Glacial Lake Expansion in the Central Himalayas by Landsat Images, 1990–2010
Citation: Nie Y, Liu Q, Liu S (
Glacial Lake Expansion in the Central Himalayas by Landsat Images, 1990-2010
Yong Nie 0
Qiao Liu 0
Shiyin Liu 0
Guy J-P. Schumann, NASA Jet Propulsion Laboratory, United States of America
0 1 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences , Chengdu , China , 2 State Key Laboratory of Cryosphere Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences , Lanzhou , China
Glacial lake outburst flood (GLOF) is a serious hazard in high, mountainous regions. In the Himalayas, catastrophic risks of GLOFs have increased in recent years because most Himalayan glaciers have experienced remarkable downwasting under a warming climate. However, current knowledge about the distribution and recent changes in glacial lakes within the central Himalaya mountain range is still limited. Here, we conducted a systematic investigation of the glacial lakes within the entire central Himalaya range by using an object-oriented image processing method based on the Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper (ETM) images from 1990 to 2010. We extracted the lake boundaries for four time points (1990, 2000, 2005 and 2010) and used a time series inspection method combined with a consistent spatial resolution of Landsat images that consistently revealed lake expansion. Our results show that the glacial lakes expanded rapidly by 17.11% from 1990 to 2010. The pre-existing, larger glacial lakes, rather than the newly formed lakes, contributed most to the areal expansion. The greatest expansions occurred at the altitudinal zones between 4800 m and 5600 m at the north side of the main Himalayan range and between 4500 m and 5600 m at the south side, respectively. Based on the expansion rate, area and type of glacial lakes, we identified 67 rapidly expanding glacial lakes in the central Himalayan region that need to be closely monitored in the future. The warming and increasing amounts of light-absorbing constituents of snow and ice could have accelerated the melting that directly affected the glacial lake expansion. Across the main central Himalayas, glacial lakes at the north side show more remarkable expansion than those at the south side. An effective monitoring and warning system for critical glacial lakes is urgently needed.
Funding: This study was supported by the National Science and Technology Support Program of Chinese MOST (Grant No. 2012BAC19B07) and the National
Natural Science Foundation of China (Grant No. 41101082) and, also supported by the Foundation of IMHE for Young Scientists. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Glacial lake outburst floods (GLOFs) pose a serious hazard in
many high mountain communities around the world  and have
increasingly been given attention in recent years due to the
associated catastrophic damages and fatalities . The
Himalayan mountainous area is one of the hardest hit regions by
GLOFs [2,4,710]. GLOFs, or glacial lake expansions, can also be
seen as an indirect indicator of climate change. Consistent with the
expectations of global warming, most Himalayan glaciers are
losing mass [5,11,12]. Glacial recession in the Himalayas has
resulted in the development of glacial lakes, especially
morainedammed glacial lakes , which have increased the potential risk
for GLOFs in this region.
Remote sensing has been proven to be the most useful method
for the monitoring and early detection of GLOF hazards in remote
mountainous regions [1,3], especially as it offers the capability to
investigate a large area. Investigating glacial lakes over a large area
and monitoring glacial lake dynamics (temporal and spatial
changes) are of high priority for the mitigation of GLOF hazards.
Glacial lake inventories and change assessments were reported for
the Nepal [3,4,6,9], Chinese , Indian  and Bhutan
Himalayas  based on remote sensing, whereas a full regional
inventory for the entire Himalaya region has not been undertaken
due to political constraints. Although a regional assessment of
glacial lake distribution and evolution in the Hindu Kush
Himalayas had been reported , these studies are still limited
to a few typical regions.
Most GLOF events documented in the Himalayas occurred in
the central Himalayas and typically consisted of outburst floods
from moraine-dammed glacial lakes [2,4,7,9,10]. The central
Himalayas have an area of approximately 286104 km2 ,
forming a natural boundary between China and Nepal, as well as
between China and India (Figure 1). Documented GLOF events
(such as Lake Cirenmaco and Dig Tsho) in the central Himalayas
destroyed a downstream hydropower station, road and bridge,
killed hundreds of people and caused millions of dollars in
economic losses [2,7,9,16]. Potentially dangerous glacial lakes in
the central Himalayas, such as Lake Imja [6,9], Ludin Tsho ,
Gangxico, Galongco [2,17] and Lake Longbasaba , have been
identified by many scientific researchers.
This study focuses on the entire central Himalaya region,
aiming to analyze the glacial lake changes across the entire region
from 1990 to 2010, spanning four time points (1990, 2000, 2005
and 2010), and to identify the rapidly expanding glacial lakes that
need to be closely monitored in the future. Further discussions will
be made on the discrepancy between the spatial distribution and
changes in these glacial lakes between the north and south sides of
the main Himalayan range. This study significantly expands on
previous work by using homogeneous satellite imagery to
investigate the glacial lakes across the entire central Himalayan
mountain range. This research will promote the awareness of the
hazard potential of glacier lakes by providing some fundamental
information for the future assessment of disaster mitigation.
Data and Methods
The precisely ortho-rectified images of Landsat satellites with a
spatial resolution of 30 m were used in this study. A total of 56
Landsat TM/ETM images were downloaded from the Global
Land Survey dataset (Global Land Survey (GLS) 1990, GLS2000,
GLS2005 and GLS2010) of the United States Geological Survey
(USGS) and the Global Land Cover Facility (GLCF). The
probability of cloud cover in mountainous areas is high ,
which makes it particularly difficult to acquire same-month images
for glacial lake monitoring over the whole study area. In order to
reduce the effect of the difference in acquisition times, high-quality
images available from similar seasons were selected, focusing on
autumn and winter. The months in which the Landsat images
used in this study were acquired include September (8 scenes),
October (20 scenes), November (20 scenes), December (6 scenes)
and February (2 scenes). A hydrological observation at Rongbuk
Glacier, Mount Qomolangma (Everest), central Himalayas
revealed an ablation period of glaciers mainly from June to
August, and river discharge declined gradually, ending after
September . Areal variation of glacial lakes in a
highaltitudinal cold environment from September to February was
slight; thus, the effect of differences in the acquisition time on the
glacial lake change should be negligible. Although the quality of
the image strips in Landsat-7 ETM after 2003 are reduced, the
glacial lake extents can be clearly distinguished from the improved
ETM images through the malfunction of the scan lines corrector
(SLC-off) repair method and geo-rectification .
Compared with traditional automatic mapping methods, such
as supervised classification  or decision tree ,
objectoriented image processing is an advanced method widely used as
an automatic feature extraction technology due to higher accuracy
and efficiency for classification [20,22,25]. The Normalized
Difference Water Index (NDWI) [1,26] and Normalized
Difference Snow/Ice Index (NDSII)  were used to derive individual
glacial lakes or glacier extent. We developed an improved method
that combines the object-oriented image processing method and
expert knowledge using the two indices to extract a preliminary
glacial lake extent. The minimum area for lake mapping from TM
or ETM is 0.0081 km2, or 9 pixels. The workflow included NDWI
and NDSII calculation, band stacking, application of edge-based
segmentation algorithms , definition of classification rules and
exporting the extracted output. The detailed workflow and
procedure for the extraction of glacial lakes were reported in our
previous studies [19,20,22].
After the automatic extraction of glacial lake features, visual
inspection and correction based upon the field experience on
glacial lakes and Google Earth images must be employed to
eliminate the misinterpretation of lakes due to shadow, lake ice
formation, cloud and snow cover features across the four time
points. The missing parts of the lake boundaries from the ETM
striping were manually corrected based on earlier TM or ETM
images without striping. Centroids of shapes were calculated as the
index of label points for each glacial lake. Lakes with independent
labels were then joined spatially into the areal attributes for lakes
across the four time points. Cross-validation and modification for
each glacial lake was conducted according to the time series areal
variation. Accuracy assessment of the classification is difficult ,
because the field measurements of most glacial lake outlines are
impossible due to their steep edges in high and cold mountainous
areas. However, field experiences still help in confirming the
locations and shapes of glacial lakes during the process of visual
correction. A total of 131 glacial lakes in 2010 were randomly
selected for accuracy assessment. These lake outlines were
converted into. KMZ format and overlaid with Google Earth
images. The results indicated that approximately 98% of glacial
lake outlines were well matched in high resolution images by
eliminating the difference in acquisition year and spatial resolution
scale. The same selected 131 glacial lakes were also cross-checked
across the four time points, and there were no clear errors.
Therefore, a high-quality final glacial lake dataset was established.
Generally, the uncertainty in the measurement of the glacial lake
area was estimated by assuming an error of 6 0.5 or 6 1 pixel
[6,15,29]. In this study, because we used Landsat images with the
same resolution of 30 m for the overall mapping and the
coregistration errors were less than 1 pixel, we used 6 30 m (1 pixel)
as the co-registration error. The formula of uncertainty in the area
calculation reported by Ye et al.  was adopted.
The GLIMS glacier inventory data (available: http://glims.org/
download/accessed 2013 Nov 20.)  was used to view the
spatial relationships between glaciers and lakes. Misinterpretations
of the outlines of glaciers determined by automated classification
of satellite images were corrected by visual inspection and
modification. Based on the locations of the glacial lakes, they are
classified into (1) pro-glacial lakes that make contact with the
glaciers, (2) pro-glacial lakes without ice contact and (3)
supraglacial lakes, following the method suggested by Gardelle et al .
In this study, the identification of rapidly expanding glacial lakes
was based on the observations that (1) a lake area larger than
0.1 km2 is within a threshold size of potentially dangerous lakes
; (2) lake areal expansion is more than 20%  from 1990 to
2010; (3) a glacial lake is in contact with modern glaciers that are
supplied directly by glacial melt water [7,15].
Other data used in this study include SRTM DEM ; a field
survey on glacial lakes in 2008, 2009 and 2012 by our work team
in the Chinese Himalayas (Figure 2); weather observations at four
meteorological stations (Tingri, Nyalam, Latse and Burang) from
Contribution of the area
Change (km2) change (%)
the China Meteorological Administration; and digitalized
Himalayan mountain range extent results based on Bolch et al .
3.1 The distribution and changes of glacial lakes
Glacial lakes in the central Himalaya showed obvious expansion
in area and increase in number from 1990 through 2010. In total,
there were 1,314 glacial lakes (197.2260.004 km2) in 2010 within
the study area. The total area of glacial lakes expanded by
28.8160.012 km2 (17.11%) from 1990 to 2010. Larger glacial
lakes expanded more rapidly than the smaller ones. The total area
has increased by 10.7360.012 km2 for lakes with areas greater
than 1.0 km2 but less than or equal to 3.0 km2 and
9.0560.012 km2 for those greater than 3.0 km2 (Table 1).
In 1990, there were 350 glacial lakes with areas greater than
0.1 km2. The number increased by 42 from 1990 to 2010. There
were 31 glacial lakes larger than 1 km2 in 2010, of which 12 lakes
were newly formed after 1990. Glacial lakes larger than 1 km2
have expanded in area by 43% during the last 20 years.
The process of glacial lake change was very complex, consisting
of self-expansion, new formation and the disappearance of glacial
lakes. From 1990 to 2010, the number of newly formed glacial
lakes was greater than the number that disappeared. More than
80% of lake expansions consisted of the growth of existing glacial
lakes over three spans of time (Table 2). Thus, newly formed lakes
are not the biggest contribution to lake expansion in the central
3.2 Difference in changes in glacial lake types
Pro-glacial lakes in contact with glaciers have a higher
expansion rate than supra-glacial lakes. Pro-glacial lakes in contact
with glaciers increased in area by 23.87 km2, which accounted for
82.85% of the total glacial lake expansion from 1990 to 2010.
Proglacial lakes disconnected from glaciers have the lowest expansion
rates (Table 3).
3.3 Altitudinal difference in distribution and change of
A clear altitudinal distinction in the distributions and changes of
glacial lakes was identified at the north and south sides of the main
Himalayan range (Figure 3). All the glacial lakes were located
between 3500 m and 6100 m a.s.l. with a simple normal
distribution. The majority of glacial lakes were distributed in an
altitudinal zone from 4800 m to 5700 m at the north side and
from 4300 m to 5600 m at the south side, accounting for more
than 87% and 93% of all glacial lakes in quantity and area,
respectively. The elevations of the glacial lakes where the majority
of the expansion was occurring (more than 91%) are between
4800 m and 5600 m a.s.l. at the north side and between 4500 m
and 5600 m a.s.l. at the south side from 1990 to 2010. The
maximum frequency of occurrence of newly formed glacial lakes
was observed between 5100 m and 5200 m a.s.l. at the south side
and between 5500 m and 5600 m a.s.l. at the north side. The zone
in which the greatest increase in glacial lake area occurred was
between 5000 m and 5100 m a.s.l. at the south side.
3.4 Comparison of glacial lakes at the north and south sides of the main Himalayan range
The distribution and change of glacial lakes in the central
Himalayas is different between the north side and south side of the
mountain ridge. On the north side of the main central Himalayan
range, the increasing areas for glacial lakes of all types and for
rapidly expanding glacial lakes were greater than those at the
south side (Table 4). This trend was opposite to the expansion
trend in the Bhutan eastern Himalayas . This finding indicates
that a regional differentiation in glacial lake changes among the
entire Himalayas may exist. In this study, approximately 63% of
rapidly expanding glacial lakes was found at the northern side of
the main central Himalayan range (Figure 1).
Pro-glacial lakes in contact with glaciers
Pro-glacial lakes disconnected from glaciers
Increasing area (km2)
Figure 3. Distribution and change of glacial lakes at different altitudinal zones on the north (a and c) and south sides (b and d) of
the main central Himalayan range.
3.5 Rapidly expanding glacial lakes
We identified 67 rapidly expanding glacial lakes in the central
Himalayas, distributed into 9 river basins (Figure 1 and Table 5). The
top three rapidly expanding glacial lakes by quantity were in the Arun
(38.81%), Sun Kosi (22.39%) and Maquan He (11.94%) river basins.
Rapidly expanding glacial lakes in the Sun Kosi river basin had the
greatest expanding area (7.78 km2) from 1990 to 2010, implying a
higher potential risk for flooding that is cause for greater concern.
4.1 Typical glacial lake changes
Most glacial lakes will be safe for several years after a GLOF.
For example, the Dig Tsho glacial lake (Figure 4b) burst on 4
August 1985 , but it remained stable with almost no changes
from 1992 to 2009 after the GLOF. However, some formerly
outburst moraine-dammed lakes, for example, Lake Cirenmaco,
which breached on July 11, 1981, underwent remarkable
expansion between 1992 and 2009 (Figure 4a and Table 6).
Based on the criterion that we mentioned above, Cirenmaco was
classified as a rapidly expanding glacial lake, indicating a high
There are several other typical rapidly expanding glacial lakes
reported by previous studies in the central Himalayas that need
more attention in the future (Table 6). Galongco (Figure 4c), the
largest lake among these (4.83 km2) in 2009, has doubled in size in
the last two decades. The rates of expansion of the Galongco were
also greater than 100% during the periods 19772003 and 1986
All glacial lakes
Area in 1990 (%) Area in 2010 (%) Increasing area 19902010 (%) Count (%)
Increasing area 19902010 (%)
Rapidly expanding glacial lakes
Expanding area (km2)
2001. Imja Lake (Figure 4e) and Ludin Tsho (Figure 4f) expanded
by 1837% and 796%, respectively, from the 1960s to 2007 .
These notable expansion rates are affected by some uncertainty
due to inconsistent data sources (topographic maps and ALOS). A
consistent spatial resolution of the Landsat images used in this
study can eliminate the effects of inconsistent data sources on
glacial lake changes. Imja Lake and Ludin Tsho are still
considered of potentially high risk due to their high altitudinal
drops, although their areal expansion rate has decreased slightly
since 1992 (Table 6). Rapid growth (by approximately 110%) has
also been observed at Longbasaba Lake (Figure 4d) in the past two
decades, consistent with Yaos report in 2012 .
4.2 Causes of glacial lake changes
The increasing glacier melt water is the main supply source for
glacial lake expansion based on the dynamic processes of glacial
lakes, glaciers and climate. Pro-glacial lakes in contact with glaciers
are the greatest contributor (82.85%) of total glacial lake
expansion in the study area from 1990 to 2010. The expansion
of pro-glacial lakes in contact with glaciers is mainly driven by ice
or glacial melt water . From 1981 to 2010, the annual
precipitation decreased slightly according to the four
meteorological stations in the central Himalayas (Figure 5). However, the
annual mean temperature increased at a rate of 0.57uC?(10a)21.
An extreme warming trend that has developed in the central
Himalaya has resulted in significant glacier retreat [12,20,35,36].
Glacier runoff due to the accelerated glacial melting in the study
area has been increasing in the past few decades [22,37], which
was the main driving force for glacial lake expansion. The impact
of climate change on glacial lakes is rather complex and cannot
solely account for glacial lake change . The increasing
lightabsorbing constituents (e.g., black carbon and dust) of snow and
ice [11,38,39] may accelerate the melting in the central
Figure 4. GLOF events and changes in typical critical glacial lakes within the study area.
Area change (%)
GLOF on 4 Aug. 1985
Himalayan region, which is also a contributor to glacial lake
The distribution and change of glacial lakes across the entire
central Himalaya region for 1990, 2000, 2005 and 2010 were
investigated based on Landsat images. The same spatial resolution
(30 m) of Landsat images used in the four time points makes the
detected glacial lake change more reliable and reduces the
uncertainty resulting from using different sources of remote
Overall, glacial lakes in the central Himalayas distinctly
increased in area and number from 1990 to 2010. The larger
existing glacial lakes have contributed most to the areal expansion
in past decades. Most glacial lake changes occurred at the
altitudinal zone between 4800 m and 5600 m at the north side
and between 4500 m and 5600 m a.s.l. at the south side. The 67
glacial lakes that were selected as rapidly expanding glacial lakes
imply a higher potential risk of GLOF. Among them, the
Cirenmaco glacial lake has a potentially high risk to burst again
after GLOF. Glacial lakes expanded on the north side of the main
central Himalayas more than on the south side. The warming and
increasing light-absorbing constituents of snow and ice could have
accelerated the melting, which directly resulted in the glacial lake
This study gives a direct insight into what has happened to
glacial lakes in the central Himalayas over the past 20 years,
through remote sensing monitoring and integrated analysis. The
rapidly expanded glacial lakes were selected as potential lakes at
high risk for GLOF to monitor and observe continuously. To
address these critical rapidly expanding glacial lakes, potential
flood volume and drainage simulations for GLOFs should be
carried out in the future based on in-situ observations and field
measurements. Early warning systems and awareness of hazard
mitigation for GLOFs are now critically needed.
The authors give many thanks to the editors and anonymous reviewers for
their helpful comments.
Conceived and designed the experiments: YN QL SL. Performed the
experiments: YN QL. Analyzed the data: YN QL SL. Contributed
reagents/materials/analysis tools: YN QL SL. Wrote the paper: YN QL
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