CH25_Oct06_2011_AR_Andy by xiagong0815


									              25. Himalayan Glaciers (India, Bhutan, Nepal)

25.1 Introduction

         Snow and ice constitute a significant component of the hydrologic regime of the
Himalaya, in terms of their importance for regional water supply for irrigation,
hydropower generation and consumption. Since these patterns are influenced by climate-
induced temperature changes, there are concerns about the potential social and economic
impacts of glacier shrinkage in the last decade (Dyurgerov 2002, updated 2005; Barnett et
al. 2005; Barry 2006). Of particular concern is the impact of glacier changes on regional
water supplies (Barnett et al. 2005), their contribution to sea-level rise (Kaser et al. 2006)
and increased natural hazards such as outburst floods from moraine-dammed lakes (Mool
et al. 2002a; Mool et al. 2002b; Bajracharya et al. 2007; Bolch et al. 2008a). Glacier
melting contributes to the fast growth in glacial lakes, with some of these being converted
to moraine-dammed lakes (Hambrey 2008). These lakes have a tendency to breaching the
moraine dam (Watanabe et al. 1995), with a catastrophic impact on the downstream
valleys. Although several studies reported high rates of retreat of the Himalayan glaciers
in the last decades (Fujita et al. 2001; Kulkarni and Bahuguna 2002; Karma et al. 2003),
such trends are commonly based on a few non-representative sites, and they may not
capture the complexity of the glacier response to climate change in the Himalaya.
         Furthermore, the statement that Himalayan glaciers are melting at higher rates
than glaciers in other mountain ranges and may even disappear by 2035, as reported by
IPCC (2007) and WWF (2005), was found to be erroneous (Cogley et al. 2009).
Although alpine glaciers in the Himalaya are commonly thought to be particularly
sensitive to climate forcing, we lack a fundamental understanding of the magnitude of
feedbacks between climate forcing and glacier response in this region. Numerous
climatic, topographic and glaciological parameters govern glacier fluctuations, and these
are not well understood. The Himalaya in general lacks climatic information, field
observations of glacier parameters, and mass balance data due to terrain complexity,
logistic difficulties and geopolitics. Therefore, understanding glacier sensitivity to
climate forcing requires detailed information about climate dynamics, glacier distribution
and ice volumes, mass-balance gradients, regional mass-balance trend, and landscape
factors that control ablation.
         Mapping and assessing glaciers in the Himalaya is notoriously difficult due to
inherent sensor limitations and information extraction issues (Bishop et al. 2001; Kargel
et al. 2005). Conventional field based methods are difficult to use in the Himalaya due to
limited accessibility to the high altitude terrain and inclement weather conditions. In
some cases, the use of remote sensing for glaciologic applications is limited by spatio-
temporal spectral variations, cloud cover and saturation due to dynamic glacier surfaces
and sensor gain settings, respectively. This includes mapping small and debris-covered
glaciers and assessing termini fluctuations given temporal coverage limitations.
Furthermore, glacier elevation changes and ice-velocity determination are constrained by
systematic biases in digital elevation models, for example Shuttle Radar Topography
Mission (SRTM) elevation (Berthier et al. 2006). Nevertheless, new approaches to
information extraction have been developed, and remote sensing of glaciers in the

Himalaya is becoming increasingly useful for quantitative assessment of glacier
parameters. False-color composites made from visible and near-infrared satellite images
have been used successfully to map various glacial features such as glacier boundaries,
accumulation areas, ablation areas, equilibrium lines and moraine-dammed lakes. These
features can be mapped from satellite images using significant differences in the spectral
reflectance in glacial versus non-glacial features.
        The objective of this chapter is to present the current state in remote sensing of
glaciers in the India, Nepal, and Bhutan regions of the Himalaya. Specifically, we present
glacier changes in various climatic regimes of the Himalaya, ranging from the dry areas
of Ladakh and the monsoon transition zone of Lahaul-Spiti in the Western Himalaya
(India), to the monsoon-influenced region of the Central Himalaya (Garhwal), India and
the Eastern Himalaya (Nepal, Sikkim and Bhutan). The case studies presented in this
chapter illustrate the use of remote sensing and elevation data coupled with glacier
mapping techniques for change detection, glacier runoff and ice-flow modeling in the
context of the Himalaya.

25.2 Regional context

       25.2.1 Geologic, geographic and topographic setting

         Various terms are used to refer to the Himalayan ranges, sometimes in an
inconsistent manner. Some of the earlier studies (Mason 1954) split the Himalaya into
sub-divisions: Western Himalaya (Nanga Parbat, Lahaul-Spiti), Central Himalaya
(Garhwal, Himachal, etc.) and Eastern Himalaya (Sikkim and Bhutan), for example.
Mayevski et al (1979; 1980) distinguish between Himalaya (Everest-Kangchendzonga
region, Garhwal, Lahul-Spiti and Nanga Parbat) and Trans-Himalaya (Karakoram,
Batura-Mustagh, N.Karakoram and Khungerab-Ghujerab).. Ren et al (2007) refer to the
“Greater Himalaya” as the region spanning from 25 - 45 N, and 70 – 105 E, and they
include the Qinghai-Xizhang Plateau and the Tien Shan mountains. In this contribution
we define the Himalaya as the region extending in a SE-NW direction between ~27  - 32
 latitude and 77  - 92  longitude, south of the Tibetan plateau. We consider this
separately from the Hindu-Kush and the Karakoram ranges.
         Geologically, the Himalaya is a product of long-term plate tectonic collision
packed with suture zones, intrusive granitoid bodies, and stacked with thrust sheets and
nappe folds verging south. The Himalaya consists of a broad sweep of multiple ranges,
with no sharp topographic boundaries, and is generally considered separate from the
Pamir and Hindu Kush, which extend northwest to Pakistan. The Himalaya range is
wider on the west (~400 km), and narrower on the east (~50 km). Topography is more
rugged in the western part of the range as a result of scale-dependent erosion processes,
which produce complex relief and morphology patterns (Bishop et al. 2002). The
southern slopes of the Himalaya have steep relief, with large undulations and elevations
ranging from ~2,000 m in the foothills to the highest summit, Mt. Everest (8,848 m)

With more 7-8 km high peaks than elsewhere on the planet, the growth of mid-latitude
glaciers in the Himalaya has been large and pervasive. A wide variety of glacier sizes,
types, dynamics, topography, and debris-cover dictate unique spatio-temporal glacier
fluctuations. For example, many Himalayan glaciers are characterized by the presence of
debris-cover tongues due to rockfall debris from the steep sides (Singh 2000; Takeuchi et
al. 2000). The presence of thick debris on glacier tongues and accumulation from
avalanches often result in long glacier tongues. This creates some of the longest glaciers
in the world: Siachen (72 km), Bara Shigri (28 km), Gangotri (26 km), Zemu (26 km),
Milam (19 km) and Kedarnath (14.5 km) (Shen, 2004).

       25.2.2 Climate dynamics and glacier regimes

        Glacier regimes vary across the Himalaya depending on their location with
respect to two large-scale circulation patterns: the Asian monsoon and the Westerlies.
The Eastern and Central Himalayan glaciers (Nepal and Garhwal) are of "summer-
accumulation" type, with maximum accumulation and ablation occurring simultaneously
in the summer (Ageta and Higuchi 1984). Glaciers in the western regions (Lahul-Spiti
and the Ladakh) experience maximum precipitation in the winter, and are considered
“winter-accumulation-type” (Benn and Owen 1998). In general, accumulation on
Himalayan glaciers occurs mostly by snowfall, blowing snow and avalanches from steep
mountain slopes (Benn and Owen 1998).
        The climate of the Himalaya is dominated by the South Asian summer monsoon
circulation system, driven by the thermal contrast between the land and the tropical
oceans. Mid-troposphere heating over the Tibetan plateau during the summer causes the
inflow of moist air from the Bay of Bengal to the continent (Yanai et al. 1992; Benn and
Owen 1998). The orography of the Himalaya and Tibetan plateau (HTP) plays a
significant role in the onset of the monsoon. This elevated land mass acts as a barrier to
the monsoon winds, inducing maximum precipitation on the south slopes of the Himalaya
during the summer (June to September) (Gautam et al. 2009). Convection over the HTP
region during the monsoon season releases latent heat, thus sustaining the monsoon
(Barros and Lang 2003; Bhatt and Nakamura 2005). In the winter, the HTP has
mechanical effects on atmospheric circulation by preventing the southward flow of cold
continental air towards the Indian subcontinent (Benn and Owen 1998). The summer
monsoon produces large amounts of precipitation (300 – 400 cm/yr) on the south slope of
the Himalaya (Shrestha 2000), posing concerns for erosion and flooding in these areas
(Bookhagen and Burbank 2006). These patterns are different from rainfall patterns over
the rest of the Indian subcontinent (Shresta et al. 2000), as well as from other
tropical/sub-tropical regimes (Yanai et al. 1992). There is a E-W gradient in monsoon
intensity, with higher precipitation amounts in the Central Himalaya (Nepal and
Garhwal), and lower precipitation amounts in Lahaul-Spiti and Ladakh. Westerly winds
occasionally bring heavy snowstorms on the western ranges of Indian Himalaya, due to
moisture originating from the Mediterranean, Black and Caspian seas (Higuchi et al.
1992; Benn and Evans 1998).

        Recent studies report an increase in mean annual temperatures various parts of the
in the Himalaya in the last century. Analysis of climate station data show increasing
mean annual temperatures in the northwestern Himalaya (Bhutiyani et al. 2007;
Bhutiyani et al. 2010; Shekhar et al. 2010) and the Central/Eastern Himalaya (Shrestha et
al. 1999). Microwave satellite measurements of tropospheric temperature also indicate an
accelerated annual mean warming over the Himalayan region (0.21°C/decade ±
0.08°C/decade), with a maximum warming over the western Himalaya (0.26°C/decade ±
0.09°C/decade) (Gautam et al. 2010).

        The increase in temperature is accompanied by decreasing trends in monsoon
precipitation (Bhutiyani et al. 2010), and a reduction in total seasonal snowfall (Dimri
and Kumar 2008; Shekhar et al. 2010) for the same period.

         Three recent studies (Lau and Kim 2006; Gautam et al. 2009; Gautam et al. 2010)
reported an enhanced troposhperic pre-monsoon warming (April-May-June) over the
Hindu-Kush Himalayan region and the southern slopes of the Tibetan Plateau.
         The warming trends were associated with radiative heating from increased
absorbing (coarse) dust aerosols in the atmosphere over the Indo-Gangetic Plain (Gautam
et al. 2010)

       25.2.3 Previous glacier mapping and observations

         Long-term assessments of glacier change in the Himalaya have been limited
mostly to direct observations of glacier termini. These measurements have shown these
glaciers to be generally retreating during the last century (Yamada et al. 1992; Kulkarni
and Bahuguna 2002; Kulkarni et al. 2007; Kumar 2008; Bolch et al. 2008b; Bhambri et
al. 2010), some with alternating retreat and advance periods (Mayewski and Jeschke
1979). These increasing trends in temperature in the Central and Eastern Himalaya are
consistent with widespread glacier retreat reported in various studies (Fujita et al. 1997;
Kadota et al. 2000; Fujita et al. 2001; Bolch et al. 2008b; Bhambri et al. 2010). In
contrast, the Karakoram range to the west of the Himalaya has experienced a decreasing
trend in maximum and minimum temperatures of 1.6 and 3 deg C, respectively (Shekhar
et al. 2010), along with an increase in winter precipitation (Fowler and Archer 2006).
These trends may be consistent with lower rates of retreat of glaciers, or alternating
retreat and advance with mostly stable termini, reported from some Karakoram glaciers
(Schmidt and Nusser 2009). However, it is also important to note that the rapid
thickening observed in the last 2 decades is reported as an “anomaly” and associated with
topographic control on mass balance, mainly an elevation effect (Hewitt 2005). In
summary, it is still unclear to what extent the east-west trends in precipitation patterns
and pre-monsoon temperature trends influence glacier fluctuations, magnitude of ice
velocities, mass balance gradient, and regional mass balance in various areas of the

      One of the main challenges in glacier change detection in the Himalaya is the lack
of comprehensive, accurate baseline glacier inventories to use as a basis for comparison

with new glacier data from remote sensing. For Nepal, a glacier inventory has been
conducted on the basis of topographic maps from the 1970’s and 1980’s (Mool et al.
2002b). The eastern part of the Nepal Himalaya has been mapped more extensively on
the basis of field observations and aerial photographs (Higuchi 1980), or Schneider's 1:50
000 topographic maps (Muller 1970). A glacier inventory of Bhutan has been conducted
by Mool et al. (2002a); some glacier data have also been published by Karma et al.
(2003). For India, the earliest glacier maps are available from topographic surveys
conducted by expeditions in the mid-nineteenth century in India (Mason 1954). For a
detailed literature review of the glaciers surveyed by these expeditions, the reader is
directed to Bhambri and Bolch (2009a). The Geologic Survey of India (GSI) published an
updated glacier inventory for India based on Survey of India maps at 1:50,000 scale
(Sangewar and Shukla 1999); this is however not available in digital form. More recent
glacier mapping studies were conducted for a few areas in India (Himachal Pradesh, Uttar
Pradesh and Sikkim) based on Indian remote sensing, and are currently not in public
domain (Kulkarni and Buch 1991; Kulkarni 1992b; Bahuguna et al. 2001; Krishna 2005).
        Field-based glacier mass-balance studies are scarce in the Himalaya, limited to a
few glaciers, and most often data are not in public domain. Glaciers with short-term
mass-balance measurements include: Chhota Shigri Glacier in the Chandra Valley
(Lahaul and Spiti, Himachal Pradesh) (Dobhal et al. 1995; Wagnon et al. 2007); Naradu
glacier in Himachal Pradesh, Indian Himalaya, 1992 – 1996 (Kaul et al. 1997) and 2000 –
2003 (Koul and Ganjoo 2010); Dokriani Glacier in Bagirathi Valley (Garhwal Himalaya,
Uttaranchal), studied from 1992 until the present (Singh et al. 2000; Dobhal et al. 2004;
Dobhal et al. 2008), and Gara glacier (1974 – 1983), Gor-Garang (1977 – 1985) and
Shaune Gorang (1981 – 1990) in Himachal Pradesh, Indian Himalaya, Neh Nar (1978 –
1984) and Rulung glacier (1979 – 1981) in Jammu and Kashmir, Cangme Khangpu (1978
– 1987) in Sikkim, and Tipra Bank (1981 – 1988) and Dunagiri glaciers (1984 – 1992) in
Uttar Pradesh, mentioned in unpublished reports from the Survey of India (Ravishanker
1999). Glacier AX010 in the Shoring Himal in Nepal has been monitored from 1978 to
1979 (Yamada et al. 1992). The mass balance of the Yala and Langtang glaciers in Nepal
Himalaya was inferred from low-altitude temperature and precipitation records from
Kathmandu for the periods 1969 – 1997 (Tangborn 1999). Due to the scarcity of these
measurements, long-term mass-balance records and assessments of spatial variations of
glaciological parameters and change over the Himalaya are still missing from global
records (Dyurgerov and Meier 2000; Kaser et al. 2006). The response of alpine glaciers
across the entire Himalaya is not known with certainty, and this constitutes a significant
gap in our knowledge of climate-glacier dynamics.

25.3. Case studies and specific topics

25.3.1 Glacier variations in the Khumbu and Garhwal Himalaya
             Bolch, T.1, Bhambri, R.2, Bajracharya, S.3, Mool, P.3, Chaujar, R.K.4
         Institut für Kartographie, Technische Universität Dresden, 01069 Dresden, Germany
         Guru Nanak Khalsa College, Karnal-132001, Haryana, India
         International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal
         Wadia Institute of Himalayan Geology, Dehradun-248001, India

       Introduction and study areas

         We investigated glacier changes in two study areas in the Himalaya. The first
study area, the Garhwal Himalaya, is part of the Uttarakhand state of India along the
border of Tibet/China (Fig.1). Major sources of moisture in the area are monsoons in
summer and western disturbances in winter. Maximum snowfall occurs from December
to March (Dobhal et al. 2008). Our research area includes the upper Bhagirathi basin with
the Gangotri Glacier (~142 km²), the upper Alaknanda basin including Satopanth (~21
km2) and Bhagirathi Kharak Glacier (~31 km2), and Saraswati basin. Kamet (7756 m
a.s.l.) is the highest peak in the study region. The ablation zone of the larger valley
glaciers such as Gangotri is debris-covered to a great extent. The elevation of the
Gangotri glacier tongue is 3922 m while the ELA is situated at about 4875 m. Our second
study area is situated in the Khumbu and Imja valleys south of Mt. Everest in the Solu
Khumbu district of Nepal (Fig. 1). The climate there is influenced by the Indian
monsoon, and 70–80% of the precipitation occurs during the summer months. The annual
precipitation recorded near Khumbu Glacier in about 5000 m a.s.l. varies between 400
and 500 mm (Tartari 1998). The largest glaciers are Khumbu (length ~17 km), Nuptse
(~6 km), Lhotse ~7 km), and Lhotse Shar/Imja Glacier (~6 km), whose tongues are
heavily covered with supraglacial debris. The termini of the debris-covered glaciers
extend down to about 4900m, which is 300–400 m lower than the tongues of the glaciers
with clean ice. The end of the active debris-covered glacier tongues is not clearly
identifiable. Velocity measurements indicate that these tongues contain parts of stagnant
ice at the front (Bolch et al. 2008a; Quincey et al. 2009).
        Data and Methods
        Glacier area changes were investigated based on multi-temporal remote-sensing
data of various types and resolution: Corona KH-4 (1962), KH-4B (1972), Landsat TM
(1992), Ikonos (2000, 2001), ASTER (2001, 2003, and 2005) and Cartosat-1 (2007) for
the Everest area, as well as Corona (1968) Landsat TM (1990), ASTER (2006), and IRS
P6 LISS IV (2006), and Cartosat-1 (2006) for the Garhwal Himalaya. All the satellite
images were orthorectified using the generated ASTER DEM. Fieldwork was conducted
in both study areas for ground truth and GCP collection. For one master scene (2001
ASTER for Khumbu and the 2006 ASTER for Garhwal Himalaya), the band ratio
NIR/SWIR was used to map areas of clean ice on glaciers. Areas of shadow and the
debris-covered glacier tongues were improved manually and compared with the high
resolution data sets (Ikonos, Cartosat-1 and IRS P6) for evaluation. The glacier outlines
based on the panchromatic Corona images had to be fully adjusted manually, while image
ratioing helped to speed up glacier delineation with the multispectral images. Cast
Shadows hampered the correct identification of some glaciers in the Garhwal Himalaya.
We were able map 36 selected glaciers on the Landsat TM, and only 21 on the Corona
image. The estimation of glacier volume changes at Mt. Everest is based on digital
surface models from stereo Corona (years 1962, 1972), ASTER (average of the years
2001, 2002, and 2003), and Cartosat-1 (2007) images. The relative surface elevation
differences were used due to errors in absolute elevation of the DEMs. The estimated
error for the relative DEMs was 9.6 m (Bolch et al., 2008b). The DEM coverage and its
quality allowed calculating the volume loss of the debris-covered Khumbu, Nuptse,
Lhotse Nup and Lhotse glaciers to be calculated.

        Glacier variations in the Garhwal Himalaya
        Our results suggest that glacial area in Garhwal Himalaya decreased from 599.9
±10.2 km2 (1968) to 572.5 ±15.4 km² (2006). In total, 4.56% (~0.12% yr-1) of the glacier
area was lost from 1968 to 2006. The data from a sample of 29 selected glaciers from
1968 to 1990 reveals a loss of 3.48 km2 ± 0.1km2 (3.49%; 0.15% yr -1) in glacier area.
The area loss for the same subset of glaciers was 5.67 km2 ± 0.14 km2 (5.89%; 0.36% yr -
  ) from 1990 to 2006, which is about 50% higher than the 1968 – 1990 period (Bhambri
et al. 2010). The number of glaciers increased from 82 in 1968 to 88 in 2006 due to the
fragmentation of glaciers. Smaller glaciers (< 1 km²) lost 19.4% (~0.51% yr-1) of their ice
area. In contrast, glaciers > 50 km² lost 2.8% of their area in the same time period
(~0.07% yr-1). We found a significant increase in the debris-covered area of glaciers from
1968 to 2006 (14% area, 0.39% yr-1). There was no significant influence of median, mean
glacier elevation, aspect, and slope on the glacier shrinkage rate in the study area. Based
on high-resolution Corona and Cartosat-I images, we found that Gangotri Glacier lost
only 0.38 km2 (0.01 km² yr-1) from 1968 to 2006 at its terminus. These results are in
agreement with ground measurements reported by Geological Survey India: Srivastava
(2004) reported that Gangotri Glacier lost only 0.58 km2 (~0.01 km² yr-1) from 1935 to
1996 at its front area. In addition, the ice-covered area of Satopanth Glacier decreased by
0.314 km² (1.5%) near the snout from 1962 to 2006. Bhagirathi Kharak Glacier lost an
area of 0.129 km2 (0.4%) during a similar time period in Alaknanda-basin (Nainwal
         Glacier variations in the Khumbu Himalaya (Mt. Everest area)
         Previous studies of the Mt. Everest area showed that debris-free glaciers in Nepal
Himalaya receded after the middle of the 19th century, but parts of the debris-covered
glaciers remained as stagnant ice (Iwata 1976). Comparison of glacier outlines from 1960
and 1975 glacier inventories also indicated that most of the glaciers retreated (Higuchi et
al. 1980), with a likely accelerated retreat during the 1980s (Yamada et al. 1992). Our
investigation shows a decrease in the overall ice-covered area at Mt. Everest by 5.3 ± 2%
(~0.12% yr-1) between 1962 and 2005. The highest area loss occured from 1992 to 2001
(~0.25% yr-1), and the lowest from 1962 and 1992 (~0.09% yr-1). The area loss was
driven by shrinkage of the clean-ice area (0.24% a1); the debris-covered area increased by
0.06% a-1 (Table 1) (Bolch et al. 2008b). The identifiable snouts of the debris-covered
glaciers of the Mt. Everest region such as Khumbu, Lhotse or Nuptse Glacier were found
to be stable during the investigation period. This is in contrast to the debris-covered
glaciers in the Garhwal Himalaya, which experienced both downwasting and a snout
recession. Thinning rates range from 11m (Lhotse Glacier) to nearly 17m on average
(Khumbu Glacier) from 1962 to 2002. All glaciers showed similar behaviors (Bolch et al.
2008b, Fig. 2): a slight downwasting at the glacier snouts where the glaciers were
presumably stagnant. The downwasting was more pronounced in the middle part of the
glaciers, with an average of >20 m (0.5 m yr-1), and a maxima exceeding 50 m (1.25 m
yr-1), and less pronounced in the upper debris-covered, active part of the glacier. These
results are in agreement with the study by Kadota et al (2000).

        The formation of new glacial lakes poses a potential danger of an outburst
concomitant with the area loss of the glaciers (Bolch et al. 2008a). Past records showed
that at least one GLOF event occurs every 3 to 10 years in the Himalayan region

(Bajracharya et al. 2008). With rising temperatures and more variability in the climate,
the frequency of GLOF events is expected to increase in the coming years.

  25.3.2 Geomorphologic and surface reflectance change assessment of Mt Everest
                              (Sagarmatha area)

                     Jeff Kargel, Greg Leonard and Roberto Furfaro
                   University of Arizona, P.O. Box 210011 Tucson, Arizona 85721-0011

        Changes in glacier surfaces measured using satellite images are documented
either by DEM subtraction (Bolch et al. 2008b) or flow displacement (Bolch et al.
2008a). Bolch et al (2008b) derived glacier volume changes in the Khumbu region of
Nepal Himalaya by subtracting two DEMs: one produced from a 2005 ASTER scene, and
another produced from 1962 Corona imagery. The resulting elevation-difference map of
the area from Khumbu Glacier to Imja Glacier showed a thinning of the Khumbu glacier
by an average of about 40 cm/year. Multispectral image correlation techniques, radar
interferometric and speckle-tracking techniques were used to derive flow displacement
vectors occurring on glaciers over an interval of time in the same area of the Himalaya by
Bolch et al. (2008a). Multitemporal image subtraction is also useful to identify areas
where images are uncorrelated; hence, where surface changes have occurred, whether due
to lateral movement of terrain due to glacial flow, collapse, changes in ground cover, or
any other material or topographic change. When radiance is transformed to reflectance
values, the uncorrelated parts of reflectance images are good indicators of change,
particularly when the images are acquired under similar illumination conditions. The
transformation to reflectance, however, is always difficult when the requirement is to find
small differences in reflectance, i.e., computing a small number typically from two large
numbers. If illumination geometry or phase angle of observations differ substantially,
apparent differences in radiance commonly are not transformed accurately to differences
in reflectivity, because the photometric properties of surfaces caused by complex
microstructure can dominate the radiance differences. Generally, there is not enough
information to make a transformation with enough accuracy that differencing can work.
        Here we emphasize a much simpler and more reliable approach, which requires
observations to be made with the sun in basically the same spot in the sky and using the
same phase angle. This implies ideally using scenes obtained on the same date in
different years, and same nadir view. Excellent repeat ASTER image coverage of the
Mount Everest area has permitted selection of two very high-quality images (near zero
percent cloud and haze coverage, and low snow coverage) acquired almost exactly four
years apart (December 20, 2001 and December 15, 2005). The image pair differs by only
five days in acquisition time of year, and <4 minutes in time of day. Coupled with a
collection date very close to the winter solstice, this renders the illumination conditions
almost identical. Additionally, the imaging system gain settings and view angles of this
image pair also are the same, so that the images appear almost identical except in glacier
areas and associated lakes and snow fields, where many physical changes have occurred
and are readily discerned in radiance space, without any transformation to reflectance.
This circumstance has permitted an investigation of glacier changes simply by evaluating

the change image produced by subtraction of the 2001 radiance image from the 2005
radiance image (Fig. 3). For our image pair, however, almost all the variables are
controlled, except for the real surface changes we seek to image. Technical details of our
methodology, constraints, and limitations are given in Appendix 25.1.
         Real changes at the glacier surface are interpreted as such:
         (1) Glacier flow discerned from effects of aspect and slope changes: The pattern
of hill shading and illumination on the glaciers' surfaces have shifted due to ice flow that
has occurred between image capture dates; where the local surface of the glacier is either
uniformly debris covered, or is uniform bright ice or snow, but in either case is
undulating and flowing; the subtraction image shows most of these features as a mottled
gray-scale component that dominates many of the glaciers. The various mottled shades of
gray occur because the spectrum has not shifted much, but the pattern of photometric
shading and illumination associated with small undulations has been displaced as a
function of multi-temporal shifts in aspect and slope at pixel scale and greater.
         (2) Supraglacier pond and blue ice positional changes: Where small supraglacial
ponds (typically observed as areas of blue color) have shifted, blue is subtracted from
where the pond was in 2001 (hence, producing red areas); and blue has been added to the
pond's new location (causing a blue area to appear). Moving ponds, and also moving
patches of blue ice, appear as paired red and blue areas with the blue zones juxtaposed
down-glacier from the red. Assuming no change in pond size, the separation distance and
direction between the red and blue spots indicates the local flow displacement vector.
However, our analysis does not produce the vectors or flow velocities, but simply
highlights where the glaciers (and other surfaces) are very active, and where they are less
active or stagnant. Disappearance of ponds is represented by red areas, and new ponds are
blue areas, in each case being without a pairing of the other color.
         (3) Ogives and crevasse swarms: These features (which may have oscillations of
physical surface, with any combination of components of relief, ice microstructure, and
variable debris to ice ratio) tend to appear in the subtraction image as patterns of
alternating blue and red or contrasting gray scale.
         (4) Snow, ice, and rock exposures: Areas covered with snow in 2001, which were
exposed as blue ice in 2005 appear as very dark gray or dark blue patches. Black pixels
generally represent areas that changed from snow to rock. On the other hand, rock areas
in 2001 that later received snow from avalanches appear as white areas in the subtraction
image. Many large snow avalanches have occurred in between the two images, but the
biggest difference in the snowline is that it retreated sharply in 2005 compared to four
years earlier, as indicated by the black areas on portions of many glaciers.
         (5) Glacier- and moraine-dammed lakes: Lakes that were differentially ice
covered in each year, or where the water’s turbidity has shifted, appear as red, blue,
green, white, and black mosaics.
         In summary, the colors represented in the differenced image (Fig. 2) are indicative
of the type of change that occurred, and the overall texture of gray and colored mottlings
(speckle) indicates where glaciers and other surfaces have been most active. Where
glaciers appear as untextured neutral gray colors, there has been little change, and the
glacier would appear to be stagnant, or otherwise may have experienced only subpixel
displacement over the 4 years between image acquisitions. It can be seen that most of the
long valley glaciers are fairly stagnant near their termini, have broad lateral zones of

comparative inactivity, and central cores of more active ice. The general appearance is
indicative of basal sliding, a very large yield stress, or a very strong non-Newtonian
power-law material. Conversely, Imja Glacier appears active right up to the calving and
retreating margin along Imja Lake. This multispectral change image adds an indication of
which material surface units (snow, ice, debris, and water) are involved in the surface
change. Optimal use of this type of information would be a complement to other types of
change assessment, including flow speed and topographic changes. For reasons that are
evident in our description of methodology, limitations, and errors (Appendix 25.1),
routine application of this method of analysis would require an observation program
designed to image glaciers on or near anniversary dates. No such program is currently in
place, but this should be an objective for GLIMS for a next-generation observing system.

            25.3.3.Recent glacier changes in Ladakh, north-western India
                                Kamp, U.1, Byrne, M.1, Bolch, T.2
        Department of Geography, The University of Montana, Missoula, MT 59812, U.S.A.
        Institut für Kartographie, Technische Universität Dresden, 01062 Dresden, Germany

        Introduction and study areas
        Ladakh, including the two districts of Leh and Kargil, makes up more than half of
the Indian State of Jammu and Kashmir in the north-western part of India. The rugged
topography has an average elevation of over 3000 m, with its highest points at Nun
(7,135 m) and Kun (7,077 m) in the Greater Himalaya Range. The capital Leh (3,506 m)
receives a mean annual precipitation of only 93 mm (Archer and Fowler 2004). Hence, as
also observed in the field, the local population is dependent on snow and glacier melt
water. Hewitt (2005) found that in the northern Karakoram Range only one-third of snow
accumulation on glaciers occurs during the summer season with the Asian monsoon,
because the Greater Himalaya Range in the south of Ladakh blocks much of the monsoon
precipitation. As a result, aridity generally increases towards the north, and valley
glaciers are more numerous and larger in the southern Greater Himalaya and Zanskar
ranges. In the northern Ladakh Range, locally restricted cirque glaciers are common
rather than valley glaciers (Zgorzelski 2006). The equilibrium line altitude for glaciers
with a northwest to northeast orientation is between 5,200 and 5,400 m. (Burbank and
Fort 1985). We investigated glacier changes in three study areas in Ladakh. Site one,
located in the Ladakh Range, includes small glaciers, while the other two sites, located in
the Greater Himalaya Range, are characterized by the presence of larger valley glaciers
(Fig.1). Results of this study are reported by Byrne (2009) and Kamp et al. (submitted),
and the current contribution is a summary of their work.
        Data and methods
        We analyzed data from Landsat and ASTER sensors, covering the period from
1975 to 2006. Geomorphological field mapping and GPS observations of glacier termini
were conducted in the summers of 2007 and 2008. ASTER DEMs were generated
employing Silcast 1.07 software which does not require any GCPs. Cuartero et al. (2005)

presented for such Silcast-generated ASTER DEMs that had been produced without
GCPs an accuracy of ~ 6 m (RMSEZ) when compared to 40 individual checkpoints
(CPs), and Toutin (2008) described this accuracy as being sufficient in most DEM
analyses. Snow and ice covered areas were mapped automatically with NIR/SWIR ratios
of the Landsat/ASTER scenes from each available year, followed by visual image
interpretation. Manual editing was necessary to delineate the debris-covered glacier
sections, as automated methods were not accurate enough for multi-temporal mapping
(Kamp et al. submitted). The relative percentage of debris cover on glaciers tongues was
taken into account when quantifying glacier changes.
         The terminus of the easternmost glacier (area: 0.96 km2; length: 1955 m) at
Himis-Shukpachan in the Ladakh Range (Site 1) receded ~ 75 m (-3.8%) between 2000
and 2007, which is an average of 9 m (0.46%) of recession per year (Byrne 2009; Kamp
et al. submitted) (Table 2). In the 33-year span between 1975 and 2008, the ~ 23 km long
Drang Drung Glacier in the Greater Himalaya Range (Site 3) experienced a total
recession of 311 m (-1.3%, 9 m/a) (Fig. 4), and from 1990-2006 its debris-covered area
increased by more than 10%. In contrast to this general recession, Parkachik Glacier
(~ 13 km length; Site 2) at Nun Kun Massif (7135 m asl., 7077 m asl.) in the Greater
Himalaya Range fluctuated: a retreat of ~ 141 m (-1.1%; 13 m/a) from 1979-1990 was
followed by an advance of ~ 179 m (1.4%; 13 m/a) from 1990-2004 (Fig.4), and it then
retreated (-7 m) at a low rate (< 2 m/a) once again from 2004-2008. Usually, the debris-
covered area decreases in the ablation zone of a glacier that gains mass (Stokes et al.
2007; Bolch et al. 2008b). However, although it advanced from 1990-2004, Parkachik
Glacier’s debris-cover area increased by < 8% (0.6%/a).
       Discussion and conclusion
         The two studies summarized here concluded that most of the glaciers in Ladakh
have been receding since at least the 1970s. One exception to this trend was Parkachik
Glacier, which, after a period of retreat in the 1980s, experienced a rapid advance in the
1990s and early 2000s. This anomalously behavior might be due to the specific location
of Parkachik Glacier: (i) it reaches down from Nun Kun Massif to the Suru River over a
relatively short distance, i.e. it is steep and has an impressive ice fall, and (ii) Nun Kun
Massif might trap much of the precipitation from the westerlies during the winter, which
particularly feeds Parkachik Glacier on the northern slope. Also Hewitt (2005) reported a
number of glaciers in the Pakistani Karakoram to be anomalously advancing. These
studies suggested global warming as being responsible for the observed glacier recessions
and fluctuations in Ladakh and the Pakistani Karakoram, respectively. In the Greater
Himalaya Range of Ladakh in north-western India, most of the glaciers receded between
1975 and 2008 with maximum glacier length reduction of ~ 300 m, although some
individuals fluctuated due to specific local topographic circumstances. It is assumed that
this trend is a result of global warming.

 25.3.4. Recent glacier changes and velocities in the Brahmaputra river

                   Andreas Kääb1, Regula Frauenfelder1,2, Iris Sossna3
                          Department of Geosciences, University of Oslo)
                                 Norwegian Geotechnical Institute
                            Department of Geography, University of Jena

Introduction and study areas
Glaciers in the southern and central parts of the Himalaya are expected to be especially
sensitive to present atmospheric warming due to their “summer-accumulation” type
(Ageta and Higuchi 1984). This is likely to have substantial impacts on the hydrological
dynamics, resulting in a greater variability in precipitation and stream flows, increasing
intensity of extreme events comprising water quantity and water quality. The overall
objective of the BRAHMATWINN project was to enhance and improve capacity to carry
out an integrated water resources management for the Brahmaputra river basin. Our study
areas were: Northwest Brahmaputra (NW), Lhasa River (Nyainqentanglha range) and
Wang Chu basin/Bhutan (Fig.1).
Data sources
As part of this project, current glacier distribution and glacier changes since the 1960s
were investigated using multi-temporal optical remote sensing data from Landsat,
ASTER and CORONA. Repeat glacier outlines were combined with the DEM from the
Shuttle Radar Topography Mission (SRTM) and analyzed within a GIS in order to assess
changes in glacier area. New glacier outlines were derived from Landsat ETM+ data from
~ 2000. The Chinese Glacier Inventory (CGI) served as reference for the 2000 glacier
data. The CGI is based on 1980 airphotos for NW, and 1970 photos for Lhasa area.
Glacier area changes
Glaciers were mapped automatically using the band-ratio-approach presented in detail by
Paul et al. (2002) and Kääb et al. (2002). First, ratio images from a channel division
TM4/TM5 were computed. From these ratio images, binary glacier masks were created
by interactive thresholding. In the dry northern slopes of the Himalaya, the threshold
value for the ratio image was ~ 2.5. A 3x3 median filter was applied to reduce noise and
the resulting raster areas are finally converted into vector data. This vector data consisted
of large contiguous ice masses, which were divided into individual glaciers by
intersecting these data with a vector layer of glacier basins. The glacier basin layer was
digitized manually using the Landsat ETM+ as base data to discern the glacier basins and
the ice divides. Wherever possible, the CGI data were also used as reference. Visual
inspection of the glaciers and associated glacier basins was used as an additional
information source. The resulting vectors of individual glaciers were compared to the
CGI glacier outlines to find disagreements due to geo-referencing or digitizing errors in
the CGI. Misclassification typically occurs for wetlands, lakes and rivers erroneously
classified as ice, and missing classification on middle- and side-moraines. Errors due to
cast shadow (induced by the steep relief) and debris-covered glaciers were corrected
manually. For the Wang Chu basin, glacier outlines were obtained by manual digitization
of 1974 CORONA data and 2000 ASTER data. For this basin, manual digitization was
preferred over semi-automatic approaches due to the small number of glaciers and wide-
spread debris cover.

After the above post-processing procedures, 197 glaciers in the NW area and 476 glaciers
for Lhasa were finally used in the change analysis. Table 3 and Fig. 5 show glacier area
and volume changes for the study areas NW and Lhasa. Our study found a decadal
glacier area change of -8.9%/10 yr in the NW Brahmaputra, -7.1%/10 yr in the Lhasa
river area, and -6.6%/10yr in the Wang Chu basin in Bhutan. The differences in the rates
of retreat among the regions can partly be attributed to the variety of glacier size
distribution in the study areas, different climatic conditions (monsoon influences), the
degree of debris cover, and different glacier hypsographies. Our results are in good
agreement with area changes reported from other studies: In Pumqu (Tibet), mean glacier
area changes amounted to -8%/10yr for the period 1970-2001 (Jin et al. 2005). Karma et
al. (2003) found mean glacier area changes in the order of -9%/10yr for the period 1963-
1993 for Bhutan. We note a significant discrepancy between our results and another
recent study in the Lhasa river area (-6.1% vs ~ -20% from our study over a similar time
period; Bolch et al. 2010). The main reasons for this difference are assumed to lie in the
accuracy and correction of the CGI (Bolch et al. 2010).

Glacier volume changes
Since glacier volume cannot be measured directly from space, we used two different
empirical relations between glacier area and mean glacier thickness to estimate glacier
volumes: one developed by Maisch (1992) and one by Driedger and Kennard (1986).
Both relations are based on radar measurements of glacier thickness and the areas of the
glaciers investigated. The large discrepancy between both methods underlines that these
approaches for glacier volume estimation can only yield rough first-order estimates.
We estimated a volume loss of ~-20% between the 1970s and 2000, or roughly -0.3 - -0.4
m water equivalent per year as average over the study areas using both volume estimate
methods. Assuming that the glacier changes in these three study regions are
representative for the entire Brahmaputra basin, an upscaling of our area-change results
using the CGI and size-class specific area changes was done. These assumptions give an
area loss on the order of -13% per decade since the 1970s, or a volume loss of -7 km3
w.e. per year (0.02 mm/yr sea level equivalent) for the glaciers in the Brahmaputra
catchment. Our findings suggest that the glaciers in the three study regions in the upper
Brahmaputra river basin have lost roughly 20% of their water-equivalent ice reserves
during the last 20–30 years.
Glacier velocities
The surface velocity field of glaciers is an important glaciological parameter for glacier
inventorying and monitoring, and glacier hazard assessments. We investigated ice flow
velocities in the northernmost section of the Bhutan Himalayan main ridge, called
Lunana (~28 N, 90–91 E), separating the Tibet plateau to the north from the central
Himalaya to the south (Fig.1). The lowest terrain is 3700 m a.s.l., located south of the
main ridge. The northern sections towards the Tibetan plateau have minimum elevations
of around 5000 m, with the highest peaks around 7300 m. Most glaciers north of the
divide are oriented to the north, and most glaciers south of the divide to the south. The
glacier tongues we investigated are found at minimum elevations of 5000 m asl. to the
north, and 4000 m to the south of the main Bhutan watershed.

We derived horizontal displacements of individual glacier features from multitemporal
ASTER orthoimages using the Correlation Image Analysis Software CIAS (Kääb and
Vollmer 2000). A double cross-correlation function based on grey values of the images
was used to identify corresponding image blocks. Figs. 6 and 7 show the surface speeds
between 20 January 2001 and 20 November 2001 (Kääb et al. 2005).The glacier tongues
on the northern and southern slope differ in topography and surface characteristics as
well as dynamics. The northern glaciers originate from large ice plateaus of up to 7000
m, while the southern glaciers originate from steep ice and rock faces having comparably
small accumulation areas. These large head walls provide the sustained supply of rock-
debris that covers the southern glaciers, whereas such debris accumulation is not
significant for the northern glaciers. In addition to being debris-covered, the southbound
glacier tongues contain thermokarst features such as rapidly changing depressions and
supraglacial ponds. In their terminus zones, glacier speed is near the noise level of the
image matching techniques applied, i.e. in the range of 10– 20 m/yr. Higher speeds are
only reached in steep glacier parts above the tongues.
The northbound glacier tongues showed speeds of several tens to over 200 m/yr (Fig. 7).
These high speeds with steep transverse gradients at the margins imply large amounts of
basal sliding. Well preserved crevasses over the observation period also indicate minimal
basal drag. The northern glaciers have almost no debris cover. Their light-blue ice colour
in ASTER VNIR RGB false colour composites indicates a comparably high content of air
bubbles refracting the sunlight. The fast-flowing glacier tongues could well reflect the
balance velocity, i.e. the ice flux needed to drain the relatively large glacier accumulation
areas through the northbound valleys in order to keep the glaciers in geometric
Surface slopes of the southern and northern glacier tongues were on the order of a few
degrees. For similar slopes, the surface speed was clearly higher for the northern glaciers.
Thus, different surface slopes can largely be excluded as the reason for the large speed
differences observed. The speed differences point to different basal processes and higher
balance velocities. For the calving glacier tongues, the uniform and relatively high speed
might also be linked to an influence of lake water pressure reducing the basal drag. The
low speeds on the southern glacier tongues indicate little supply of ice to large sections of
the tongues. Such reduced ice flux supports the development of pronounced differential
melt, such as from the evolution of supraglacial ponds and other thermokarst features.
The low speeds enhance the accumulation of debris on the glacier surface through
reduced debris transport. In summary, the southern tongues appear to be nearly stagnant.
The expected present response to atmospheric warming for these glacier tongues is
downwasting—essentially decoupled from the dynamics of the upper glacier parts. Under
certain topographic circumstances, some southern glacier tongues might even loose
contact to the upper glacier parts and become dead ice. As a consequence, enhanced
development of glacial lakes on and at the southern glacier tongues, and increase of
related hazards has to be expected under conditions of continued atmospheric warming.
In contrast to the southern ones, the northern glacier tongues are fast-flowing. Most
likely, these glaciers will dynamically adjust to climate variations and thus respond by
retreat to atmospheric warming rather than by local decay. This retreat is enhanced by
calving processes where the glacier tongues terminate in lakes. The high ice velocities on

the northern glacier tongues are an efficient and thus important component of the ice
mass turnover within these glaciers. Therefore, variations of the long-term mass balance
of the northern glaciers will be reflected by changes in ice speed.

25.3.5 Glacier variations in Himachal Pradesh and Uttarakhand, Indian
                      A. V. Kulkarni1, I. M. Bahuguna2 and B. P. Rathore2
                     Space Application center (ISRO), Ahmedabad 380015, India
                   Divecha Centre for Climate ChangeIISc, Bangalore 560016, India

The oldest information about glacial extents in the Indian Himalaya is available from
Survey of India topographic maps at 1:50000 scale, constructed from vertical air
photographs and limited field investigations from 1962. Spectral reflectance values
derived from the field and satellite images for the accumulation area show high
reflectance in bands 2, 3 and 4 in IRS LISS-II and LISS-IV sensors. On the other hand,
the reflectance values for the ablation areas in band 2 and 3 are higher than the
surrounding terrain, but lower than vegetation in band 4. Therefore, these spectral
characteristics are useful to differentiate between glacial and non-glacial features. If
glaciers are covered by debris, then identification and mapping of glacial terminus are
normally difficult, and require the use of geomorphologic features help identify terminus.
Moraine-dammed lakes tend to form downstream of the glacier terminus (Fig. 8), and can
be easily identified on satellite images. Depending upon illumination geometry, these
walls can form shadows in downstream direction, which can be used as markers for
termini delineation (Bahuguna et al. 2007).
In this study, mapping of glacial extents in 2001/02/04 was carried out using LISS-III or
LISS-IV images. We selected images from July to September, when snow cover is at its
minimum and the glacier area is fully exposed. Glacier boundaries digitized manually in
a GIS from Indian topographic maps were used as validation. Remote sensing glacier
outlines were derived using standard bands combination of 2,3,4 and 2,4,5 bands from
LISS images and visual interpretation techniques. Image enhancement techniques were
used to enhance the difference between glacial and non-glacial area. Field investigations
were carried out at Shanue Garang glacier (Baspa basin), Parbati glacier (Parbati basin),
and Chhota Shigri, Samudra Tapu and Patsio glaciers (Chenab basin) for validation of the
snout position using a GPS. The relative position of the terminus was compared with
geomorphologic features such as moraines, origin of stream from snout and moraine-
dammed lakes. Since the GPS instrument cannot be easily mounted on terminus, due to
safety considerations, the relative position of terminus was estimated using
geomorphological features as moraines, water bodies and seasonal streams. Glacier
length changes were measured along the centerline on satellite imagery using GIS.
Results yielded a total of 1317 glaciers with a total area of 5866 km2 in 1962. The area
decreased to 4921 km2 in 2001/04, which is an overall area loss of 16% for this time
period. Basin-wise values of glacier area loss are given in Table 5. One example of

glacier change is shown in Fig 8, for the debris-covered tongues of Samudratapu glacier.
The amount of glacier retreat varies from glacier to glacier and from basin to basin,
depending on parameters such as maximum thickness, mass balance and rate of melting
at terminus (Kulkarni et al. 2005). In addition, loss in glaciated area depends on glacier
area (Kulkarni et al. 2007), possibly because glacier response time is directly proportional
to thickness (Johannesson et al. 1989), and thickness is directly proportional to its areal
extent (Chaohai and Sharma 1988). Glacier response time is known as the amount of time
take by glacier to adjust to a change in its mass balance. In the Himalaya, for small
glaciers (> 1 km2), if not heavily covered by debris, the rate of melting at the snout is
around 6 m a-1, and the response time can be estimated between 4 and 11 years (Kulkarni
et al. 2005). If other parameters are constant, small glaciers are expected to react faster to
climate changes. This has been observed in our study area, where glaciers smaller than 1
km2 have lost almost 38 % of their area between 1962 and 2001/04. On the other hand,
larger glaciers with areal extent higher than 10 sq km have lost only 12 % in area during
the same period. At the same time, the numbers of glaciers increased due to the
disintegration of ice (Fig.9)
Future steps for this analysis include improving the termini position assessment with the
use of a laser range finder and GPS, where the distance between fixed point on stable
land and glacial terminus will be estimated. This will provide a much-needed validation
of the remote sensing based methodology.

25.3.6 Remote sensing estimates of glacier mass balance in the Himachal
                  Pradesh (India) during 1999-2004

                                      Etienne Berthier1 and Yves Arnaud2
                         CNRS; LEGOS; 14 Avenue Ed. Belin, F-31400 Toulouse, France
                    IRD/ LTHE, LGGE 54 Rue Molière, BP 96, 38402 St Martin d’Hères, France

Introduction and study area
Mass balance of Himalayan glaciers is poorly sampled in the field, and only a few sparse
estimates based on remote sensing techniques exist in this area. To contribute to filling
this gap, we conducted the first space based measurement of glacier elevation changes to
monitor glacier mass balances in the Lahaul-Spiti region of the Indian Himalaya (Figs.1
and Fig 10).

The annual mass balance of Chhota Shigri glacier of (16.5 km2) has been measured in the
field since 2002 using a network of stakes and pits located between 4300 and 5500 m
a.s.l. Surveys were performed each year at the end of the ablation season (late
September/early October) (Wagnon et al. 2007).

The elevation changes were obtained by comparing a DEM derived from 20004 SPOT
imagery with the 2000 SRTM DEM following a procedure developed and validated in
the French Alps (Berthier et al. 2004). The 2004 DEM was derived from two SPOT5

satellite optical images without any ground control points thanks to the good onboard
geolocation of SPOT5 scenes. SRTM elevations were used as reference on the ice-free
zones. Glacier outlines were manually digitized from a cloud free ASTER image
acquired on 28 September 2002 and were made available to the GLIMS database (http:// Before comparison on glacier surface, the two DEMs were evaluated on
the non-glaciated areas surrounding the glaciers where no elevation change is expected.
We found that a long wavelength bias affected the SPOT5 DEM, and was correlated to an
anomaly in the roll of the SPOT5 satellite. A bias was observed as a function of altitude
and was attributed to the SRTM dataset. Both biases were modelled and removed by
adjusting precisely the SRTM and the SPOT5 DEMs to permit unbiased comparison of
the two DEM on the 915 km2 ice-covered area.

A clear thinning of 4 to 7m was observed on most glaciers between 4400 and 5000m,
including the debris-covered tongues (Fig 11). Elevation changes were small in the upper
reaches of the glaciers (a slight thinning of about 2 m). Elevation changes were converted
to volume changes using the hypsometry of each glacier. The conversion of volume
changes to mass balance requires the knowledge of the density of the material loss or
gain. This is straightforward in the ablation zone where ice (density 900 kg/m2) is
involved, but more problematic in the snow-covered accumulation zone, where density
varies from snow to ice. Here, we computed the mass balances using two hypotheses
concerning the density of the material lost in the accumulation zone (ice or snow). We
assumed an ELA of 5100 m as determined in the field for the Chhota Shigri during the
hydrological years 2002-2003 and 2003-2004. We obtained an average annual mass
balance of 0.7 to 0.85 m/a (water equivalent) for the period 1999 and 2004, depending on
the density values we use for the material lost (or gained) in the accumulation zone. The
uncertainties associated with these elevation changes and mass balance are difficult to
quantify given the lack of simultaneous ground measurements. However, a promising
agreement is found between the remote sensing estimates (1 to 1.1 m/a w.e. during 1999-
2004) and the field estimates (1.13 m/a w.e. during 2002-2004) for the mass balance of
Chhota Shigri glacier.

We have demonstrated here the use of space-borne DEM to derive relatively accurate
estimates of the regional mass balances in a remote area of Northern India (Berthier et al.
2007). These measurements are crucial to better monitor the ice loss of Himalayan
glaciers and estimate their contribution to water resources. This remote sensing
methodology will be even more valuable in the near future with the availability of new
satellite DEMs. Using DEMs which will be further apart in time from SRTM DEM
increases, there will be a clearer signal to noise ratio, and the average mass balances will
most likely be easier to quantify.

    25.3. 7 Glacier changes in the Sikkim Himalaya in the last four decades from
                                  Landsat and ASTER data

                                     Adina Racoviteanu
               Department of Geography and Institute of Arctic and Alpine Research
                           University of Colorado, Boulder CO 80309

    Introduction and study area
    This study focuses on glaciers in the Sikkim Himalaya (27° 04’ 52” N to 28° 08’ 26”
N latitude and 88° 00’ 57” E to 88° 55’ 50” E longitude), located in Eastern India
between Nepal and Bhutan (Fig.1). Relief ranges from 300m in the eastern parts to
8598m (Kangchendzonga), with steep and rugged topography. Based on an inventory
constructed from 1970 topographic maps, published in 1999 by the Geological Survey of
India, there were 449 glaciers covering an area of 705.54 km2. Subsequent estimates of
glacier coverage have inconsistencies due to various ways of processing of the remote
sensing data. The glacierized area was estimated from Indian IRS-1A and Landsat data to
be 431 km2 in 1987/88 (Kulkarni 1992b). Mool et al (2002a) reported 285 glaciers
covering an area of 576.433 km2 on the basis of 2000 Landsat imagery.

    For the baseline glacier inventory, I used the 1:150 000 Swiss topographic map,
compiled from Survey of India maps and other topographic surveys, published in 1981 by
the Swiss Foundation for Alpine Research. The whole state is covered by 20 topographic
maps from the Survey of India, from 1950’s to 1970’s. While most of the maps are from
1962, the exact date of each quadrant is not available. The original Survey of India maps
are restricted in the 80 km wide area from the borders to coastline, including Jammu and
Kashmir, northern and eastern districts of Himachal Pradesh, northern district of
Uttarkand and Sikkim, as well as Bhutan (Srikantia 2000; Survey of India 2005).
Consequently, the exact date of the ice masses on the Swiss topographic map is not
known, which introduces uncertainty in the area change estimations. Here, I consider the
reference date as ~1960s, and the error terms are calculated by considering a time step of
one decade for the potential data source (1950’s or 1970’s). Ice masses from this
topographic map were digitized manually in a GIS and were split into individual glaciers
using topographic information from the SRTM DEM version 4 (CGIAR), and the
protocol developed by Manley (2008).
    The new remote sensing glacier inventory was constructed based on one Landsat
scene from Nov 2000 and six orthorectified ASTER scenes from 2000 – 2005. The date
of this inventory is referred to as ~2000’s. I used automatic delineation of clean glacier
ice, which relies on the spectral uniqueness of glacier ice in the visible and near-IR part
of the electromagnetic spectrum. I applied the normalized snow difference index (NDSI)
(Hall et al. 1995), which takes advantage of the differences in spectral properties of
glacier ice to distinguish it from other surfaces. The resulting image was segmented using
a carefully selected threshold (NDSI > 0.7) to obtain a binary map of glacier – non-
glacier areas. A 3x3 median filter was applied to remove noise and create the final map of
clean ice. Debris covered area was delineated using a combination of semi-automatic
algorithms and manual digitization. The 2001 ASTER scene from Nov 27, 2001 and the
ASTER DEM derived from it were used in a decision tree classifier in ENVI, which
combines surface reflectance, topography and kinetic surface temperature constructed
from ASTER thermal bands (AST08 product). The decision tree eliminates areas that are

not “suitable” for debris cover based on preliminary knowledge (absence of clean ice,
slope < 12 degrees, absence of vegetation and clouds). Vegetation was delineated using
the Normalized Difference Vegetation Index (NDVI >0.05). Clouds were eliminated
using the near-infrared band 3 of ASTER (0.760 to 0.860m). At these wavelengths,
clouds are highly reflective and snow and ice are absorbant (band 3 > 90). The thermal
information derived from ASTER thermal bands was particularly useful for narrowing
the range of potential debris in the last stage of the decision tree. A transect across Zemu
glacier in the kinetic temperature band (AST08) showed the debris cover to be colder
than the illuminated moraine on the north side of the glacier, and warmer than the
shadowed moraine on the south side of the glacier, with a narrow temperature range of
272 – 283 degrees K. Surface temperature increases from the upper part of the debris
cover, which is in contact with the clean ice to glacier terminus, indicating that the thick
debris cover at the terminus is insulating the ice underneath. Field observations from
other glaciers in the Himalaya yielded debris thicknesses of as much as 2 m at the glacier
toes (Kayastha et al. 2000). Debris thicker than a few centimeters (“critical thickness”)
insulates the ice underneath (Nakawo et al. 1993; Nakawo and Rana 1999). Thresholding
the temperature band with the above range helped to map the entire surface of the debris-
cover glaciers, but also mis-classified other areas outside the glaciers, which had a similar
temperature range. These areas were corrected using manual digitization. The final map
of potential debris-cover is shown in Fig. 12. A few areas displayed digitizing errors:
turbid/frozen lakes were classified as snow/ice because their bulk optical properties are
very similar in the visible and near-infrared wavelengths (Dozier 1989a; Dozier 1989b);
glaciers underneath low clouds could not be classified; and transient snow outside
glaciers and on the glacier surface made it impossible to distinguish between snow and
ice; debris-covered ice was not distinguished by the NDSI algorithm.

    Glacier area changes
    The 1960’s glacier inventory based on the topographic map yielded 158 glaciers with
a total area of 742.03 km2. The 2000 glacier inventory yielded 188 glaciers with an area
of 540.72 km2. This amounts to an area loss of 201.31 km2 between 1960s and 2000, or
27.12 % (-0.68%  0.2% yr-1) (Fig.13), considering the uncertainty in the data source for
the baseline topographic date ( 10 years). The increased number of glaciers (30 glaciers)
from 1960’s to 2000’s indicates the ice disintegration, reported in other parts of the
Himalaya (Kulkarni et al. 2007; Bhambri et al. 2010). Overall, there were 15 debris-
covered glacier tongues with an area of 64 km2 in 2000, which represents 11.9% of the
entire glacierized area. In 2000, the glacier size ranged from 0.05 – 105 km2, with an
average size of 4 km2. The average termini elevation increased from 4604 m in 1962 to
4702 m in 2000. This represents a retreat of the glacier tongues up the valley of +98m on
average. The average median elevation of the glaciers (considered here as a rough
estimate of the equilibrium line altitude -ELA) (Benn and Lehmkuhl 2000)increased from
4301m in 1955 to 4549 in 2000, or +107 m. The changes in glacier area are comparable
to rates of retreat reported in the western parts of the Himalaya, including this chapter (-
0.7% per year) (Kulkarni et al. 2007).

25.4 Summary and outlook

This chapter presented the current understanding on Himalayan glacier changes in the last
few decades, using remote sensing and topographic maps. Specifically, the contributions
in this chapter focused on glacier area and thickness changes, velocity changes, and the
contribution of glaciers to streamflow. Most contributions relied on semi-automatic
methods for glacier delineation, and encountered topographic shadowing effects, and
difficulties with water bodies, which had to be corrected manually. Applying standard
image processing and terrain analysis techniques poses challenges in the Himalaya due
to the complexity of terrain and lack of field measurements for validation. Conclusions
with respect to remote sensing algorithms include:
     Combining multi-temporal remote sensing data of various types and spatial
         resolution (ASTER, Landsat TM, Corona, Cartosat and LISS) is useful for
         construction new glacier inventories in the Himalaya, where one sensor only
         might be limited by cloud cover or gain settings;
     Band ratio was the most common method used for delineating glaciers over large
         areas of the Himalaya, with some manual adjustment for clouds, water bodies and
         debris covered glaciers
     Restrictions on old Indian topographic maps poses a challenge in deriving
         estimates of glacier changes based on remote sensing imagery - some of the
         contributions used Corona imagery as a baseline dataset
     The SRTM DEM was useful in estimating glacier thickness changes, and infer
         mass balance of Himalayan glaciers
     Uncertainties in glacier change estimates are hampered by the lack of information
         about old topographic maps, when used.
Conclusions with respect to spatio-temporal glacier changes over the Himalaya in the last
decades, based on contributions in this chapter, include:
     Glacier area changes range from 0.12% per year in Garhwal Himalaya and
         Ladakh Himalaya to ~0.4% per year in Himachal Pradesh and 0.68% per year in
         Sikkim (Eastern Himalaya) in the last three decades, approximately.
     Smaller glaciers (<1km2) lost more area than larger glacier in the same time
         period, while long valley glaciers covered with debris appear to be generally
     An increase in the debris covered area of glaciers was noted in Garhwal, Khumbu
         and Ladakh Himalaya
     Glacier area changes were accentuated in the 1990 – 2000 decade in comparison
         with previous decades in Garhwal (threefold increase) and Ladakh (twofold
     Glacier volume loss are on the order of 20% in the last three decades in
         Brahmaputra basin
Future work in the Himalaya needs to focus on better quantifying the uncertainties
involved in glacier change estimates, extending glacier volume and mass balance
estimations to larger scales, and understanding the effect of larger circulation patterns on
glacier mass balance.


Table 1: Changes of total ice cover, the clean-ice and debris-covered ice areas in the Khumbu
Himalaya between 1962 and 2005 based on spaceborne imagery. Source: (Bolch et al. 2008b).
               Ice extent [sq.kmkm2]                 Change Rate (sqkm2/yr)         Change Rate (%/yr)
            Total    Clean    Debris    Period     Total    Clean       Debris   Total    Clean     Debris
            Area     Ice      Cover                Area     Ice         Cover    Area     Ice       Cover
1962        92.26    56.54    35.73     1962-      -0.11    -0.13       +0.02    -0.12    -0.24     +0.06
(Corona)                                2005
1992        89.69    54.63    35.05     1962-      -0.086   -0.064      -0.023   -0.09    -0.11     -0.06
(Landsat                                1992
2001        87.64    51.59    36.05     1992-      -0.228   -0.338      +0.111   -0.25    -0.62     +0.32
(ASTER)                                 2001
2005        87.39    50.8     36.60     2001-      -0.11    -0.13       +0.02    -0.12    -0.24     +0.06
(ASTER)                                 2005

Table 2: Changes in length and debris-covered area for three glaciers in Ladakh.

Glacier Name                  Length in     Observation        Change in         Change in         Change of
                             2007 or 2008     Period            Length            Length             Debris-
                                (km)                             (m)               (%)            covered Area

No name                           2             2000-2007            −75           −3.8                 N/A
(Himis-Shukpachan Area;
Ladakh Range)

Drang Drung                       23            1975-2008            −311          −1.3
(Greater Himalaya Range)                        1990-2004                                               +10

Parkachik                         13            1979-1990            −141          −1.1
(Nun Kun Massif;                                1990-2004            +179                               +8
Greater Himalaya Range)                         2004-2008             −7

Table 3. Upper table: Glacier area/volume changes in the north-west Brahmaputra basin
       (Area 1) and the Lhasa river basin (Area 2). Reference glacier outlines are from
       the Chinese glacier incventory, in Area 1 based on airphotos from 1970s, in Area
       2 from 1980s. Glacier outlines from 2000 are from Landsat data. Lower table:
       Estimated glacier volumes and volume changes in Areas 1 and 2. The volume
       estimates are based on two empirical relations between glacier area and mean
       glacier depth (1, Maisch 1992; 2, Driedger and Kennard 1986).

Table 4. Glacier retreat in Himachal Pradesh from 1962 – 2001/2004
                        No.        Area 1962 (Km2.)    Area 2001/2004         Loss in area
    Basin           of glaciers                           (Km2.)          1962 - 2001/2004 (%)
   Chandra              116                 696              554                   20
    Bhaga               111                 363              254                   30
   Parbati               90                 493              390                   20
   Basapa                19                 173              140                   19
   Warwan               253                 847              672                   21
     Bhut               189                 469              420                   10
    Miyar               166                 568              523                   08
  Alaknanda             126                 734              638                   13
  Bhagirathi            187                1218             1074                   11
  Gauriganga             60                 305              256                   16
    Total              1317                5866             4921                   16

Figure captions

Fig. 1 Study area (list sites and locations)
Fig. 2 Glacier volume loss 1972-2007 at Mt. Everest calculated based on a Corona KH-
4B and a Cartosat-1 DEM.
Fig 3 (to be updated) Image subtraction ASTER 2001 – 2005 Jeff, update here

Fig. 4: Glacier change results based on semi-automated mapping approaches: (a)
Parkachik Glacier (SA-3) (b) Drang Drung Glacier (SA-4) (c) Glacier 4 (SA-5); (d)
Glacier 10 (SA-5).

Fig 5 Glacier changes in the Lhasa river basin between 1970 and 2000. Blue areas:
glaciers as mapped from Landsat data of 2000; red areas: glacier areas lost since 1970

(Chinese glacier inventory); light and dark violet areas: probable and likely permafrost
distribution as modelled following an approach by Keller (1992), adjusted using regional
meteorological data. The hillshade in the background is based on the SRTM DEM.
Stripes in the hillshade stem from nearest-neighbour interpolation during re-projection.
Size of section shown: 30 x 25 km. North to the top.

Fig 6 Selected, representative glacier speeds per year (m a-1, red numbers) over the
main ridge of the Himalaya in Bhutan. Velocities are derived from repeat ASTER data of
2001. Northbound glaciers flow significantly faster than the debris-covered southbound
ones. For a detail of the upper right glacier, see Fig. 7. Background image: ASTER 321
RGB composite with R converted to green to simulate true colors.

Fig 7 Velocity field (vectors) and isolines of speed of a glacier in Bhutan (upper right
glacier in Fig. 6) as derived from repeat ASTER data of 2001. North to the right.
Backround image: ASTER 3N orthoimage with SRTM contour lines overlain.

Fig. 8: Retreat of Samudra Tapu glacier, Himachal Pradesh between 1962 and 2006.

Fig. 9: Fragmentation of glacier in the Parbati river basin.

Fig. 10: (a) 12 November 2004 SPOT5 image for the Chotta Shigri area, Lahaul-Spiti,
with the main rivers. The arrows indicate Chhota Shigri and Bara Shigri glaciers and the
white rectangle locates the Chhota Shigri glacier shown in (b). Note the shadows due to
the steep valley walls and the low solar illumination in late autumn.

Fig 11: Map of glacier elevation changes (in meters) between February 2000 and
November 2004 for the glaciers in the Lahaul/ Spiti region.

Fig 12 Results of the debris-covered mapping algorithm for the Zemu glacier area. Pixels
classified as debris are shown in red; clean glacier outlines are shown in black

Fig 13 Glacier area changes 1955 - 2000 in the Zemu area of Sikkim Himalaya. The two
sets of outlines are shown on a color composite using ASTER 432 bands.

Fig. 14. A topographic map of the Langtang valley. The thick solid lines indicate the
boundaries of Langtang. S1 and BH represent hydrological observation sites in
Langtang Khola Basin and Base House for meteorological observations, respectively.

Fig. 15. Variations in observed annual mean air temperature, total precipitation and
calculated discharges in Langtang Khola Basins from July 1985 to June 1986 and
from 1988 to 2007. The values plotted in 1985 represent from July 1985 to June
1986. Solid lines are trend lines of discharge and air temperature variations.



Fig. 2.



Fig 5

Fig. 6

Fig 7

Fig 8

Fig 9

Fig 10

Fig 11

Fig 12


Fig 14

                         2500                                             15
                         2000      Ppt.
   Discharge/Ppt. (mm)

                                                                               Temp. (o C)




                           0                                               0
                            1984   1988    1992   1996   2000   2004   2008

Fig 15

APPENDIX 25.1. Image Differencing: Methodology, Limitations, and Errors

The ASTER images used in this analysis include identically subset portions of
AST14DMO_00312202001050229 and AST14DMO_00312152005045832, collected on
December 20, 2001 and December 15, 2005, respectively. Preprocessing for these on-
demand products includes radiometric calibration and geometric co-registration of intra-
sensor image bands; output includes at-sensor radiance given in W/ m2 µm sr (EOS Data
handbook, 2003). Subsequent to this, for each image, VNIR bands 3, 2, and 1 were
stacked into an image cube and rendered into RGB color space at full ASTER VNIR
spatial resolution (15m/pixel). The image pair was co-registered using several mutual
ground control points selected from the images; sub-pixel accuracy for the relative co-
registration between the image pair was achieved (RMSE = 4.4m). The images were
subsequently differenced, the DN values of the later image minus the earlier and the
residual 8-bit image was rescaled in DN so that the minimum DN difference is 0 and
maximum DN difference is 255 (e.g., rescaled minimum DN values represent terrain that
had the maximum change from bright in 2001 to dark in 2005; and rescaled DN = 255
represents maximum shift from dark in 2001 to bright in 2005). Unchanged terrain,
whether it is snow, rock, water, or ice, appears as neutral gray in the subtraction image
due to this DN rescaling. The results of image differencing (Figure 3) include many types
of changes, most of them real changes to the surfaces, but including these types of
        (1) Shadow shift: The 5 day difference in time of year means that the vector to the
sun is not exactly the same, such that shadows have moved slightly, with some areas
emerging into sunlight and others moving into shadow. As a first-order assessment of
change in shadow position, we calculate that the solar zenith angle increases over a 5-day
period from December 15 to December 20 by 0.36 degrees, and the solar azimuth shifts
by 0.56 degrees (with image time of day approximated as an identical 10:30 AM local
time); for shadows of local topographic relief of 3000 meters, the shadow of the
mountain peak would increase in length from 5229 m to 5276 m (increase of 47 m), and
azimuthal position of the shadow peaks shifts 51 m, for a total shift in location of 69 m
(root of the sum of the squares of two orthogonal components of the shadow shift), or
roughly 4.6 ASTER VNIR pixels. Shadow shifts in this image pair become sub-pixel for
local relief <650 m. Thus, the high-frequency relief on the glacier, where local relief is
of order 100 m and less, does not have significant shadow-shift associated with changing
illumination geometry, and changes shown on the glacier, in this example, are generally
due to actual changes of the glacier. The contribution of this artifact in glacier areas is
negligible and less than the image registration error.
        (2) Shadow suppression: Another shadow-related artifact is more an artifact of
suppression of real change indicators, within shadow areas, where neutral gray tones
(suggesting little change) predominate in the change image, even where real changes may
have occurred. The problem is that the shadow causes low DN values, and subtraction of
one small number from another small number (even if there have been intrinsic material
changes) results in third small number, which then gets rescaled to a near-neutral gray;
hence, the method is not well suited to shadowed areas, though some changes can be
discerned in those areas.
        (3) Solar illumination angle: There occurs a slight differential illumination due to
difference in imaging date or any variation in optical haze. This causes general

background terrain of stream valleys, small hills and other terrain to show up faintly in
the subtraction image because the solar incidence angle and cos(i) factor in reflectance
was not exactly the same. Sunward facing slopes see very little impact, but anti-sunward
slopes can experience a substantial change in cos(i) even when the solar angle changes by
a couple degrees.
        (4) Image pair mis-registration: Any slight misregistration also would yield a
slight ghost of the original images, but as stated above, image misregistration is only
about 0.3 pixels. It is evident that most of the high-contrast and colored features in the
change image are present near the mountain summits, and in glaciers and lakes, where
real changes have occurred. Areas lacking glaciers, snow, and lakes, even in areas with
rugged terrain, including sharp mountain crests and peaks, almost fade away to uniform
neutral gray in the change image, thus demonstrating that all of the above artifacts
combined and our lack of transformation to reflectance space and lack of explicit
accounting of photometric phase angle effects, have very little influence on the change
image. Imaging time of day is potentially another factor to consider, but for any sun-
synchronous orbiting spacecraft, if latitude and longitude and day of year are controlled,
imaging time of day also is controlled. This is not generally going to be the case for
different satellites. Even when different spacecraft fly in strict formation, such as
Landsat 7 and Terra, there is a slight difference in acquisition time because of the leading
and trailing orbits.

The biggest constraint on application of this analysis approach is that images near
anniversary dates must be used; if the images are not exact anniversary dates, it is best
done near the summer or winter solstice, when the sun is not moving very rapidly from
day to day. In practice, several days difference from the anniversary date does little
harm, but much more than that would cause photometric and shadow change effects to
dominate over many of the surface material changes. There is one other option for
application of this method, and it is to choose dates that are mirrored around the solstice,
for instance one image obtained ten days before the solstice and another ten days after the
solstice, on different years; in this way the sun moves back to the same position in the
sky. However, when imaging day-of-year varies so much, some changes can occur by
differences in vegetative state, which might not be of interest for glaciologists.


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