty, but would probably overestimate it
as it does not account for the reduction of the error due to
averaging over larger regions (Berthier et al., 2007, 2010).
Another suitable measure for the uncertainty is the standard
error of the mean (SE) defined as
while n is the number of the included pixels. We choose a
decorrelation length of 600m for the ASTER DTMs with an
effective spatial resolution of 30m and a length of 400m for
all other higher resolution DTMs to minimize the effect of
auto-correlation. These numbers are slightly more conserva-
tive than the average value of 500m utilized by Berthier at
el. (2010) for DTMs with coarser resolution (mostly 40m).
Koblet et al. (2010) suggested a decorrelation length of
100m for DTMs based on aerial images of a spatial reso-
lution of 5m.
We used the standard error (SE) and the mean elevation
difference (MED) of the non glacier area (Table 2) as an
estimate of the uncertainty according to the law of error
e =
Volume change was calculated for each glacier assuming that
the density profile remains unchanged and that only ice is lost
or gained (Paterson, 1994; Zemp et al., 2010). To convert
volume changes into mass change, we assumed an ice den-
sity of 900kgm
and assigned an additional uncertainty of
7% due to lack of ground truth (Zemp et al., 2010). We inter-
polated small data voids (<10pixel) within the ice covered
The Cryosphere, 5, 349–358, 2011
T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) 353
Table 2. Statistics of the DTM differences for the investigated periods.
Period DTM DTM Mean elev. STDV Mean elev. STDV N SE
coverage coverage diff. no glac. diff. glac. glac. no glac. no glac.
study area glac. no glac.
) (km
) (m) (m) (m) (m) (m)
1962–1970 137.8 25.4 0.9 22.3 1.9 15.7 319 1.3
1970–1984 83.8 24.4 3.6 26.3 9.9 16.1 163 2.1
1984–2002 83.4 25.7 2.2 26.5 5.4 18.2 107 2.6
2002–2007 174.5 59.8 +2.2 22.8 3.2 13.5 214 1.6
1984–2007 80.6 20.9 +2.4 15.8 9.2 15.4 109 1.5
1970–2007 152.9 46.5 +2.2 19.4 13.2 15.6 293 1.1
glac.: glacier area, no glac.: non glacier area, N : number of considered pixels.
Table 3. Glacier volume loss and mass balance 1970–2007, and 2002–2007.
Period 1970–2007 Period 2002–2007
Glacier Glacier Glacier area Average Specific Glacier area Average Specific
Size covered elev. mass covered elev. mass
by DTM diff. balance Diff. by DTM balance
) (km
) (m) (mw.e.a
) (km
) (m) (mw.e.a
Changri Shar/Nup 13.0 6.85 11.6±2.5 0.28±0.08 6.93 1.6±2.7 0.29±0.52
Khumbu accumulation area
6.2 4.3 6.2±2.5 4.66 +1.2±2.7
Khumbu ablation area
10.8 10.0 13.9±2.5 10.1 4.0±2.7
Whole Khumbu 17 14.26 11.1±2.5 0.27±0.08 14.7 2.5±2.7 0.45±0.52
Nuptse 4.0 3.45 9.4±2.5 0.25±0.08 3.52 2.2±2.7 0.40±0.53
Lhotse Nup 1.95 1.86 7.6±2.5 0.18±0.07 1.86 5.7±2.7 1.03±0.51
Lhotse 6.5 6.71 10.7±2.5 0.26±0.08 6.71 6.1±2.7 1.10±0.52
Lhotse Shar/Imja
10.7 8.65 20.6±2.5 0.50 ±0.09 8.87 8.1±2.7 1.45±0.52
Amphu Laptse 1.5 1.05 10.0±2.5 0.24±0.08 1.08 4.3±2.7 0.77±0.52
Chukhung 3.8 1.88 5.3±2.5 0.30±0.08 3.2 +0.2±2.7 +0.04±0.54
Amadablam 2.2 1.86 12.0±2.5 0.29±0.08 2.5 3.1±2.7 0.56±0.52
Duwo 1.0 0.37 12.2±2.5 0.30±0.08 0.37 10.9±2.7 1.96±0.53
Sum/Average 61.7 46.9 13.3±2.5 0.32±0.08 49.6 4.4±2.7 0.79±0.52
We assumed an ELA of 5700m based on Ashai (2001) and interpretation of the satellite images.
For the year 2007, the estimated volume of Imja lake (37.8 ×10
, calculated based on the area extent 0.91 km
and the average depth of 41.4m) was added to the elevation
difference of 1970–2007. For 2002–2007 we added the volume difference between 2007 and 2002 (2.0×10
). Data is based on Fujita et al. (2009).
areas using a spline algorithm. We did not fill the larger data
gaps e.g. on steep slopes. The glacier tongues, the avalanche
cones, and Western Cwm are represented in the DTMs of
1970, 2002, and 2007 (Fig. 3, Tables 3, 4) which allow es-
timation of the mass balance for the entire glacier. Only
Changri Nup, Duwo, and the debris-free Chukhung Glacier
have larger data gaps. Detailed investigations on Khumbu
Glacier are limited to the tongue below 5700 m (mainly the
ablation area) due to the small coverage of the aerial images.
3 Volume changes and mass losses
3.1 Periods 1970–2007 and 2002–2007 for the whole
study area
Between 1970 and 2007 significant surface lowering oc-
curred on all investigated glaciers (Fig. 3, Table 2). The
greatest lowering was on Imja/Lhotse Shar Glacier. Except
for this glacier, which displays surface lowering throughout
the terminus, most glaciers show maximum lowering in their
mid ablation zones, with a negligible change near their ter-
mini. Overall ice loss is estimated to be >0.6km
with an
average surface lowering of 0.36 ± 0.07m a
or a specific The Cryosphere, 5, 349–358, 2011
354 T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya)
Fig. 4. DTM differences on Khumbu Glacier for different times.
mass balance of 0.32 ±0.08 m w.e. a
between 1970 and
2007 (Table 3). The surface lowering for the debris-covered
parts only is 0.39±0.07ma
, clearly showing that signif-
icant thickness loss occurs despite thick debris cover. Most
glaciers also experienced surface lowering between 2002 and
2007 (Fig. 3, Table 3). The specific mass balance of all
10 glaciers has possibly doubled compared to 1970–2007
). However, the uncertainty is high.
3.2 Detailed multi-temporal investigations on Khumbu
The surface of the ablation area of Khumbu Glacier lowered
in all investigated time periods (Table 3). DTM differenc-
ing (Figs. 3 and 4) and longitudinal profiles, in particular for
1970–2007, show almost no surface lowering in the clean ice
zone below Khumbu Icefall (Figs. 5 and 6, section A); an
increasing downwasting in the debris-covered part, with the
highest lowering between 2 and 8km from the terminus (B,
Table 4. Volume loss of the ablation area of Khumbu Glacier
Time DTM coverage Average down- Downwasting rate
) wasting (m) (ma
1962–1970 4.9 2.74±1.54 0.34±0.19
1970–1984 9.9 2.53±4.16 0.18±0.29
1984–2002 9.8 6.72±2.78 0.37±0.16
2002–2007 10.0 3.95±2.68 0.79±0.52
1984–2007 9.8 13.00±2.84 0.56±0.13
C), and almost no lowering within 1.5km of the terminus
(D). For 1970–1984 only lowering between 1.5 and 5.5 km
of the terminus is significant (Fig. 5). Between 1970 and
2007, average surface lowering rate in the ablation area was
. The rate for 1984–2002 is higher than
for 1962–1970 and 1970–1984, and comparison of the re-
cent DTMs (2002–2007) suggests further accelerated surface
The Cryosphere, 5, 349–358, 2011
T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) 355
Fig. 5. (A): Khumbu Glacier based on the 2007 Cartosat-1 image
including the profile and the different sections of surface lowering
(see Fig. 6A). (B, C): Terminus of Khumbu Glacier based on the
1970 Corona KH-4B and on the 2007 Cartosat-1 image.
lowering (Table 4). However, these differences are statisti-
cally not significant. Comparing the periods 1970–1984 and
1984–2007, however, shows an almost statistically signifi-
cant increase in the surface lowering rate (0.18 ±0.29 m a
in comparison to 0.56±0.13 m a
). The accumulation zone
of Khumbu Glacier has possibly also thinned during the in-
vestigated time, while there might be a slight thickening in
recent time (2002–2007).
4 Discussion
Stereo capability, acquisition in the 1960s and 1970s and
relatively high spatial resolution make Corona KH-4, KH-
4A and B imagery a valuable source for geodetic mass bal-
ance estimations. While the earlier KH-4 data were already
found to be suitable for this task (Bolch et al., 2008b) we
have shown that the later higher resolution KH-4 results in
lower uncertainties. The accuracy and even distribution of
the ground control points used for the rectification of the
imagery are crucial for the resultant accuracy of the DTM.
Hence, it could be expected that an even better accuracy than
ours could be achieved if more precise GCPs are available.
Other reconnaissance images such as Hexagon KH-9 from
the 1970s and early 1980s are also suitable for this task if
coverage with no clouds and little snow cover are available
(Surazakov and Aizen, 2010). Surazakov and Aizen (2010)
have shown a RSME
for mountainous terrain of 20 m,
which is the same range as our results using KH-4B data
despite lower resolution of the KH-9 data. Reseau marks on
the KH-9 images facilitate minimizing the distortion. Corona
and Hexagon data are also of high value for mapping glaciers
and investigating area changes (Bhambri et al., 2011; Bolch
et al., 2010; Narama et al., 2010). The generation of mass
Fig. 6. (A): Profiles of the DTM differences of Khumbu Glacier.
(B): Longitudinal profiles of the surface elevation of Khumbu
Glacier 1970 and 2007. See Figs. 3 and 5 for the location of the
balance time series estimation of different data sets with dif-
ferent resolution requires careful co-registration and adjust-
ment. This is especially true for the 1984 DTM based on
aerial imagery, highlighted by a clear positive-negative trend
on hillslopes with opposing aspects for the periods 1970–
1984 and 1984–2002 (visible on the lower right of the im-
ages on Fig. 4). However, a shifting to adjust this area
would worsen the accuracy of the steep slope in the northern
part of the DTM. Hence, the distortion of the 1984 DTM is
more complex and could not be fully adjusted relative to the
others. Although inaccuracies remain on steep slopes with
all DTMs, most of the glacier area is not affected by these
biases. The inclusion of the lower resolution 2002 ASTER
DTM provides some insight in the recent volume loss of the
glaciers. However, the volume change of the glaciers is too
small for a significant signal to be detected. In addition, the
large scatter in the volume change amongst the investigated
glaciers needs further investigation.
The quality of the DTMs, however, is supported by lo-
cal details, such as the area on Khumbu Glacier arrowed on
Fig. 4. A large thinning occurred in this area between 1970 The Cryosphere, 5, 349–358, 2011
356 T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya)
and 1984, which can be attributed to the growth of a lake
basin visible on the 1984 aerial photograph (Supplement,
Fig. 4). Melting and calving around the margins of lakes
is well known to produce locally high ablation rates (Sakai et
al., 2000; Benn et al., 2001). Thickening occurred in this re-
gion between 1984 and 2002, attributable to drainage of the
lake reducing ablation and ice inflow from upglacier.
The utilized imagery also allows length changes of the
glaciers to be examined in detail. Bajracharya and Mool
(2009) argued that Khumbu Glacier has undergone termi-
nus retreat, based on comparison of recent imagery with
old topographic maps. Our images, however, indicate that
the terminus region of Khumbu Glacier has undergone very
little change in recent decades (Fig. 5b, c and Supple-
ment Fig. 1). This highlights the fact that debris-covered
glaciers may have stable terminus positions even though
they are in negative balance, and that volume changes are
more reliable indicators of glacier health than area changes
(cf. Scherler et al., 2011).
The calculated average 1970–2007 thickness changes for
the whole study area of 0.36 ±0.07 m a
, based on in-
dependent data sets, is very close to the value of 0.33±
presented by Bolch et al. (2008b) for 1962–2002,
but with smaller uncertainty. The wider coverage of this
study including Imja Glacier, and the accumulation area
of Khumbu Glacier, as well as lower uncertainties and the
multi-temporal coverage, allow greater insight into decadal
glacier changes and the influence of debris cover.
The longitudinal profile of glacier thinning of Khumbu
Glacier shows similar characteristics to those presented by
Nakawoet al. (1999) based on estimated ice flowand thermal
properties derived from Landsat data. Based on field mea-
surements, Kadota et al. (2000) found a slight surface lower-
ing (5m) about 1 km upglacier of the terminus and higher
surface loss (more than 10m in places) up to the pinnacle
zone close to Everest base camp for the period 1978–1995.
This is in good agreement with our data. These observa-
tions increase confidence in the observed patterns of down-
wasting, despite the remaining uncertainties.
The pattern of surface lowering on Khumbu Glacier can
be explained in terms of ice dynamics and surface melt
rates. Sustained high rates of ice delivery below the icefall
largely offset melt in the upper ablation area, where debris
cover is thin or absent (Figs. 5 and 6a, section A). Further
downglacier, thin debris cover increases the ice melt (sec-
tion B), in line with field measurements of increased sur-
face lowering (Takeuchi et al., 2000). Thinning rates remain
high downglacier despite an increasing debris thickness, due
to very low glacier velocities and ablation associated with
supraglacial lakes and exposed ice cliffs (Sakai et al., 2000,
2002) (section C, Supplement, Fig. 2). Almost no thinning
was observed within 1km of the terminus (section D), which
may reflect either a thick, complete debris cover or indicate
that ice loss is already complete. The possible slight surface
lowering in the accumulation area of the glacier from 1970
to 2007 might be due to less snowfall. This is consistent
with an ice core record at the East Rongbuk Glacier north of
Mt. Everest that indicates decreasing snow accumulation for
1970–2001 (Kaspari et al., 2008).
The pattern of downwasting has resulted in the devel-
opment of a concave glacier profile on Khumbu Glacier,
with very low surface gradients in the lower ablation zone
(Fig. 5b). This trend indicates that a glacial lake could de-
velop about 1.5 to 3 km upstream of the terminus, as was pre-
dicted in simulations using a 1D-coupled mass balance and
flow model by Naito et al. (2000). The formation of a large
lake on Khumbu Glacier would have major consequences for
outburst flood hazards downstream.
The greatest amount of mass loss occurred on Imja
Glacier, which can be at least partly attributed to the
proglacial Imja Lake, which enhances ice losses by calving.
This lake grew significantly since its formation in the late
1960s, reaching 0.9km
in 2008 (Supplement, Fig. 3, Fu-
jita et al., 2009; Bolch et al., 2008a). The comparatively thin
debris cover of Imja Glacier, apparent in exposed ice cliffs,
is likely to be another reason for the higher mass loss. At
Imja Glacier, a slight thinning is also observed on the relict
ice-cored moraine situated below Imja Lake. This is in line
with recent field measurements in this area which revealed
a lowering of about 1ma
(Fujita et al., 2009). The mass
loss of the smaller glaciers, such as Amphu Laptse Glacier,
amounts to half of that of Imja Glacier but is still significant.
Khumbu Glacier, for which data from the year 1984 was
available, showed a higher surface lowering for the period
1984–2007 in comparison to 1962–1984. The recent trend
of more negative mass balances since 2002, however, needs
further investigation, as it is not statistically significant. Ac-
celerated thinning could reflect decreasing velocity (Quincey
et al. 2009), higher air temperatures (Prasad et al., 2009), de-
creasing snow accumulation (Kaspari et al., 2008), or a com-
bination of these factors. The influence of black carbon (BC)
as summarized by Ramanathan and Carmichael (2008) can-
not be excluded but is negligible for the ablation zones with
debris-cover as BC does not lead into changes in the albedo
there. The catchment topography plays an important role for
the glacier flow regimes in Khumbu Himalaya (Quincey et
al., 2009). Forexample, Kangshung Glacier with a largehigh
altitude accumulation area flowing from Mt. Everest towards
the east, shows flow activity across the entire snout while all
other avalanche fed glaciers and glaciers with a lower accu-
mulation area such as Khumbu Glacier contain large stag-
nant parts. Our result shows comparatively little scatter of
the mass balance amongst the investigated, mainly avalanche
fed glaciers in the same study region. Mass balance esti-
mate of Kangshung Glacier would reveal how the catchment
topography and the higher flow influence the mass balance.
Unfortunately, only the upper part of Kangshung Glacier is
covered by our multi-temporal DTMs making a mass balance
estimate impossible.
The Cryosphere, 5, 349–358, 2011
T. Bolch et al.: Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) 357
The total mass balance of Khumbu Glacier for 1970–
2007, at 0.27±0.08mw.e.a
is lower than that observed
on several other Himalayan glaciers including Chhota Shi-
gri Glacier (0.98 mw.e.a
, 2002–2006 Wagnon et al.,
2007 and –1.02 to –1.12 m w.e. a
, 1999–2004 Berthier et
al., 2007) or the small debris-free Glacier AX010 (0.6 to
, 1978–1999 Fujita et al., 2001), but similar
to Dokriani Glacier (0.32mw.e. a
, 1992–2000 Dobhal
et al., 2008). However, the different observation times and
glacier sizes have to be considered, and Khumbu Glacier also
has possibly a more negative mass balance in recent years.
The tendency towards increased mass loss has also been ob-
served worldwide and for the few other Himalayan glaciers
with mass balance estimates (Cogley, 2011). The mass loss
of the investigated glaciers is similar to the average mass
loss of the 30 reference glaciers worldwide for 1976–2005
) (Zemp et al., 2009).
5 Conclusions
This study presents the longest time series of geodetically
derived mass-balance and glacier volume change estimates
obtained to date in the Himalaya. Geodetic mass-balance
estimates based on early stereo Corona and recent satellite
data are suitable for tracking glacier changes through time,
thus filling major gaps in glaciological knowledge of the Hi-
malaya and other mountain regions. However, careful rela-
tive adjustments of the DTMs are necessary to obtain suit-
able accuracies of DTMs based on different data sources
with different resolutions. Mass balance information is ur-
gently needed to improve knowledge of the response of Hi-
malayan glaciers to climate change and to allow prediction
of future glacier change and its influence on water resources,
river runoff, sea level rise, and glacial hazards.
Glaciers south of Mt. Everest have continuously lost mass
from 1970 until 2007, with a possibly increasing rate in re-
cent years. All glaciers lost mass despite partly thick debris-
cover. The highest loss was observed at Imja Glacier which
terminates into a lake. The specific mass balance of the in-
vestigated glaciers of 0.32±0.08mw.e. a
is not higher
than the global average.
Supplementary material related to this
article is available online at:
Acknowledgements. We thank M. Buchroithner for the support and
B. Shreshta, S. Bajracharja and P. Mool and the staff from ICIMOD,
Nepal for cooperation. We are grateful for the thorough reviews
of A. Racoviteanu, G. Cogley and an anonymous reviewer on an
earlier version of this paper. The comments by the anonomous
reviewer, D. Quincey and the short comments of M. Pelto and the
scientific editor A. K
ab improved it further. H. Raggam helped
with RSG software. GAF AG offered Cartosat-1 data at a reduced
price and D. Quincey provided the 1984 images. DFG (Deutsche
Forschungsgemeinschaft) provided financial support (Codes BU
949/15-1 and BO 3199/2-1). T. Bolch was partly funded through
the ESA project GlobGlacier (21088/07/I-EC).
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... Since glaciers are presently far from equilibrium (Marzeion et al., 2018;Zekollari et al., 2020;Miles et al., 2021), their debris cover is evolving through time (Stokes et al., 2007;Bhambri et al., 2011;Bolch et al., 2011;Shukla and Qadir, 2016;Tielidze et al., 2020). Indeed, medial moraines and debris patches -which are formed by the accumulation and transport of debris -tend to grow and to expand laterally with increasing ablation (Anderson, 2000;Jouvet et al., 2011;Rowan et al., 2015;Kienholz et al., 2017;Wirbel et al., 2018;Verhaegen et al., 2020). ...
... Additionally, ice-marginal moraines, which may become unstable when glaciers retreat, can supply the ice surface with additional debris (Van Woerkom et al., 2019). As a consequence, glaciers with negative mass balances tend to increase their debriscover fractions through time (Stokes et al., 2007;Bhambri et al., 2011;Bolch et al., 2011;Shukla and Qadir, 2016;Tielidze et al., 2020). In the Karakoram region, instead, positive and negative debris-cover changes have offset one another during the past 40 years . ...
... The lateral expansion of an already existing debris layer (e.g. medial and ice-marginal moraines, as well as isolated debris patches) is linked to the local mass balance (Stokes et al., 2007;Bhambri et al., 2011;Bolch et al., 2011;Shukla and Qadir, 2016;Tielidze et al., 2020). We describe the process of lateral expansion on a yearly time step by ...
Full-text available
Currently, about 12 %–13 % of High Mountain Asia’s glacier area is debris-covered, which alters its surface mass balance. However, in regional-scale modelling approaches, debris-covered glaciers are typically treated as clean-ice glaciers, leading to a bias when modelling their future evolution. Here, we present a new approach for modelling debris area and thickness evolution, applicable from single glaciers to the global scale. We derive a parameterization and implement it as a module into the Global Glacier Evolution Model (GloGEMflow), a combined mass-balance ice-flow model. The module is initialized with both glacier-specific observations of the debris' spatial distribution and estimates of debris thickness. These data sets account for the fact that debris can either enhance or reduce surface melt depending on thickness. Our model approach also enables representing the spatiotemporal evolution of debris extent and thickness. We calibrate and evaluate the module on a selected subset of glaciers and apply GloGEMflow using different climate scenarios to project the future evolution of all glaciers in High Mountain Asia until 2100. Explicitly accounting for debris cover has only a minor effect on the projected mass loss, which is in line with previous projections. Despite this small effect, we argue that the improved process representation is of added value when aiming at capturing intra-glacier scales, i.e. spatial mass-balance distribution. Depending on the climate scenario, the mean debris-cover fraction is expected to increase, while mean debris thickness is projected to show only minor changes, although large local thickening is expected. To isolate the influence of explicitly accounting for supraglacial debris cover, we re-compute glacier evolution without the debris-cover module. We show that glacier geometry, area, volume, and flow velocity evolve differently, especially at the level of individual glaciers. This highlights the importance of accounting for debris cover and its spatiotemporal evolution when projecting future glacier changes.
... where STDV nonglacier is the standard deviation of the elevation difference in the non-glacierized areas; N is the number of included pixels; this study choose a de-correlation length of 600 m for 30 m spatial resolution in this study (Bolch et al., 2011); SE is the standard error; and MED is the mean elevation difference in non-glacierized areas. The errors of multi-source DEMs are listed in Table 2. ...
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Mountain glaciers are an important component of the global hydrological cycle. Existing research about glacier changes in the Altai focused on limited regions. Study about recent glacier changes in the entire Altai Mountains is still lacking. We presented a consistent method for identifying glacier margins. The two new glacier inventories in 2000 and 2020 were derived from Landsat satellite imagery. Glacier surface elevation change and mass balance were obtained by comparing the 2000 Shuttle Radar Topography Mission (SRTM) and 2020 Digital Elevation Models (DEMs) generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spatial pattern of glacier changes was discussed in conjunction with climate trends. We mapped a total area of 1,096.06 ± 53.32 km2 around 2020, which amounts to 1,927 glaciers in the Altai Mountains. That was 12.02 ± 3.01% (or 0.60 ± 0.15%·a−1) less than the 1,245.75 ± 58.52 km2 around 2000. The geodetic mass balance of the monitoring glaciers in the Aktru basin for the period 2000–2011 was used to validate the geodetic survey. The average geodetic mass balance of -0.32 ± 0.09 m w. e.·a−1 on monitoring glaciers was slightly exaggerated than the observed mass balance of -0.26 m w. e.·a−1, but it was proved that the geodetic mass balance could reflect glacier changes in the Altai Mountains. An average mass loss of 14.55 ± 1.32 m w. e. (or 0.74 ± 0.07 m w. e.·a−1) was found during 2000–2020 in the Altai Mountains. Although the glacier area changes and mass balance were characterized by spatial heterogeneity, the glaciers in the Altai had experienced an accelerated shrinkage from 2000 to 2020 compared to the 20th century. The rising temperature is the foremost reason for glacier area shrinkage and mass loss according to the Climatic Research Unit (CRU) reanalysis data.
... Further, the glaciers with lower extents of debris-cover (<15%) showed higher glacier area loss compared to the sparsely debris-covered glaciers (15%-25% debris), however, the heavily debris-covered glacier having the debris extent of >25% showed a significantly lower area loss which indicates a significantly reducing trend of glacier area loss of the debris-covered glaciers with the increasing debris-cover (r 2 = 0.98; Fig. 8). The increase in the debris-cover due to the exposure of englacial debris by continuous melting in the upper reaches of ablation zone leads to further reduction in the glacier area loss (Stokes et al., 2007;Ali et al., 2017;Garg et al., 2017), and formation of supraglacial ponds and ice cliffs (Sakai et al., 2000;Bolch et al., 2011;Reid and Brock, 2014;Dobreva et al., 2017;Anderson et al., 2021). The accumulation of debris near glacier terminus results in the reduction of melt rates (Bhambri et al., 2011;Scherler et al., 2011;Basnett et al., 2013), as the thick debris-cover on the snouts reduces the retreat rate ( Fig. S2a; r = -0.35; ...
Out of 4530 glaciers covering ∼4770 km² in the Upper Indus Basin, viz., Chenab, Zanaskar and Suru sub-basins, we selected topographically and morphologically homogeneous set of 98 glaciers; 56 debris-covered and 42 clean to assess the impact of debris-cover on glacier melting. Satellite data analysis from 2000 to 2017 showed that the debris-covered glaciers have melted slower (2.49 ± 0.05%; 0.15%a⁻¹) than the clean glaciers (4.07 ± 0.03%; 0.24%a⁻¹). The intra-basin analyses revealed that clean and debris-covered glaciers in the Chenab have melted 2.34 ± 0.17% and 1.1 ± 0.30% (0.14%a⁻¹ and 0.06%a⁻¹) and retreated 8.82 ± 2.67ma⁻¹ and 5.7 ± 2.67ma⁻¹ respectively. Similarly, clean and debris-covered glaciers in the Zanaskar have melted at similar rates of 4.58 ± 0.07% and 4.55 ± 0.21% (0.27%a⁻¹ and 0.25%a⁻¹) and retreated 10.0 ± 2.67ma⁻¹ and 8.4 ± 2.67ma⁻¹ respectively. Clean and debris-covered glaciers in the Suru have melted 5.56 ± 0.09% and 4.53 ± 0.14% (0.33%a⁻¹ and 0.27%a⁻¹) and retreated 9.6 ± 2.67ma⁻¹ and 7.0 ± 2.67ma⁻¹ respectively. Extensive debris-cover of ∼32.3% modulates glacier loss in the Chenab and sparse debris-cover of 11.4% and 6.4% in the Suru and Zanaskar enhances the melting of the debris-covered glaciers. Overall, debris-covered glaciers melted slower than the clean glaciers, with inter- and intra-basin variations, suggesting that debris-cover plays a complex but significant role in modulating glacier melting under climate change.
... It is estimated that the total glacier area decreased by 24% between 1977 and 2010, and the estimated ice reserves by 29% in Nepal Himalaya (Bajracharya et al., 2014). Acharya A study on ten glaciers in the Everest region using the Corona spy imagery, aerial images and cartosat-1 images found significant loss of mass (-0.32 ± 0.08 m w. e. a -1 ) since at least 1970, despite the thick debris cover (Bolch et al., 2011). They also estimated the average elevation difference of -13.3 ± 2.5 m with maximum up to -20.6 ± 2.5 m on Imja/Lhotse Shar Glacier between 1970 and 2007. ...
... The spatial autocorrelation distance may be different with different spatial resolution. Koblet et al. (2010) and Bolch et al. (2011) assumed that the spatial auto-correlation of 20 pixels is negligible. Gardelle et al. (2013) identified autocorrelation distances of 492 ± 72 m for DEMs with 40 m spatial resolution, determined using Moran's I autocorrelation index, corresponding to nine study sites in the Pamir-Karakoram-Himalaya. ...
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Extensive efforts for quantifying regional glacier mass balance in the Qilian Mountains have been made using the geodetic method, but these estimations were rarely extended back to the period before 2000. This study presents glacier mass budgets in the Turgen Daban Range, over the western Qilian Mountain, from 1966/75 to 2020 by means of the digital elevation models generated by the topographic maps and ASTER images. The results show that the glacier mass decreased by −18.79 ± 12.48 m w.e. during the past 50 years. The average mass loss rate is estimated to be −0.19 ± 0.08 m w.e.a−1 for the 1966/75-2006 period and −0.45 ± 0.17 m w.e.a−1 during 2006–2020, respectively, suggesting a remarkable acceleration of glacier mass loss. This may be attributable to the significant increase in air temperature and the insignificant precipitation increase which cannot offset glacier melting caused by increased temperature. Due to the melting and shrinking of glaciers, the area of glacial lakes increases by 2.83 km2 from 1987 to 2020.
... Hereby, we could show that increasing mass loss during the last 50 years is a general phenomenon and that mass loss now prevails, even in regions with formerly balanced mass budgets [47]. The only region with comparable information apart from this study is the Mt Everest region, where [36,48] show a similar steady increase of mass loss since the 1960s. ...
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Meltwater from the cryosphere contributes a significant fraction of the freshwater resources in the countries receiving water from the Third Pole. Within the ESA-MOST Dragon 4 project, we ad-dressed in particular changes of glaciers and proglacial lakes and their interaction. In addition, we investigated rock glaciers in permafrost environments. Here, we focus on the detailed investigations which have been performed in the Poiqu River Basin, central Himalaya. We used in particular multi-temporal stereo satellite imagery, including high-resolution 1960/70s Corona and Hexagon spy images and contemporary Pleiades data. Sentinel-2 data was applied to assess the glacier flow. The results reveal that glacier mass loss continuously increased with a mass budget of −0.42 ± 0.11 m w.e.a−1 for the period 2004–2018. The mass loss has been primarily driven by an increase in summer temperature and is further accelerated by proglacial lakes, which have become abundant. The glacial lake area more than doubled between 1964 and 2017. The termini of glaciers that flow into lakes moved on average twice as fast as glaciers terminating on land, indicating that dynamical thinning plays an important role. Rock glaciers are abundant, covering approximately 21 km2, which was more than 10% of the glacier area (approximately 190 km2) in 2015. With ongoing glacier wastage, rock glaciers can become an increasingly important water resource.
... N is calculated using the number of pixels (N tot ) in the DEM differencing, the pixel size (PS), and the distance of spatial autocorrection, which following Bolch et al. (2011) we took to be equal to 20 pixels: In addition to EΔv i we also considered the uncertainties relating to the volume to mass conversion (E ρ ), which following Huss (2013) was taken to be ±60 kg m −3 and the error in delineating the glacier outline (E a ) which was assumed to be 5%. EΔv i was combined with E ρ and E a to derived the total uncertainty, EΔv tot : ...
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Glacierized mountain ranges such as the Himalaya comprise a variety of glacier types, including clean and debris-covered glaciers. Monitoring their behaviour over time requires an assessment of changes in area and elevation along with surface features and geomorphology. In this paper we quantify the surface evolution of glacier systems in the Manaslu region of Nepal over the last five decades using 2013/2019 multi-sensor imagery and elevation data constructed from 1970 declassified Corona imagery and 1970 declassified Corona imagery. We investigate area changes, glacier thickness, geodetic glacier mass balance and surface velocity changes at regional scales and focus on the Ponkar Glacier and Thulagi Glacier and Lake for an in-depth assessment of surface geomorphology and surface feature dynamics (ponds, vegetation and ice cliffs). The time series of surface elevation changes for the lower ablation area of Ponkar Glacier is extended using 2019 UAV-based imagery and field-based ablation rates measured over the period 2016–2019. Glaciers in the Manaslu region experienced a mean area loss of −0.26 ± 0.0001% a−1 between 1970 and 2019. The mean surface lowering was −0.20 ± 0.02 ma−1 over the period 1970 to 2013, corresponding to a regional geodetic mass balance of −0.17 ± 0.03 m w. e.a−1. Overall, debris-covered glaciers had slightly higher thinning rates compared to clean ice glaciers; lake-terminating glaciers had double thinning rates compared to land-terminating glaciers. Individual glacier mass balance was negatively controlled by glacier slope and mean glacier elevation. During the period 1970 to 2013, Ponkar Glacier had a geodetic mass balance of −0.06 ± 0.01 m w. e.a−1, inversely correlated with parts of the central trunk thickening. Between 2013 and 2019 there was a nine-fold increase in the thinning rates over the lower parts of the glacier tongue relative to the period 1970–2013. Ice-surface morphology changes between 1970 and 2019 on Ponkar Glacier include a decrease in ogives and open crevasses, an increase in ice cliffs and ponds and the expansion of the supraglacial debris and ice-surface vegetation. These changes point to reduced ice-dynamic activity and are commensurate with the observed recession and negative glacier mass balance over the last five decades.
... Vol.: (0123456789) in various locations worldwide, including the Himalayas, due to its capacity to give synoptic vision and multi-spectral assessment of Earth planetary bodies (Navalgund et al., 2007). Publicly accessible remote sensing datasets such as Landsat, Sentinel, previously prepared topographic maps, and newly declassified CORONA pictures, together with datasets from other remote sensing datasets such as the Indian remote sensing satellite series, hyperspectral images, and other advanced spaceborne techniques, have been recently used to monitor the glacier retreat (Bolch et al., 2011). ...
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There are several causes for the increasing rate of deglaciation, such as global warming, increase in the concentration of black carbon, and extensive use of fossil fuels which causes the change in the overall climate system and shifting glacier ecosystem. This study was conducted on Pindari valley glaciers part of lesser Himalaya in Uttarakhand. This study investigates to (1) monitor and map change in the frontal length or the snout region of a glacier that can be studied with the help of remote sensing techniques and (2) evaluate the decadal and annual retreat rate of the glacier from 1972 to 2018. The study applies both the maximum likelihood classifier and NDSI spectral indices based classification for extracting the glacier region for different periods. This study reveals a significant amount of retreats taking place in the selected glaciers, Pindari, Sundardhunga, Kafni, and Baljuri base camp glaciers, from 1972 to 2018 as 1719.95 m, 1751.21 m, 1057.01 m, and 810.78 m, respectively. The highest amount of change is noticed in Pindari and Sundardhunga glaciers, higher than ~ 1700 m. The study analyses temporal variation of the annual and decadal retreat rate in the Pindari valley glaciers, which would be helpful for the further study of the other glaciers.
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On the Kamchatka Peninsula in the Russian Far East, 405 glaciers with an estimated total mass of 49 Gt were reported in the 1970s. These have been retreating at an accelerated rate since the start of the 21st century. Because glacier studies in this region are scarce, ice loss and its influence on sea level rise and regional environments is poorly understood. In this study, we analyzed satellite data to quantify glacier mass change from 2000 to 2016 in six major glacier-covered regions on the peninsula. The mean rate of the glacier mass change over the study period was −0.46 ± 0.01 m w.e. a−1 (total mass change was −4.9 ± 0.1 Gt, −304.2 ± 9.1 Mt a−1), which is slightly lower than other regions in mid-latitude and subarctic zones. The mass loss accelerated from >−0.33 ± 0.02 m w.e. a−1 in the period 2000–2006/2010 to <−1.65 ± 0.12 m w.e. a−1 in 2006/2010–2015/16. The increase in mass loss is attributed to a rise in average decadal summer temperatures observed in the region (+0.68°C from 1987–99 to 2000–13). Moreover, a recent trend in Pacific decadal oscillation suggests future acceleration of mass loss due to a decline in winter precipitation.
Surge-type glaciers switch between phases of rapid and slow flow on timescales of a few years to decades. During the active surge phase, large volumes of ice are transported downglacier, creating distinct geomorphological signatures that reflect these dynamic events but ice and sediment transport remain poorly quantified. The impact of surge events, in comparison to non-surge activity, is also unclear. Here, we describe glacier-surface debris and elevation changes through surges on ten different Karakoram glaciers (Khurdopin, Drenmang, Kunyang, Braldu, Chong Kumdan II, Qiaogeli, Saxinitulu, Shakesiga, Skamri and North Crown). We use these data to characterise the surface geomorphological changes during the surges. We also calculate the mass redistribution during each of the surges and compare this to an estimate of overall glacier mass. Repeat geomorphological mapping shows that surface debris transport through the surges leads to widespread rearrangement of surface features, folding and the concentration of debris near glacier termini, confluences and margins. Ice and debris mass redistribution varies between 0.11 and 0.94 Gt per event and shows moderate correlation with total glacier volume (r² = 0.57) and glacier length (r² = 0.40). Mass change as a proportion of total glacier volume ranges from about 0.5 to 5% and is moderately correlated with total glacier volume (r² = 0.43) but not glacier length (r² = 0.10). The overall conclusion is that surge events in this region account for only a small proportion of overall mass transport, but have an important role in surface debris transport and redistribution, as well as glacier mass balance.
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The accumulation area ratio (AAR) for Himalayan glaciers representing zero mass balance is substantially lower than for North America and Europe. Regression analysis suggests 0.44 for the AAR representing zero mass balance in the western Himalaya. A good correlation was observed when this method was applied to individual glaciers such as Gara and Gor-Garang in Himachal Pradesh, India. The correlation coefficients (r), using 6 and 7 years of data, respectively, were 0.88 and 0.96 for Gara and Gor-Garang Glaciers, respectively. However, when data from six western Himalayan glaciers were correlated, the correlation was 0.74. The AAR was also estimated by using Landsat images which can be useful in obtaining a trend in mass balance for a large number of Himalayan glaciers for which very little information exists. A higher correlation was observed between equilibrium-line altitude (ELA) and mass balance. The field data from Gara and Gor-Garang Glaciers shows a high correlation coefficient, i.e. −0.92 and −0.94, respectively. The ELA values obtained from the Landsat satellite images combined with topographic maps suggest positive mass balance for the year 1986–87 and negative for 1987–88.
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Ablation and heat balance were measured in debris-covered and debris-free areas of the ablation zone of Khumbu Glacier from 22 May to 1 June 1999. On the debris-free ice, the ablation rates ranged from 1.4 to 4.7 cm day" 1 and were inversely correlated with the albedo. The contribution of turbulent heat flux to melting was very small, so net radiation accounted for about 98% of the incoming heat. Melting under debris decreased sharply with increasing thickness. Debris with thickness of 10 cm slowed melting to about 40% of that of bare ice with the same low albedo. The primary cause of melt reduction was the insulating effect of the debris. The heat stored in the debris layer during daytime was released to the atmosphere during night-time and warmed the air rather than being conducted downward to melt ice.
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A new model for coupled mass balance and flow of a debris-covered glacier was developed to account for the effects of supraglacial debris on glacier evolution. The model is reasonably consistent with observations of recent shrinkage of the ablation area of Khumbu Glacier, Nepal Himalayas from 1978 to 1999. The model predicts formation and succeeding enlargement of a depression in the lower ablation area. This depression could result in the formation of a glacial lake. Potential improvements to the model for a debris-covered glacier are identified.
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The ablation amount for entire ice cliffs reaches about 2017( of that at the whole debris-covered area, the ablation rate at the ice cliff mainly depends on shortwave radiation, which differs widely with the orientation of all ice cliff. Therefore, the distribution of ice cliffs ill relation to their orientation was observed oil a debris-covered glacier. The south-facing, cliffs were small in area because they have low slope angles and tended to be covered with debris. The north-facing cliffs. oil the other hand, were large in the studied area and maintain a slope angle larger than the repose angle of debris. They, therefore, are stable. Longwave radiation from the debris surface opposite the ice cliffs was larger on the lower portion of ice cliffs than on the upper portion in every azimuth. This difference in longwave radiation maintained a steep slope angle on ice cliff. Shortwave radiation was stronger at the upper portion of ice cliffs than at tire lower portion due to tire local shading effect, causing gentle sloping of ice clift's. This was especially pronounced at cliffs facing to south. Therefore, the dependency of the ice cliff angle in orientation call be explained by tire difference in local radiation between the upper and lower portion of the ice cliff.
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There are many supraglacial ponds on debris-covered glaciers in the Nepal Himalayas. The heat absorbed at the surface of a pond was estimated from heat budget observations on the Lirung Glacier in Langtang Valley, Nepal. The results indicated an average heat absorption of 170 W m-2 during the summer monsoon season. This rate is about 7 times the average for the whole debris-covered zone. Analysis of the heat budget for a pond suggests that at least half of the heat absorbed at a pond surface is released with the water outflow from the pond, indicating that the water warmed in the pond enlarges the englacial conduit that drains water from the pond and produces internal ablation. Furthermore, the roof of the conduit could collapse, leading to the formation of ice cliffs and new ponds, which would accelerate the ablation of the debris-covered glacier.
The ablation amount for entire ice cliffs reaches about 20% of that at the whole debris-covered area; the ablation rate at the ice cliff mainly depends on shortwave radiation, which differs widely with the orientation of an ice cliff. Therefore, the distribution of ice cliffs in relation to their orientation was observed on a debris-covered glacier. The south-facing cliffs were small in area because they have low slope angles and tended to be covered with debris. The north-facing cliffs, on the other hand, were large in the studied area and maintain a slope angle larger than the repose angle of debris. They, therefore, are stable. Longwave radiation from the debris surface opposite the ice cliffs was larger on the lower portion of ice cliffs than on the upper portion in every azimuth. This difference in longwave radiation maintained a steep slope angle on ice cliff. Shortwave radiation was stronger at the upper portion of ice cliffs than at the lower portion due to the local shading effect, causing gentle sloping of ice cliffs. This was especially pronounced at cliffs facing to south. Therefore, the dependency of the ice cliff angle in orientation can be explained by the difference in local radiation between the upper and lower portion of the ice cliff.
Surface lowering of the Khumbu Glacier, a large debris-covered glacier in the Nepal Himalayas, was detected by means of ground surveying in 1978 and in 1995. Over this interval the surface of the glacier lowered about 10 m throughout the debris-covered ablation area. Lowering in the lowermost part of the glacier, where surface ablation may be negligible, might result from subglacial meltwater interaction. Indication that ice flow is slowing suggests that shrinkage may accelerate even if ablation conditions remain unchanged.