ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-1807-2018A consistent glacier inventory for Karakoram and Pamir derived from Landsat
data: distribution of debris cover and mapping challengesInventory of glaciers and debris cover for Karakoram and PamirMölgNiconico.moelg@geo.uzh.chhttps://orcid.org/0000-0001-6223-2366BolchTobiashttps://orcid.org/0000-0002-8201-5059RastnerPhilipphttps://orcid.org/0000-0002-6761-2672StrozziTaziohttps://orcid.org/0000-0002-9054-951XPaulFrankDepartment of Geography, University of Zurich, Winterthurerstr. 190,
8057 Zurich, Switzerland, SwitzerlandGamma Remote Sensing, Worbstr. 225, 3073 Gümligen, SwitzerlandNico Mölg (nico.moelg@geo.uzh.ch)10October20181041807182713March20185April20182August20188September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/1807/2018/essd-10-1807-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/1807/2018/essd-10-1807-2018.pdf
Knowledge about the coverage and characteristics of glaciers in High Mountain
Asia (HMA) is still incomplete and heterogeneous. However, several
applications, such as modelling of past or future glacier development,
run-off, or glacier volume, rely on the existence and accessibility of
complete datasets. In particular, precise outlines of glacier extent are
required to spatially constrain glacier-specific calculations such as length,
area, and volume changes or flow velocities. As a contribution to the
Randolph Glacier Inventory (RGI) and the Global Land Ice Measurements from
Space (GLIMS) glacier database, we have produced a homogeneous inventory of
the Pamir and the Karakoram mountain ranges using 28 Landsat TM and ETM+
scenes acquired around the year 2000. We applied a standardized method of
automated digital glacier mapping and manual correction using coherence
images from the Advanced Land Observing Satellite 1 (ALOS-1) Phased Array
type L-band Synthetic Aperture Radar 1 (PALSAR-1)
as an additional source of information; we then (i) separated the glacier
complexes into individual glaciers using drainage divides derived by
watershed analysis from the ASTER global digital elevation model version 2
(GDEM2) and (ii) separately delineated all debris-covered areas. Assessment
of uncertainties was performed for debris-covered and clean-ice glacier parts
using the buffer method and independent multiple digitizing of three glaciers
representing key challenges such as shadows and debris cover. Indeed, along
with seasonal snow at high elevations, shadow and debris cover represent the
largest uncertainties in our final dataset. In total, we mapped more than
27 800 glaciers >0.02 km2 covering an area of 35520±1948 km2 and an elevation range from 2260 to 8600 m. Regional median
glacier elevations vary from 4150 m (Pamir Alai) to almost
5400 m (Karakoram), which is largely due to differences in temperature and
precipitation. Supraglacial debris covers an area of 3587±662 km2,
i.e. 10 % of the total glacierized area. Larger glaciers have a higher
share in debris-covered area (up to >20 %), making it an important
factor to be considered in subsequent applications
(10.1594/PANGAEA.894707).
Introduction
Glacier outlines and their accompanying attributes as recorded
in glacier inventories provide the baseline for climate change impact
assessments (Vaughan et al., 2013), numerous hydrology-related calculations
that consider water resources and their changes (e.g. drinking water,
irrigation, hydropower production, run-off, sea-level rise) (e.g.
Kraaijenbrink et al., 2017; Bliss et al., 2014), climatic characteristics
(Sakai et al., 2015), or modelling of past and future glacier changes (e.g.
Huss and Hock, 2015). All of this applies to most catchments of High Mountain
Asia (HMA), although some of their glacier meltwater does not directly
contribute to sea-level rise as related rivers end in endorheic basins (e.g.
Tarim basin, Aral Sea basin).
The study region in High Mountain Asia (HMA)
covers four mountain ranges. Annotations denote locations of figures in the
paper and points of orientation. International borders are tentative only as
they are disputed in several regions.
When glacier outlines are of poor quality, related hydrologic calculations at
the catchment scale (e.g. Immerzeel et al., 2010) have higher uncertainties.
This situation has improved significantly during recent years, during which
several large-scale glacier inventories for HMA have been published – among
others the Glacier Area Mapping for Discharge from the Asian Mountains
(GAMDAM) inventory (Nuimura et al., 2015) and the second Chinese Glacier
Inventory (CGI, Guo et al., 2015). Because the currently available version of
GAMDAM only partially considered ice cover on steep slopes and the CGI only
covered Chinese territory, a homogeneous basis for precise calculations
covering all relevant catchments at the large scale is still missing. As both
inventories have been combined for version 5.0 and transferred unchanged to
version 6.0 of the Randolph Glacier Inventory (RGI) (Arendt et al., 2015; RGI
Consortium, 2017), the related regional-scale calculations using this version
(e.g. Brun et al., 2017; Kraaijenbrink et al., 2017; Dehecq et al., 2015;
Kääb et al., 2015) still suffer from uncertainties which stem from
outlines of varying quality.
To overcome this situation, several regional-scale studies digitized glacier
outlines themselves (e.g. Rankl and Braun, 2016; Minora et al., 2016) to have
better control on data quality. But these again applied different criteria to
delineate glacier extents and are thus not comparable to the existing
datasets, making change assessment difficult. On the other hand, the
Karakoram and Pamir regions are characterized by a high number of surge-type
glaciers (Bhambri et al., 2017; Copland et al., 2011; Kotlyakov et al., 2008)
with often strong geometric changes over a short period of time (Paul, 2015;
Quincey et al., 2015). A precise inventory is key to determine and maybe
better understand such changes. Moreover, the large number of debris-covered
glaciers in the region (Herreid et al., 2015; Minora et al., 2016) results in
interpretation differences and is a large source of uncertainty.
The correct delineation of debris is also important for detecting very subtle
past glacier changes (Scherler et al., 2011b) and to correctly model future
glacier development (e.g. Kraaijenbrink et al., 2017; Shea et al., 2015), as
surface mass balance of ice under a supraglacial debris layer is different
from that of clean ice (Ragettli et al., 2015, 2016;
Brock et al., 2010; Nicholson and Benn, 2006). The information on debris
extent should thus be included in large-scale glacier inventories
(Kraaijenbrink et al., 2017).
The main objective of this study is to present a consistent dataset of
glacier coverage for the higher and more extensively glacierized mountain
ranges – Pamir Alai, the western and eastern Pamir, and the Karakoram of HMA
(Fig. 1) – along with the spatial distribution of debris cover for the years
around 2000. In addition, we present a structured overview of the
difficulties related to glacier mapping in this region as well as an estimate
of the respective uncertainties. Key challenges are the entity assignment, as
many glaciers in this region are of a surge type, and tributary glaciers can
be either connected to or disconnected from a larger main glacier; the
already mentioned mapping of debris-covered glacier ice; and the
differentiation of debris-covered glaciers from rock glaciers that are
increasingly abundant towards the north and the drier east of the study
region.
Study regionLocation and glacier characteristics
The study area comprises a major share of the western part of HMA (Fig. 1).
It stretches over ∼300000 km2 and fully covers the mountain
ranges of (i) Pamir and its northern neighbour Pamir Alai in Uzbekistan,
Turkmenistan, Kyrgyzstan, Tajikistan, Afghanistan and China and (ii) the
Karakoram in Pakistan, India, and China.
The mountain ranges reach their highest elevations between 5500 m (Pamir
Alai) and more than 8600 m (Karakoram, Hindu Kush), with K2 being the
highest peak with 8611 m. Glaciers are found from ∼2300 m a.s.l. up
to the highest peaks. The central Karakoram and inner Pamir are two of the
most heavily glacierized mountain regions worldwide and include some
extremely large glaciers such as Baltoro, Siachen, and Fedchenko with sizes
of about 810, 1094, and 573 km2, respectively. High peaks and deeply
incised valleys create an extreme topographic relief that is also reflected
in the geometry of the glaciers, the majority of which are valley glaciers.
In the Pamir, numerous cirques are also present, and hanging glaciers can be
found at high elevations in all regions. Larger, flat, high-altitude
accumulation areas are rare and can only be found for some of the largest
glaciers. Due to the steep terrain, most glaciers are partly fed by
avalanches from the surrounding steep valley walls (Dobreva et al., 2017;
Iturrizaga, 2011; Hewitt, 2011; Scherler et al., 2011a). This also causes an
abundance of glaciers with partly or completely debris-covered tongues.
Whereas debris cover makes glacier mapping difficult, the strong geometric
changes in surging glaciers create additional challenges for glacier
inventory compilation. Rock glaciers are present in all periglacial
environments and are abundant also in our study region.
Climate
The climate of the study region can be subdivided into two major and
independent regimes that define both the thermal and the moisture conditions.
The Pamir and the major part of the Karakoram are predominantly influenced by
westerly air flows throughout the year (e.g. Bookhagen and Burbank, 2010;
Singh et al., 1995); towards the south-east the influence of the Indian
summer monsoon (approx. June–September) becomes continuously stronger (e.g.
Archer and Fowler, 2004). In general, outer western areas of the mountain
ranges (Pamir Alai, western Pamir, Hindu Kush, south-eastern Karakoram)
receive more precipitation, whereas further inland and to the east (eastern
Pamir, central Karakoram) the climate becomes drier and more continental
(Lutz et al., 2014).
For most of the study region, the main share of precipitation falls in winter
and spring (Archer and Fowler, 2004; Bookhagen and Burbank, 2010). In regions
dominated by westerlies, winter precipitation is mostly advective, whereas
convective precipitation plays an important role in drier regions and occurs
predominantly in spring and summer (Böhner, 2006). In the eastern Pamir
and also the central–eastern Karakoram, the precipitation peak is shifted
towards spring, with important annual precipitation shares even in summer
(Zech et al., 2005; Aizen et al., 2001, 1997). In monsoon-dominated regions
(the border is roughly at 77∘ E, Bookhagen and Burbank, 2010) a
mixture of western disturbances and monsoon dominates in summer (Maussion et
al., 2014; Böhner, 2006; Bookhagen and Burbank, 2010; Archer and Fowler,
2004). Little is known about temperature and precipitation at the elevation
of glaciers; stations in valley floors yield amounts between 70 and
300 mm yr-1 (Seong et al., 2009; Archer and Fowler, 2004).
Precipitation amounts along the edge of mountain ranges and in high altitudes
are largely unknown, but can be substantially higher (“by a factor of
ten”: Wake, 1989; Immerzeel et
al., 2015), which is also suggested by snow station measurements showing snow
accumulations of >1000 mm w.e. (millimetre water equivalent) around
4000 m in the Hunza basin (Winiger et al., 2005).
List of Landsat scenes used to compile the inventory.
WRS2 path-rowDateScene IDSensorHMA region146-0368 Oct 2000LE71460362000282SGS00ETM+Karakoram147-0352 Aug 2002LE71470352002214SGS00ETM+Karakoram147-0362 Aug 2002LE71470362002214SGS00ETM+Karakoram147-03628 Aug 2000LE71470362000241SGS00ETM+Karakoram148-0354 Sep 2000LE71480352000248SGS00ETM+Karakoram148-03521 Jul 2001LE71480352001202SGS00ETM+Karakoram148-0364 Sep 2000LE71480362000248SGS00ETM+Karakoram149-03326 Jul 2009LT51490332009207KHC00TMEastern Pamir149-03413 Aug 1998LT51490341998225XXX01TMEastern Pamir, Karakoram149-03411 Sep 2000LE71490342000255SGS00ETM+Eastern Pamir149-03513 Aug 1998LT51490351998225XXX01TMKarakoram149-03529 Aug 2001LE71490352001241SGS00ETM+Karakoram150-0332 Sep 2000LE71500332000246SGS01ETM+Eastern and western Pamir150-03420 Aug 1998LT51500341998232BIK00TMEastern and western Pamir, Karakoram150-0342 Sep 2000LE71500342000246SGS01ETM+Western Pamir150-03516 Sep 1999LE71500351999259SGS00ETM+Karakoram151-03223 Sep 1999LE71510321999266EDC00ETM+Pamir Alai151-03324 Aug 2000LE71510332000237SGS00ETM+Western Pamir151-03430 Aug 2002LE71510342002242SGS00ETM+Western Pamir151-03426 Jul 2001LE71510342001207SGS00ETM+Western Pamir151-03530 Aug 2002LE71510352002242SGS00ETM+Karakoram152-03216 Sep 2000LE71520322000260SGS00ETM+Pamir Alai152-03316 Sep 2000LE71520332000260SGS00ETM+Pamir Alai, western Pamir152-0342 Aug 2001LE71520342001214SGS00ETM+Western Pamir152-03431 Aug 2000LE71520342000244SGS00ETM+Western Pamir153-03215 Sep 2000LT51530322000259XXX02TMPamir Alai153-03315 Sep 2000LT51530332000259XXX02TMPamir Alai, western Pamir154-03329 Aug 2000LE71540332000242EDC00ETM+Pamir AlaiGlacier changes
Glaciers in the Karakoram have gained considerable attention during the last
decade. The “Karakoram anomaly” that was first identified by Hewitt (2005),
based on the observed unusual behaviour of glacier termini, is now a major
research topic, and numerous studies have investigated the recent and
longer-term evolution of climate, changes in glacier extent and volume, and
glacier dynamics. These studies suggest that since the 1970s the extent and
mass of glaciers in the central Karakoram have on average hardly changed
(Bolch et al., 2017; Bajracharya et al., 2015; Bhambri et al., 2013), which
also applies to the beginning of the 21st century (Lin et al., 2017; Brun et
al., 2017; Gardelle et al., 2013; Gardner et al., 2013; Kääb et
al., 2012), while glaciers in the mountain ranges of the Hindu Kush and Hindu
Raj are mostly retreating (Sarıkaya et al., 2013; Haritashya et
al., 2009). However, the patterns of climate-induced glacier change are not
to be confounded with the strong geometric changes observed for the abundant
surge-type glaciers in the region that might occur independent of climatic
forcing (Bhambri et al., 2017; Paul, 2015; Quincey et al., 2015; Rankl et
al., 2014; Copland et al., 2011). Glaciers in the eastern Pamir were on
average almost in balance, as in the Karakoram (Brun et al., 2017; Holzer et
al., 2016). In the western Pamir, glacier volume evolution seems to be more
negative, but is for the first decade of the 2000s still relatively modest
(Brun et al., 2017); moreover, satellite images of the past two decades
reveal that many glaciers in this region have surged (e.g. Wendt et
al., 2017). However, many of the non-surge-type glaciers in the Pamir have
been continuously retreating and losing area and mass since the Little Ice
Age (Holzer et al., 2016; Khromova et al., 2006; Shangguan et al., 2006).
Input data
As a mapping basis we have used six Landsat 5 TM and 22 Landsat 7 ETM+
Level 1T scenes, with the latter offering a 15 m panchromatic band for
improved mapping quality (Table 1). Additionally, we have also used coherence
images derived from the Advanced Land Observing Satellite 1 (ALOS-1) Phased
Array type L-band Synthetic Aperture Radar 1 (PALSAR-1) scenes acquired
around 2007 to aid in mapping the debris-covered glacier parts (Atwood et
al., 2010; Frey et al., 2012) and the global digital elevation model (GDEM)
version 2 from ASTER (hereafter referred to as GDEM2; United States
Geological Survey, 2018a).
The TM and ETM+ scenes served as a basis for glacier mapping while the
coherence images were used for corrections of debris-covered glacier areas.
Moreover, satellite images available in Google Earth served as a visual
control for outline detection, with data originating mainly from very
high-resolution optical sensors such as QuickBird, Worldview, Pléiades 1A
and 1B, and SPOT 6 and SPOT 7 (GoogleEarth 2017); unfortunately these were
not available for all regions.
Coherence images have been produced from ALOS-1 PALSAR-1 scenes usually
separated by 46 days and acquired over summer (Table 2). The processing of
the images takes into account a number of effects (e.g. sensor geometry,
radiometric calibration, frequency interference) that influence the noise of
the radar interferogram. The remaining decorrelation can be ascribed to
changes in landscape surface properties, e.g. due to movement of landforms.
More details on the processing line can be found in Frey et al. (2012).
List of ALOS-1 PALSAR-1 scenes used to generate the coherence
images.
PathFramesDate 1Date 2IntervalHMA region(days)533770–78022 Jul 20096 Sep 200946Pamir Alai528720–73022 Jul 20096 Sep 200946Karakoram, western Pamir528750–76022 Jul 20096 Sep 200946Western Pamir52270022 Aug 20077 Oct 200746Karakoram52369024 Jul 20078 Sep 200746Karakoram52469010 Aug 200725 Sep 200746Karakoram524700–71010 Aug 200725 Sep 200746Karakoram524750–76010 Aug 200725 Sep 200746Eastern Pamir523690–7008 Jun 200724 Jul 200746Karakoram525700–73012 Jul 200727 Aug 200746Karakoram525750–77012 Jul 200727 Aug 200746Eastern Pamir526710–73013 Jun 200729 Jul 200746Karakoram52677013 Jun 200729 Jul 200746Eastern Pamir527710–73015 Aug 200730 Sep 200746Karakoram, western Pamir529720–75018 Jun 200718 Sep 200792Karakoram, western Pamir529760–78018 Jun 200718 Sep 200792Western Pamir, Pamir Alai530720–7505 Jul 200720 Aug 200746Karakoram, western Pamir530760–7705 Jul 200720 Aug 200746Western Pamir, Pamir Alai5307805 Jul 200720 Aug 200746Western Pamir, Pamir Alai53173022 Jul 200722 Oct 200792Western Pamir531750–77022 Jul 200722 Oct 200792Western Pamir53178022 Jul 200722 Oct 200792Western Pamir, Pamir Alai532720–7508 Aug 200723 Sep 200746Western Pamir532760–7708 Aug 200723 Sep 200746Western Pamir, Pamir Alai5327808 Aug 200723 Sep 200746Western Pamir, Pamir Alai535770–7805 Jul 200720 Aug 200746Pamir Alai53677022 Jul 200722 Oct 200792Pamir Alai5377708 Aug 200723 Sep 200746Pamir Alai
A DEM is needed to retrieve drainage divides and topographic information for
a glacier inventory. The freely available SRTM DEM (United States Geological
Survey, 2018b) and the GDEM2
(both with 30 m cell size) could have been used for this purpose. The
optical GDEM2 has a potentially reduced quality in low-contrast regions such
as shadow and snow-covered accumulation regions, but it has been averaged
from scenes acquired over a 12-year period strongly reducing these factors.
On the other hand, the SRTM DEM has a precise acquisition date (February
2000) but suffers from data voids in steep terrain due to radar shadow and
layover, which affect the final quality over glacierized areas, in particular
when using void-filled versions. A direct comparison (subtraction) of both
DEMs as recommended by Frey and Paul (2012) confirmed these differences. We
finally decided to work only with the GDEM2 as it had fewer data voids along
mountain crests (important to derive correct drainage divides) and because it
is spatially consistent; i.e. data voids over glaciers in the SRTM DEM did
not have to be filled with some other DEM data (which is beneficial for
deriving consistent topographic information and increases traceability). The
vertical accuracy was found to be around 9 m (probably higher in steep
terrain) and similar for both DEMs (Satgé et al., 2015). For consistency,
the glacier separation and all subsequent topographic analysis of glacier
elevation, slope, and aspect are thus based on the GDEM2.
MethodsGlacier mapping
We applied the well-established semi-automatic band ratio method (Paul et
al., 2002) to classify glaciers (the clean-ice and snow part), taking
advantage of the reflection contrast between snow–ice and other land
surfaces in the red and short-wave infrared (SWIR) parts of the
electromagnetic spectrum, corresponding to Landsat TM or ETM+ bands 3 (red)
and 5 (SWIR). An individual, scene-adjusted band ratio threshold between 1.5
and 3.5 is applied to separate glaciers and snow from other terrain and to
compute a binary raster image, which is smoothed using a 3 by 3 majority
filter and is then converted to a vector file for further editing.
Mapping of heavily debris-covered tongues. (a) False-colour
image overlaid by raw outlines. (b) PALSAR-1 coherence images
overlaid by raw (yellow) and corrected (red) outlines. The decorrelated image
areas are shown in black.
Elongated rock glaciers that are (almost) connected to the active
glacier tongue are hard to distinguish. (a) False-colour Landsat
image overlaid by raw outlines. (b) PALSAR-1 coherence images are
fuzzy and potentially misleading in this case. (c) High-resolution
imagery is decisive for a correct decision, because rock glaciers can be
visually distinguished from the glacier tongue.
Extensive moraines and large areas of debris can be found on dead
ice and active glaciers.
Due to the spectral similarity of debris on and off glaciers, there is so far
no method available to automatically map debris cover over a large set of
glaciers using optical satellite imagery alone. Hence, several studies have
tested combined approaches that generally include topographic information
derived from a DEM and other data (Robson et al., 2016; Racoviteanu and
Williams, 2012; Rastner et al., 2014; Bolch et al., 2007; Paul et al., 2004).
However, all methods require time-consuming manual post-processing, and the
quality of the results depends to some extent on the experience of the
analyst.
As debris-covered glacier tongues can be difficult to identify visually, even
when using high-resolution images (Paul et al., 2013), we have utilized
ALOS-1 PALSAR-1 coherence images. Such images have also been used for glacier
mapping in Alaska (Atwood et al., 2010) or as supportive means for correcting
automatically derived glacier outlines in the western Himalaya by Frey et
al. (2012). The coherence images were primarily used to detect the existence
of debris-covered glacier tongues, while the exact positions of the glacier
margin and terminus were detected using the optical Landsat image. The data
are combined to account for the time difference of up to 9 years between
coherence and Landsat images. Non-surging debris-covered glaciers commonly
change little during such periods (Scherler et al., 2011b), especially in
this region (e.g. Baltoro glacier has been stable for at least 25 years)
(Paul, 2015).
The elevation of a glacier can be described by different elevation
parameters. One that is well suited for a comparison between different
glacier types and sizes as well as an indication for climatic differences is
the median elevation, which is indicative of the equilibrium line altitude
(ELA) at a balanced mass budget (Braithwaite and Raper, 2009) and similar to
the midpoint elevation (Raper and Braithwaite, 2009), that has been used in
several studies to characterize glaciers (e.g. Haeberli and Hoelzle, 1995)
and climatic conditions, primarily precipitation amounts (e.g. Sakai et
al., 2015; Bolch et al., 2013).
Mapping challenges and solutions
The main challenges for mapping glaciers in this region are the correct
delineation of debris-covered glacier parts (including their separation from
rock glaciers), seasonal snow, cast shadow, and orographic clouds. In the
following, we shortly describe these challenges and present the techniques
applied to overcome them.
Debris cover. The main reason for extensive debris cover on glaciers
is steep/high topography with ice-free rock walls leading to rock falls and
avalanches onto the glacier surface (e.g. Herreid et al., 2015; Scherler et
al., 2011a; Paul et al., 2004). Apart from the central Karakoram, most
regions exhibit glacier recession, which is another factor for increasing
debris coverage on the glacier surface (Rowan et al., 2015; Kirkbride and
Deline, 2013).
The debris-covered glacier area in this study was mapped manually by editing
the automatically derived clean-ice outlines. Key difficulties in identifying
these regions are the small solar incidence angle at these latitudes
(reducing topographic contrast in the terminus region), unclear boundaries
between supra-glacial debris and moraines or rock glaciers, and debris in
shadow (e.g. Bishop et al., 2014). Heavily debris-covered glacier tongues are
often in contact with lateral or frontal moraines (Figs. 2, 3, and 4) and
their composition is very similar, leading to similar spectral properties and
the need of applying other measures for identification. Whereas human
recognition has the ability to trace very subtle features for identification
of debris on glaciers, the 30 m spatial resolution of Landsat images is
often too coarse for a clear assignment.
In this study we mostly relied on the ALOS-1 PALSAR-1 coherence images for
identifying the margins of debris-covered glaciers (Fig. 2). Their usability
decreases with decreasing glacier size as well as when the images become
“fuzzy” and glacier margins less clear; in high-alpine terrain this could
happen, as also glacier-free terrain can change: permafrost landforms such as
rock glaciers, talus ramparts, and moraines complicate the proper
identification of glaciers (Figs. 2, 3, and 4). In such cases we also used
the very high-resolution imagery available in Google Earth and similar tools
for identification. Furthermore, multi-temporal data aided in terminus
identification by either providing better contrast or by using them in
animations (Paul, 2015). In some cases it was also possible to consider
glaciological relations for a first approximation of glacier extent. For
example, a tiny accumulation area would likely not support a large glacier
tongue (and vice versa).
In contrast to glaciers – massive bodies of ice originating from continuous
snow accumulation – rock glaciers probably have a different genesis: they
develop in a permafrost environment either from ice-cored moraines or on
talus slopes that provide constant debris input, and they commonly have a
higher debris content than glaciers (Berthling, 2011; Haeberli et al., 2006;
Barsch, 1996). Especially towards the cold–dry regions of central Asia, rock
glaciers of both types are increasingly abundant (Bolch and Gorbunov, 2014;
Gorbunov and Titkov, 1989). In particular, moraine-derived rock glaciers
challenge the analyst as there is often a continuous transition between the
glacier and the rock glacier, making it hard to define a divide (cf. Monnier
and Kinnard, 2015). A well-developed rock glacier can in principle be
distinguished from a debris-covered glacier by characteristic surface
patterns such as the arc-shaped transverse ridge and furrow structure instead
of the longitudinal debris striations and supra-glacial ponds found on most
debris-covered glaciers (Bishop et al., 2014; Bodin et al., 2010). However,
identifying such differences using remotely sensed imagery requires a spatial
resolution better than 15 m (Paul et al., 2003) and might not work at all when rock glaciers are
not well developed. We separated debris-covered glaciers from rock glaciers
based on interpreting the above data sources (Google Earth, coherence images)
and their known morphological characteristics. In the cases where no clear
boundary could be found, we followed a more conservative interpretation that
might have resulted in a potential underestimation of the debris-covered
glacier area.
Glacier detection in shadow with the supporting input of
high-resolution Google Earth images.
Seasonal snow. Seasonal snow can obscure the underlying glacier ice
and is included in the automatic classification result due to the similar
reflection properties of snow and ice. Seasonal snow and clouds also required
consideration of scenes from years other than 2000. For larger glaciers with
a low-lying terminus, it would have been possible to adjust the (snow-free)
terminus to the year 2000 scene; we have not applied this in favour of
temporarily consistent glacier outlines. Interestingly, for some regions it
was much harder to find satellite scenes with satisfying snow conditions than
for others. It was particularly difficult for the eastern Pamir and some
parts of the northern–central Karakoram, potentially resulting in related
higher area uncertainties for the accumulation areas of the glaciers. Our
strategy to reduce the impact of wrongly mapped seasonal snow was threefold:
we applied a size filter of 0.02 km2 to remove the smallest snow patches;
snow attached to glaciers was manually removed after visual inspection, and
in some regions a different scene (with less snow but possibly more clouds)
was chosen to improve results (see Table 1). Despite these measures, we
assume that glacier area in this inventory is likely overestimated due to the
inclusion of seasonal snow.
Shadow. Cast shadows from mountains decrease reflection values,
partly down to near-zero. This results in considerable noise in this region
for a band ratio using a red (or near infrared) band. As TM band 1 (blue) is
strongly influenced by atmospheric scattering, ice and snow in shadow are
much more visible and can be distinguished using an additional threshold
(e.g. Paul and Kääb, 2005). Although the shadow problem is less
pronounced in lower latitudes due to the higher solar elevation angle, it is
still a problem in the study region due to the high and steep terrain. We
have used the additional blue band to map glaciers in shadow automatically or
applied manual corrections on contrast-enhanced true-colour composites in
case the automated refinement was not successful. We also analysed scenes
from a different date or another sensor (including very high-resolution
imagery as available in Google Earth and similar tools) to reveal if glaciers
are possibly present (Fig. 5). However, this is time-consuming and in some
regions images are not available or do not meet the criteria for glacier
identification (e.g. due to snow cover). In the cases where glaciers in
shadow could not be identified, a related underestimation of glacier area
results.
Cloud coverage. Cloud-free scenes were available for most of the
study region. In the few cases when cloud cover prevented glacier mapping,
the problem was solved multi-temporally by using additional scenes from years
close to the year 2000 (Paul et al., 2017). In some regions, scenes with high
cloud coverage and possible precipitation events were followed by scenes with
extensive snow coverage, so that we had to use scenes from other years. The
entire study region is thus a mosaic of many individual scenes (see Table 1).
Calculating the debris-covered area share of glaciers
For calculating the area share of debris cover, we decided to consider only
the ablation areas of glaciers (i.e. the region below their median
elevation), because debris deposited in the accumulation area should emerge
on the glacier surface only below the ELA (Braithwaite and Raper, 2009;
Braithwaite and Müller, 1980). We distinguished the debris cover from
snow and ice surfaces by applying a constant threshold of 2.0 to all band
ratio images from Landsat TM bands 3 and 5 (red and SWIR) and subtracted the
resulting clean-ice glacier map from the corrected glacier map. The threshold
was found empirically with satisfying results for all scenes from TM and
ETM+ sensors. Changing the threshold by ±0.2 changed the result by
less than the mapping uncertainties (∼5 % for debris-covered areas,
see Sect. 6).
Debris cover classification in the Kongur Shan in the eastern
Pamir.
Glacier definition and separation using drainage divides
We based the mapping and division of glaciers on the Global Land Ice
Measurements from Space (GLIMS) definition of a glacier (Raup and Khalsa,
2007), stating that a glacier includes “all tributaries and connected
feeders that contribute ice to the main glacier, plus all debris-covered
parts of it”. For the sake of consistency with earlier datasets and the
GLIMS definition, surge-type
tributaries were not separated from the main glacier tongue, even if they
contribute only during an active surge phase. The preparation of a dataset
where these short-term tributaries are properly separated is worthwhile but a
considerable extra effort. Stagnant ice masses (e.g. from a former surge)
that were still connected to the glacier tongue were mapped as part of the
glacier. In cases where the active glacier has clearly receded away from the
stagnant “dead” ice (e.g. after a surge phase), only the active glacier was
mapped. In contrast to this definition, the surging Bivachny glacier tongue
was separated from Fedchenko glacier in the confluence region although one
can argue that Bivachny is a connected feeder (see Fig. 6 in Wendt et
al., 2017). A size filter of 0.02 km2 was applied to remove small
seasonal snowfields and remaining noise. Automatically classified polygons
larger than this are considered as glaciers in this inventory, but this does
not mean that all seasonal or perennial snowfields have been excluded, nor
that some of the mapped glaciers are in fact perennial snow.
Glacier complexes – at least two glaciers connected in their accumulation
areas – can be split into single glaciers using drainage divides derived
from a DEM. This is performed in two steps. Firstly, raw drainage basins are
calculated by watershed analysis using a flow direction grid derived from a
sink-filled DEM. Afterwards, overlying raw basins are merged to one basin
polygon per glacier considering pour points and a buffer (Falaschi et
al., 2017; Kienholz et al., 2013; Bolch et al., 2010). This approach proved
to be robust even for the large regions of the Karakoram and Pamir as in
general glaciers are divided by very steep mountain crests. Secondly, manual
corrections were performed, which took about 90 % of the total processing
time. Gross errors were improved using a colour-coded flow direction grid in
the background, a hillshade, the original Landsat scenes, and sometimes
oblique views in Google Earth. We manually assigned the same ID to separated
glacier polygons that were obviously linked by mass transport (e.g.
regenerated glaciers).
Uncertainty estimation
Since there is no ground truth or reference data for any larger set of
glaciers in the study region, we calculated uncertainties for the relevant
input data rather than accuracy (Paul et al., 2017).
Glacier mapping uncertainties originate from the coverage of glaciers by
seasonal snow and/or debris, shadow, and clouds. These need to be corrected
manually (on-screen digitizing) by a well-trained analyst. According to the
literature, the uncertainty of automatically and manually digitized glacier
outlines (clean ice only) ranges between 2 % and 5 % and is dependent
on glacier size (Paul et al., 2013, 2011; Andreassen et al., 2008; Bolch and
Kamp, 2006). Paul et al. (2013) estimated uncertainties using a sample of
manually and automatically digitized glaciers from a number of experts and
found a mean standard deviation of ∼5 %. Other studies (Bolch et
al., 2010; Granshaw and Fountain, 2006) have used a buffer-based estimate,
where the final uncertainty depends on the pixel size of the input image. The
study by Paul et al. (2017) suggested a tiered system of uncertainty
assessment related to workload. We used three of the methods: (1) fixed
uncertainty values applied to all glaciers, (2) the buffer method with
different buffer sizes for clean and debris-covered glacier parts, and
(3) independent multiple digitization of outlines by all analysts for three
difficult debris-covered glaciers.
For (1), we applied an uncertainty of ±2 % for the clean ice and
±5 % for the debris-covered ice. This is an upper boundary estimate,
because it does not account for the overlapping area of the two surface
types. For the buffer method (2) we applied an uncertainty of ±1/2 pixel
for clean-ice parts and ±1 pixel for debris-covered parts. This also
provides an upper-bound estimate and we use the standard deviation of the
uncertainty distribution for the estimate, as a normal distribution can be
assumed for this type of mapping error. It is applied to glacier complexes
excluding overlapping areas as well as the border of clean and debris-covered
ice of the same glacier. Due to the abundant debris-covered glaciers in the
study region, we also performed method (3) to obtain a more realistic
uncertainty estimate for the analysts participating in the outline
correction. They manually corrected the outlines of three example glaciers
from different regions three times (Glacier 1:
38∘34.4′ N, 72∘12.8′ E; Glacier 2:
39∘38.8′ N, 69∘41.9′ E; Glacier 3:
36∘0.3′ N, 75∘14.6′ E), with differing additional
information being considered (e.g. coherence images and Google Earth
imagery). The glaciers are of different size and contain a substantial
debris-covered part; they also feature difficulties of moraines, glacier
confluences, and cast shadow.
As not all satellite scenes used to compile the inventory are from the same
year, there is a certain temporal uncertainty introduced. However, glacier
changes within the ±2-year difference to the target year 2000 are likely
within the uncertainty of the glacier outlines and should thus not matter.
The actual date information is given for each glacier in the attribute table.
The upper table shows the basic inventory statistics for all
glaciers, with the lower table only showing the basic inventory statistics
for glaciers larger than 5 km2.
We identified 27 877 glaciers (larger than 0.02 km2) in the four HMA
regions covering 35519.7±1958 km2; western Pamir and Karakoram
each host over 10 000 glaciers, whereas the other regions contain
2000–4000. As in other larger regions where detailed glacier inventories
have been compiled (e.g. Kienholz et al., 2015; Guo et al., 2015; Pfeffer et
al., 2014; Le Bris et al., 2011; Bolch et al., 2010), the histogram is
strongly skewed towards small glaciers (Fig. 7).
Histogram of all glaciers by number. Please note the logarithmic
scale of the left y axis.
Only 3.5 % (985) of all glaciers are larger than 5 km2, and most of
them are located in the Karakoram. In total, they cover over 60 % of the
glacierized area. On the other hand, 83 % (23 048) of all glaciers are
smaller than 1 km2 but cover only ∼15 % of the total area.
The mean glacier size is 1.29 km2, with large differences between the
regions: from 0.57 km2 in the Pamir Alai to 2.07 km2 in the Karakoram
(Table 3). The average median elevation is 4978 and 5169 m for all glaciers
and glaciers larger than 5 km2, respectively, and differs by only a few
metres from the mean elevation.
Glacier orientation of the different HMA regions. (a) shows
the values based on average glacier aspect; (b) is based on the
30 m raster cells. Lower elevations tend to have a higher share of the
north-facing glacier area. The respective numbers of “All” are given in the
table (c).
Slope per glacier regions and surface type (avg. slp.: average slope
of glacierized area; avg. slp. acc.: average slope in the accumulation area;
avg. slp. abl.: average slope in the ablation area; avg. slp. deb.: average
slope in the debris-covered area).
Extremes
In the Pamir Alai, the largest glacier is Zeravshan glacier, with an area of
106.3±6.7 km2, which is 3 times larger than the second largest.
Zeravshan glacier stretches over 2600 m from 2800 to 5400 m, close to the
highest elevations in the Pamir Alai range. The largest elevation range is
covered by Tandykul glacier (39∘27′ N, 71∘8′ E) 50 km
further east, with almost 3000 m (2450–5400 m). Its heavily debris-covered
tongue lies in a deep valley that is well shielded from the south. Overall,
only a few larger valley glaciers (19 larger than 10 km2) have several large tributaries.
In the western Pamir, Fedchenko glacier is by far the largest with 573±19.5 km2 (not including Bivachny glacier with 170±8.5 km2 since
it is in contact but not contributing). Bivachny glacier starts right below
the summit of Ismoil Somoni Peak (formerly known as Pik Kommunízma, 7495 m) and terminates at about 3420 m, whereas Fedchenko
glacier stretches from Independence Peak, with an elevation of 6940 m, down to below
2900 m; hence, both glaciers are spanning an elevation range of over
4000 m. The region hosts several large glacier systems (13 larger than
50 km2, 108 larger than 10 km2) that are arranged in two clusters:
one is around Fedchenko glacier in the Yazgulem Range and one around Lenin
Peak in the Trans-Alai Range. Also this region has steep topography and
several glaciers reach an elevation range of around 4000 m. However, these
numbers are a snapshot in time and have to be treated with care, since there
are many surge-type glaciers whose current phase state can significantly
influence minimum elevation and area (Kotlyakov et al., 2008). We found the
lowest-lying terminus at a very small, north-facing and likely avalanche-fed
glacier (70.65∘ E/38.99∘ N) in the Petra Pervogo Range,
reaching down to below 2400 m.
The eastern Pamir region has 38 glaciers larger than 10 km2, evenly
distributed over the individual mountain ranges. The largest glacier
(109.4±6.9 km2) is Karayaylak glacier, draining the northern basin
of the Kongur Shan. It starts at the top of Kongur Tagh, with the highest
mountain in the Pamir at an elevation of 7680 m, and reaches down to
2819 m, spanning an elevation range of over 4800 m, which is by far the
largest value in this region. One of its tributaries has reportedly surged in
2015 (Shangguan et al., 2016). The neighbouring Qimgan glacier starts at the
same peak facing south-east and reaches down to 3160 m (almost 4500 m
elevation range). A smaller, east-facing glacier in the Oytagh glacier park
reaches the same low elevation as Karayaylak glacier (2824 m).
Siachen glacier is the largest of its kind in the Karakoram. With an area of
1094.2±31.2 km2 (including all of its major tributaries) it is by
far the largest glacier in the study area, and with over 70 km in length
Siachen and Fedchenko are the longest glaciers in the mid-latitudes. Two more
glaciers have an area over 500 km2: Baltoro with 810±36.1 km2
and Biafo with 560±23.8 km2. Both glaciers reach their lowest
elevations in the central part of the Hunza valley (around Gilgit), with
terminus elevations of around 2500 m and below (Hopar glacier: 2260 m). Two
large glaciers reach elevation ranges of 5200 m (Batura and Baltoro), but
also smaller glaciers like Shishper (45±4.1 km2),
Passu (62.2±1.7 km2), and
Rakaposhi (14.4±0.9 km2) stretch over an elevation range of 5000 m.
Once again, many of these glaciers are of a surge type (e.g. Bhambri et
al., 2017), and their minimum elevations and area values after a surge might
strongly differ from those at the end of a quiescent phase. The highest
glacierized regions in the Karakoram are found around K2 (8611 m; Baltoro
glacier) and Distaghil Sar (7885 m; Yazghil glacier, Hispar glacier).
Glacier aspect analysis
On average, most glaciers are oriented towards the north sector (mean:
71.5%±5.4%, Fig. 8a). The relative distribution is similar
among the regions, and the largest variations occur in the aspects with a
small glacier share (SE, S, SW; normalized SD: 0.21, 0.32, 0.31). The
distribution of single cells (instead of one mean aspect value per glacier)
shows a similar pattern although with less significance of north aspect.
Nevertheless, the north sector has the highest share in all regions (mean:
56.5%±5.7%, Fig. 8b), while the south and south-west host the
smallest share of glacierized area.
In contrast to other regions, we found no correlation between median
elevation and aspect.
Glacier slope analysis
The mean slope of all glaciers is 26.4∘. It decreases to
22.6∘ for glaciers larger than 5 km2; hence, mean slope is size
dependent. The decrease in mean slope between the sample of all glaciers and
glaciers larger than 5 km2 is relatively large for Pamir Alai
(-5.6∘) and very small for the eastern Pamir (-1.4∘).
Mean slope varies between different parts of the glacier, with the
accumulation area being the steepest section and the debris-covered areas
being far flatter in all regions (Fig. 9).
To determine whether glaciers constantly get steeper from the terminus to the
upper reaches of the accumulation area, we normalized the elevation
distribution of all glaciers such that each glacier covered the value range
from 0 to 1 from the terminus to the upper end, divided into sections of 0.1.
The result clearly reveals a mean slope of about 12∘ in the lower
parts and a constant increase to over 30∘ at the highest elevations
of each glacier (Fig. 10). The uppermost band is again somewhat flatter,
possibly due to the transition of slope direction at crests. The pattern is
similar in all regions, but the slope increase along the glacier is higher
than average in the eastern Pamir and lower than average in the Pamir Alai.
Glacier slope along 10 glacier elevation sections. The glaciers were
normalized for elevation to compare high- and low-elevation glaciers.
Glacier elevation analysis
The median elevation of glaciers larger than 2 km2 ranges from 2800 to
over 6500 m. There is a statistically significant correlation (p<0.001)
between median elevation and latitude (r2=0.48) and longitude (r2=0.66), which appears as a rise of median elevation from the north-west
towards the south-east across the study region (Fig. 11).
Glacier median elevation over the study area of glaciers larger than
0.5 km2. The inset shows median elevation, standard deviations, and
minimum and maximum elevations per bin.
This rise becomes even clearer when looking at separated areas along a
“fishbone” transverse profile of our study region (inset). The average
values of each segment reveal a rise in median elevation from 3980 m (bin 1)
to 5860 m (bin 6), with an average trend of 1.9 m km-1 along the
profile.
Hypsography
Plotting the glacier hypsography of the different HMA regions (Fig. 12a)
reveals a number of further differences among the regions. Most apparent is
the difference in elevation: the median elevation extends from 4141 m (Pamir
Alai) to 5419 m (Karakoram), with the western Pamir (4941 m) and eastern
Pamir (5119 m) in between the two. Most of the glacierized area is located
in the Karakoram (60 %) where the ice is distributed over a large
elevation range (Fig. 12b). In contrast, in the Pamir Alai most of the
glacier area is situated closely around the median elevation. The large
glaciers in the Karakoram reach far down and occupy large areas in lower
elevations, further away from the median elevation than in other regions. The
eastern Pamir shows a similar drop in the area share of higher elevations,
but the curve flattens in elevations over 1000 m above the median elevation.
This is related to the shape of topography that is dominated by distinct
mountain ranges with large areas above 6500 m (Kongur, Muztag Ata, Kingata
Shan). When analysing the hypsography of glaciers with over 10 %
debris-covered area compared to the rest of the sample, the insulation effect
becomes visible, with debris-covered glaciers occupying considerably more
area at lower elevations (Fig. 12c).
Debris cover
The mapping quality of the debris-covered areas is defined by the corrected
outlines as well as by the clean-ice threshold used to differentiate between
debris cover and clean-ice surfaces. It contains the same uncertainties and
is homogeneous throughout the different Landsat scenes (Fig. 13). The total
amount of debris-covered glacier area is 3580 km2, i.e. 10 % of the
total glacierized area with small differences among the four HMA regions. The
uncertainty considering a buffer of ±1 pixel along the (manually mapped)
debris-covered glacier boundary yields ±662 km2, including a buffer
of ±1/2 pixel along the (automatically mapped) boundary between clean
and debris-covered ice, and the uncertainty increases to ±1131 km2;
the latter number is high due to the great number of small debris polygons.
The lowest and highest debris-covered area shares are found in the western
(8 %) and eastern Pamir (12 %). There is no significant relation
between glacier size and debris-covered area share. The distribution in
aspect is somewhat skewed towards the north and north-east (12 % and
11 % vs. 8 %–9 % in E, SE, S, SW, W, NW), but this is less of a
systematic pattern than for the total glacierized area. The highest values
are found in the eastern Pamir where north-facing glaciers are debris covered
by over 17 %, whereas Pamir Alai exhibits the largest range
(N = 15 % vs. SW = 6 %).
Generally, there is no relation between the mean slope of a glacier and the
area share of its debris cover. However, the mean slope of the debris-covered
part of the glaciers is 16.6∘ (±5.5), whereas the mean slope of
these glaciers is 26.1∘ (±3.2). This was expected since the
debris cover is usually situated at the flatter glacier tongues (Paul et
al., 2004). Looking at the ablation area of all glaciers, the mean slope is
25.0∘ (±4.2). The ablation areas of more strongly debris-covered
glaciers are somewhat flatter: glaciers with a debris-covered area of ≥10 % on average have a steepness of 22.7∘ (±4.0); in
contrast, glaciers with less than 5 % debris cover have a mean slope of
25.7∘ (±4.1).
Uncertainties and the multiple digitizing experiment
By applying previously assumed area uncertainties (±2.5 % for clean
ice, ±5 % for debris-covered ice) to the mapped glacier area, the
derived total glacier area is 35520±1955 km2 . With the buffer
method (clean ice ±1/2, debris-covered ice ±1 pixel) we obtain a
similar uncertainty of ±1948 km2. Both methods are applied to
glacier complexes to avoid double counting of overlapping areas of adjacent
glaciers. Finally, the multiple digitization experiment resulted in a ±13 % standard deviation (averaged over all experiments). This value
appears high, but it reflects the mapping reality in challenging situations
with debris-covered glacier tongues. For two of the three test glaciers, the
difference between the largest and the smallest area mapped was less than
5 % of the mean glacier area. The third example is a small (∼2.9 km2) and steep glacier with a high share of its area hidden in
shadow, a large and barely visible debris-covered part and adjacent rock
glaciers (see Fig. S2 in the Supplement).
Here, the respective uncertainty is ±33 %. Taking this as a
worst-case scenario, only few such cases exist in a larger inventory and the
high uncertainty has little impact on the overall uncertainty.
Glacier hypsography of the different regions (a),
normalized by the respective median elevation (b). Dashed lines
represent the 25 % and 75 % area elevations. (c) Hypsography
comparison of more and less debris-covered glaciers.
Debris cover on glaciers in the central Karakoram.
Paul et al. (2013, 2015) showed that analyst interpretations for
debris-covered glaciers and glacier parts in shadow can differ by up to
50 %. Our experiment showed that, if the glacier is affected by both
shadow and debris cover and is additionally small, the differences can be
even higher with up to 70 %. The experiment also confirmed that area
differences mainly depend on the interpretation of the debris-covered parts.
Thereby, using coherence images improved the analyst's interpretation.
Although the overall effect was small (on average ∼1 %), it
reduced the dispersion of the analyst's interpretations considerably (see
Fig. 14). The different timing of Landsat (2000) and ALOS-1 PALSAR-1
(2007–2009) imagery had only a small impact, as geometric changes during
these 7 years were small. The use of Google Earth imagery did not lead to
notable outline modifications as they either had low quality (resolution,
snow cover) or provided a mere confirmation of the existing interpretation
from Landsat and coherence images. We conclude that the area uncertainty of
the debris-covered parts of a glacier is of the order of 10 % to
20 %. However, at least one third of this uncertainty can be disregarded
due to direct contact to clean-ice glacier parts (see Fig. 13).
Results of the expert round robin, example Glacier 2.
(a) shows mapping results solely based on the satellite image,
whereas (b) shows mapping results after manual corrections using the
additional source of coherence images and Google Earth high-resolution
imagery.
The mapping uncertainty for the clean-ice glacier parts was found to be low,
notwithstanding the simple method applied (constant threshold for all
scenes). Using different thresholds of 2.0±0.3 yielded results in the
range of 5 % of the debris-covered area, which is smaller than the
uncertainty from the manual correction of the debris-covered glacier parts.
All uncertainty values have to be seen from the perspective of methodological
uncertainties, e.g. the inclusion of possible snowfields at high elevations,
which can easily increase the area of a small glacier by 50 % or more.
With this in mind, the uncertainties presented above are in general much
smaller and are more of an academic nature. As the uncertainties from the
expert round robin are close to those from the buffer method, we use the
uncertainty derived by the buffer method as the uncertainties assigned to our
results, knowing that they are on the conservative side.
We also performed a comparison in regions where the CGI (Guo et al., 2015),
the GAMDAM (Nuimura et al., 2015), and our inventory have mapped the
glaciers, to determine major differences among them. Compared to the CGI, our
total glacier area is ∼15 % larger (despite a similar glacier
definition) and the CGI overlaps with 82 % of our inventory. Our
debris-covered areas are somewhat larger along the margins of the tongues and
more of the smaller glaciers at higher elevations are included (Fig. 15).
Regions where the CGI area is larger (7 % in total) are related to the
inclusion of areas enclosed by different branches of the same glacier, as
well as dead ice and rock glaciers in front of a terminus. The GAMDAM
inventory covers 13 % less area than ours and also overlaps with 82 %
of the area. Here the difference is clearly linked to a diverging glacier
mapping definition that mostly excludes headwalls steeper than 40∘
(Nuimura et al., 2015). Moreover, many debris-covered glacier areas and in
some cases entire glaciers have not been mapped. On the other hand, almost
all of the areas covered by GAMDAM but not by our inventory are mapped as
debris-covered glaciers. We think that excluding steep headwalls leads to an
incomplete inventory and that the inclusion of rock outcrops in the CGI
constitutes a commission error that needs to be corrected for some
applications. Overall, the differing interpretation of debris-covered glacier
parts and seasonal snow is seemingly the main source of differences in
glacier extents for the same region when mapped by different analysts.
Comparison example of the three inventories.
Discussion
When compiling a large-scale glacier inventory, it is essential to have
homogeneous quality of the input data used, at best also in a temporal sense,
to ensure high credibility of the resulting outlines and topographic
parameters as well as low uncertainties of the glacierized area. This can be
achieved by using globally consistent datasets such as the Landsat images and
the GDEM2. However, the latter does not fulfil the criterion of temporal
consistency and future work might overcome this issue. There are strategies
like DEM fusion to improve DEM quality in regions of very steep terrain or
low-contrast glacier surfaces (e.g. Shean et al., 2016; Lee et al., 2015;
Tran et al., 2014 and references therein), but the impact of such quality
issues is difficult to assess without accurately geo-referenced
high-resolution reference data (Kääb et al., 2016; Frey and Paul,
2012). With the semi-automated processing line applied here and the few
experts involved in the manual corrections, we assume a homogeneous quality
of the glacier outlines throughout the study area has been achieved. However,
our glacier extents are likely on the conservative side for debris-covered
ablation areas, leading to an underestimation of glacier area, whereas the
included perennial ice and snowfields in steep terrain at high elevations are
more generous than in other interpretations.
The extraction of debris-covered ice was performed automatically by applying
a single threshold value to all scenes and removing the resulting clean-ice
areas from the corrected glacier polygons. This is likely the easiest method
that still provided good results. Adapting the clean-ice threshold changed
the resulting debris-covered area only by ±2.5 %, indicating that
the transition from clean ice to continuous debris cover is relatively sharp.
Herreid et al. (2015) used a function applied to Landsat band combinations
that was fit to manually derived reference data of a single glacier and
adapted it to the various mapping dates (using different Landsat sensors).
This method might be superior, but it is more labour-intensive and visual
comparison with the figures in Herreid et al. (2015) shows very high
agreement. Another approach was applied by Kraaijenbrink et al. (2017), who
used the normalized difference snow index (NDSI) together with a composite
image of Landsat 8 band 10 (thermal infrared) scenes to detect debris cover
based on the RGI 5.0 outlines. A major prerequisite for all methods is the
use of glacier outlines that are well adjusted for debris cover. Glacier
retreat was found to correlate with an increase in supraglacial debris cover
(e.g. Stokes et al., 2007) and, hence, multi-temporal mapping of debris
extent should be applied. As extensive debris cover affects glacier melt and
geometry (e.g. Anderson and Anderson, 2016), we recommend including it in the
published glacier inventories (GLIMS, RGI), by (a) adding the debris mask as
a polygon and (b) including debris cover share in the attribute table. Our
results show a total of ∼10 % debris-covered area, with many of the
larger glaciers reaching 20 % or more. These numbers complement and
confirm existing estimates in HMA that are based on smaller samples. Values
reported from the central Karakoram are 20 % (Minora et al., 2016) and
∼21 % (Herreid et al., 2015), Frey et al. (2012) calculated
16 % for the western Himalaya, and for the entire Himalaya a ∼10 % coverage was calculated (Kraaijenbrink et al., 2017; Bolch et
al., 2012).
Comparing the freely available results by Kraaijenbrink et al. (2017) to our
results, the total difference is <1.5 % of the total debris-covered
area (3365 km2 vs. 3409 km2), a value well below the uncertainty of
these areas. However, the differences vary from one region to another, from
-18.4 % in the Pamir west to 5.7 % in the Karakoram.
The pattern of glacier median elevations found in our study reflects
combinations of climatic and topographic aspects. A similar west–east and
north–south gradient was also found in the study by Sakai et al. (2015), who
determined median elevations from a glacier inventory (GAMDAM, Nuimura et
al., 2015) for all of HMA. Whereas the latitudinal extent of 7∘
decreases air temperatures and thus median elevations towards the north, the
precipitation decrease from west to east due to leeward rain shadow effects
increases median elevations in the eastern Pamir and Karakoram. Approximating
the balanced-budget ELA (ELA0) with the median elevation has been
successfully applied in many mountain ranges and works well for different
glacier types (Braithwaite and Raper, 2009). However, this concept likely
does not apply to surge-type glaciers and glaciers that are mainly nourished
by snow avalanches (Hewitt, 2011). For the latter as well as debris-covered
glaciers, ELA0 values are expected to be higher than those calculated
here due to the additional accumulation and reduced ablation. This is
supported by the fact that we find debris-covered areas also above the median
elevation and by a finding from Braithwaite and Raper (2009), who mention
possible accumulation-area ratio values below 0.5 for Himalayan glaciers.
We also performed a detailed analysis of uncertainties and analysed the most
important sources contributing to uncertainty. It is, however, impossible to
perform a rigorous calculation, as this would require a comparison with
appropriate reference data. The uncertainties presented here are based on
different methods and some of the values are higher than reported previously.
This is mainly because of the high debris coverage and the large number of
(very) small glaciers. Under such challenging conditions, area differences
among the analysts were as high as uncertainties due to the possible wrong
consideration of seasonal snow. Hence, the total area of our inventory will
likely be somewhat larger than other inventories for this region as these
might have excluded the maybe just snow-covered steep regions at highest
elevations. Once scenes without seasonal snow in these regions become
available, glacier extents should be revisited and corrected as required.
The dataset is downloadable at
10.1594/PANGAEA.894707 (Mölg et al., 2018).
Conclusion
We derived a new glacier inventory for a substantial
part of western High Mountain Asia (Karakoram and Pamir) and have presented
in detail the derived characteristics of the glaciers in this region. Special
emphasis was given to the description of mapping challenges for
debris-covered glaciers (and distinguishing them from rock glaciers),
seasonal snow, and shadow, along with the selected solutions. In the absence
of appropriate reference datasets, we instead applied various methods for
uncertainty assessment and compared our outlines to other existing
inventories covering the same region. As an extension to already existing
datasets we included outlines and percentages of the debris-covered area for
each glacier.
We mapped 27 437 glaciers covering 35287±1209 km2, with about
10 % of the area being debris covered. The ASTER GDEM2 was found to be
superior to the SRTM DEM (1 arcsec) in deriving drainage divides and
topographic information for each glacier as the latter suffered from too many
(wrongly interpolated) data voids in this region. The application of a
constant band ratio threshold to derive clean-ice areas for all scenes to
create the debris cover maps was found to be of sufficient quality.
Uncertainties derived from three different methods were all in good agreement
(3.4 %) but the multiple-digitizing experiment also revealed larger
deviations among the analysts under challenging conditions (debris, shadow,
small glacier). However, the availability of coherence images improved the
quality and consistency of the manual corrections for debris-covered glaciers
considerably.
The analysis of the topographic information revealed several interesting
dependencies among the glaciers and also across the regions. Despite the fact
that in the Karakoram the largest glaciers are facing south-east (Siachen,
Biafo), east (Batura, Skamri), or west (Baltoro, Hispar), most glacier area
(47 %) is still exposed to the three northern sectors. Glacier median
elevation has little dependence on aspect but a strong one on longitude and
latitude (higher towards the drier north and east), indicating a close
relation to precipitation amounts. Glacier hypsometry reveals a peak
distribution that is highest (∼5700 m) in the Karakoram, similar but
700 m lower in the eastern and western Pamir, and lowest in Pamir Alai
(∼4200 m). Glaciers in the Karakoram have a comparably higher area
share at the lowest elevations and glaciers larger than 5 km2 or
debris-covered glaciers are flatter (22.6 and 16.6∘, respectively)
than on average (26.4∘). By location, glaciers are especially flat
(<15∘) in their lowest third and progressively steeper (>30∘) in the uppermost third, indicating the dominance of large
valley glaciers with very flat tongues and steep head walls. Both glacier
outlines and the separate outlines of the debris-covered parts will be freely
available from the GLIMS database to facilitate applications such as
distributed mass balance modelling and albedo calculation; debris-thickness
calculation; determination of run-off (with a melt reduction under
debris-covered areas); and future geometric evolution, sediment transport,
and mountain erosion rates, to name a few.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-10-1807-2018-supplement.
NM, FP, TB, and PR designed the study. NM and FP
wrote the manuscript. NM, PR, TB, and FP generated and edited
glacier outlines and basins. TS produced the coherence
images. NM extracted debris cover and performed all
analysis. All authors contributed to the final form
of the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank J. Graham Cogley and an anonymous reviewer as well as the journal
editor for their thorough reviews and constructive comments that helped
improve the clarity of the paper. We acknowledge funding from the ESA
projects Glaciers-cci (4000109873/14/I-NB) and Dragon 4 (4000121469/17/I-NB) as well as the
Copernicus Climate Change Service (C3S) that is implemented by ECMWF on behalf of the European Commission.
The manual digitizing experiment was performed by the authors with additional
contributions by Holger Frey and Raymond Le Bris.
Edited by: Reinhard Drews
Reviewed by: J. Graham Cogley and one anonymous referee
ReferencesAizen, E. M., Aizen, V. B., Melack, J. M., Nakamura, T., and Ohta, T.:
Precipitation and atmospheric circulation patterns at mid-latitudes of Asia,
Int. J. Climatol., 21, 535–556, 10.1002/joc.626, 2001.
Aizen, V. B., Aizen, E. M., Melack, J. M., and Dozier, J.: Climatic and
Hydrologic Changes in the Tien Shan, Central Asia, J. Climate, 10,
1393–1404, 1997.Anderson, L. S. and Anderson, R. S.: Modeling debris-covered glaciers:
response to steady debris deposition, The Cryosphere, 10, 1105–1124,
10.5194/tc-10-1105-2016, 2016.Andreassen, L. M., Paul, F., Kääb, A., and Hausberg, J. E.:
Landsat-derived glacier inventory for Jotunheimen, Norway, and deduced
glacier changes since the 1930s, The Cryosphere, 2, 131–145,
10.5194/tc-2-131-2008, 2008.Archer, D. R. and Fowler, H. J.: Spatial and temporal variations in
precipitation in the Upper Indus Basin, global teleconnections and
hydrological implications, Hydrol. Earth Syst. Sci., 8, 47–61,
10.5194/hess-8-47-2004, 2004.Arendt, A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen,
J.-O., Hock, R., Huss, M., Kaser, G., Kienholz, C., Pfeffer, W. T., Moholdt,
G., Paul, F., Radić, V., Andreassen, L. M., Bajracharya, S., Barrand, N.
E., Beedle, M., Berthier, E., Bhambri, R., Brown, I., Burgess, E. W.,
Burgess, D., Cawkwell, F., Chinn, T., Copland, L., Davies, B., Angelis, H.
de, Dolgova, E., Earl, L., Filbert, K., Forester, R., Fountain, A. G., Frey,
H., Giffen, B., Glasser, N. F., Guo, W., Gurney, S. D., Hagg, W., Hall, D.,
Haritashya, U. K., Hartmann, G., Helm, C., Herreid, S., Howat, I., Kapustin,
G., Khromova, T. E., König, M., Kohler, J., Kriegel, D., Kutuzov, S.,
Lavrentiev, I., Le Bris, R., Liu, S., Lund, J., Manley, W., Marti, R., Mayer,
C., Miles, E. S., Li, X., Menounos, B., Mercer, A., Mölg, N., Mool, P.,
Nosenko, G., Negrete, A., Nuimura, T., Nuth, C., Pettersson, R., Racoviteanu,
A., Ranzi, R., Rastner, P., Rau, F., Raup, B., Rich, J., Rott, H., Sakai, A.,
Schneider, C., Seliverstov, Y., Sharp, M. J., Sigurðsson, O., Stokes, C.
R., Way, R. G., Wheate, R., Winsvold, S., Wolken, G., Wyatt, F., and
Zheltihyna, N.: Randolph Glacier Inventory – A Dataset of Global Glacier
Outlines: Version 5.0: GLIMS Technical Report, Global Land Ice Measurement
from Space, Colorado, USA, Digital Media, 10.7265/N5-RGI-50 (last
access: 13 March 2018), 2015.Atwood, D. K., Meyer, F., and Arendt, A.: Using L-band SAR coherence to
delineate glacier extent, Can. J. Remote Sens., 36, S186–S195,
10.5589/m10-014, 2010.Bajracharya, S. R., Maharjan, S. B., Shrestha, F., Guo, W., Liu, S.,
Immerzeel, W., and Shrestha, B.: The glaciers of the Hindu Kush Himalayas:
Current status and observed changes from the 1980s to 2010, Int. J. Water
Resour. D., 31, 161–173, 10.1080/07900627.2015.1005731, 2015.
Barsch, D.: Rockglaciers: Indicators for the Present and Former Geoecology in
High Mountain Environments, in: Springer Series in Physical Environment,
Springer Berlin Heidelberg, Berlin, Heidelberg, 16, 331 pp., 1996.Berthling, I.: Beyond confusion: Rock glaciers as cryo-conditioned landforms,
Geomorphology, 131, 98–106, 10.1016/j.geomorph.2011.05.002, 2011.Bhambri, R., Bolch, T., Kawishwar, P., Dobhal, D. P., Srivastava, D., and
Pratap, B.: Heterogeneity in glacier response in the upper Shyok valley,
northeast Karakoram, The Cryosphere, 7, 1385–1398,
10.5194/tc-7-1385-2013, 2013.Bhambri, R., Hewitt, K., Kawishwar, P., and Pratap, B.: Surge-type and
surge-modified glaciers in the Karakoram, Scientific Reports, 7, 15391,
10.1038/s41598-017-15473-8, 2017.
Bishop, M. P., Shroder, J. F., Ali, G., Bush, A. B. G., Haritashya, U. K.,
Roohi, R., Sarikaya, M. A., and Weihs, B. J.: Remote Sensing of Glaciers in
Afghanistan and Pakistan, in: Global Land Ice Measurements from Space, edited
by: Kargel, J. S., Leonard, G. J., Bishop, M. P., Kääb, A., and Raup,
B. H., Springer Berlin Heidelberg, Berlin, Heidelberg, 509–548, 2014.Bliss, A., Hock, R., and Radić, V.: Global response of glacier runoff to
twenty-first century climate change, J. Geophys. Res. Earth, 119, 717–730,
10.1002/2013JF002931, 2014.Bodin, X., Rojas, F., and Brenning, A.: Status and evolution of the
cryosphere in the Andes of Santiago (Chile, 33.5∘S.), Geomorphology,
118, 453–464, 10.1016/j.geomorph.2010.02.016, 2010.Böhner, J.: General climatic controls and topoclimatic variations in
Central and High Asia, Boreas, 35, 279–295,
10.1111/j.1502-3885.2006.tb01158.x, 2006.Bolch, T. and Gorbunov, A. P.: Characteristics and Origin of Rock Glaciers in
Northern Tien Shan (Kazakhstan/Kyrgyzstan), Permafrost Periglac., 25,
320–332, 10.1002/ppp.1825, 2014.
Bolch, T. and Kamp, U.: Glacier mapping in high mountains using DEMs, Landsat
and ASTER data, in: Proceedings of the 8th International Symposium on High
Mountain Remote Sensing Cartography, La Paz, Bolivia, 21–27 March 2005,
edited by: Kaufmann, V. and Sulzer, W., 2006.
Bolch, T., Buchroithner, M., Kunert, A., and Kamp, U.: Automated delineation
of debris-covered glaciers based on ASTER data, in: GeoInformation in Europe,
edited by: Gomarasca, A., Millpress, Rotterdam, 403–410, 2007.Bolch, T., Menounos, B., and Wheate, R.: Landsat-based inventory of glaciers
in western Canada, 1985-2005, Remote Sens. Environ., 114, 127–137,
10.1016/j.rse.2009.08.015, 2010.Bolch, T., Kulkarni, A., Kaab, A., Huggel, C., Paul, F., Cogley, J. G., Frey,
H., Kargel, J. S., Fujita, K., Scheel, M., Bajracharya, S., and Stoffel, M.:
The state and fate of Himalayan glaciers, Science, 336, 310–314,
10.1126/science.1215828, 2012.Bolch, T., Sandberg Sørensen, L., Simonsen, S. B., Mölg, N., Machguth,
H., Rastner, P., and Paul, F.: Mass loss of Greenland's glaciers and ice caps
2003–2008 revealed from ICESat laser altimetry data, Geophys. Res. Lett.,
40, 875–881, 10.1002/grl.50270, 2013.Bolch, T., Pieczonka, T., Mukherjee, K., and Shea, J.: Brief communication:
Glaciers in the Hunza catchment (Karakoram) have been nearly in balance since
the 1970s, The Cryosphere, 11, 531–539, 10.5194/tc-11-531-2017, 2017.Bookhagen, B. and Burbank, D. W.: Toward a complete Himalayan hydrological
budget: Spatiotemporal distribution of snowmelt and rainfall and their impact
on river discharge, J. Geophys. Res., 115, F03019,
10.1029/2009JF001426, 2010.
Braithwaite, R. J. and Müller, F.: On the parameterization of glacier
equilibrium line altitude, in: Proceedings of the Workshop at Riederalp,
Switzerland, 17–22 September 1978, IAHS-AISH Publ. No. 126, 263–271, 1980.Braithwaite, R. J. and Raper, S.C.B.: Estimating equilibrium-line altitude
(ELA) from glacier inventory data, Ann. Glaciol., 50, 127–132,
10.3189/172756410790595930, 2009.Brock, B. W., Mihalcea, C., Kirkbride, M. P., Diolaiuti, G., Cutler, M. E.
J., and Smiraglia, C.: Meteorology and surface energy fluxes in the
2005–2007 ablation seasons at the Miage debris-covered glacier, Mont Blanc
Massif, Italian Alps, J. Geophys. Res., 115, D09106,
10.1029/2009JD013224, 2010.Brun, F., Berthier, E., Wagnon, P., Kääb, A., and Treichler, D.: A
spatially resolved estimate of High Mountain Asia glacier mass balances,
2000–2016, Nat. Geosci., 10, 668–673, 10.1038/NGEO2999, 2017.Copland, L., Sylvestre, T., Bishop, M. P., Shroder, J. F., Seong, Y. B.,
Owen, L. A., Bush, A., and Kamp, U.: Expanded and Recently Increased Glacier
Surging in the Karakoram, Arct. Antarct. Alp. Res., 43, 503–516,
10.1657/1938-4246-43.4.503, 2011.Dehecq, A., Gourmelen, N., and Trouve, E.: Deriving large-scale glacier
velocities from a complete satellite archive: Application to the
Pamir–Karakoram–Himalaya, Remote Sens. Environ., 162, 55–66,
10.1016/j.rse.2015.01.031, 2015.Dobreva, I., Bishop, M., and Bush, A.: Climate–Glacier Dynamics and
Topographic Forcing in the Karakoram Himalaya: Concepts, Issues and Research
Directions, Water, 9, 405, 10.3390/w9060405, 2017.Falaschi, D., Bolch, T., Rastner, P., Lenzano, M. G., Lenzano, L., Lo Veccio,
A., and Moragues, S.: Mass changes of alpine glaciers at the eastern margin
of the Northern and Southern Patagonian Icefields between 2000 and 2012,
J. Glaciol., 63, 258–272, 10.1017/jog.2016.136, 2017.Frey, H. and Paul, F.: On the suitability of the SRTM DEM and ASTER GDEM for
the compilation of topographic parameters in glacier inventories, Int.
J. Appl. Earth Obs., 18, 480–490, 10.1016/j.jag.2011.09.020, 2012.Frey, H., Paul, F., and Strozzi, T.: Compilation of a glacier inventory for
the western Himalayas from satellite data: Methods, challenges, and results,
Remote Sens. Environ., 124, 832–843, 10.1016/j.rse.2012.06.020, 2012.Gardelle, J., Berthier, E., Arnaud, Y., and Kääb, A.: Region-wide
glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011,
The Cryosphere, 7, 1263–1286, 10.5194/tc-7-1263-2013, 2013.Gardner, A. S., Moholdt, G., Cogley, J. G., Wouters, B., Arendt, A. A., Wahr,
J., Berthier, E., Hock, R., Pfeffer, W. T., Kaser, G., Ligtenberg, S. R. M.,
Bolch, T., Sharp, M. J., Hagen, J. O., van den Broeke, M. R., and Paul, F.:
A reconciled estimate of glacier contributions to sea level rise: 2003 to
2009, Science, 340, 852–857, 10.1126/science.1234532, 2013.
Gorbunov, A. P. and Titkov, S. N.: Kamennye Gletchery Gor Srednej Azii (Rock
glaciers of the Central Asian Mountains), Akademia Nauk SSSR, Irkutsk, 1989.Granshaw, F. D. and Fountain, A. G.: Glacier change (1958–1998) in the North
Cascades National Park Complex, Washington, USA, J. Glaciol., 52, 251–256,
10.3189/172756506781828782, 2006.Guo, W., Liu, S., Xu, J., Wu, L., Shangguan, D., Yao, X., Wei, J., Bao, W.,
Yu, P., Liu, Q., and Jiang, Z.: The second Chinese glacier inventory: Data,
methods and results, J. Glaciol., 61, 357–372, 10.3189/2015JoG14J209,
2015.Haeberli, W. and Hoelzle, M.: Application of inventory data for estimating
characteristics of and regional climate-change effects on mountain glaciers:
A pilot study with the European Alps, Ann. Glaciol., 21, 206–212,
10.3189/S0260305500015834, 1995.Haeberli, W., Hallet, B., Arenson, L., Elconin, R., Humlum, O., Kääb,
A., Kaufmann, V., Ladanyi, B., Matsuoka, N., Springman, S., and Mühll, D.
V.: Permafrost creep and rock glacier dynamics, Permafrost Periglac., 17,
189–214, 10.1002/ppp.561, 2006.Haritashya, U. K., Bishop, M. P., Shroder, J. F., Bush, A. B. G., and Bulley,
H. N. N.: Space-based assessment of glacier fluctuations in the Wakhan Pamir,
Afghanistan, Climatic Change, 94, 5–18, 10.1007/s10584-009-9555-9,
2009.Herreid, S., Pellicciotti, F., Ayala, A., Chesnokova, A., Kienholz, C.,
Shea, J., and Shrestha, A.: Satellite observations show no net change in the
percentage of supraglacial debris-covered area in northern Pakistan from
1977 to 2014, J. Glaciol., 61, 524–536,
10.3189/2015JoG14J227, 2015.
Hewitt, K.: The Karakoram Anomaly? Glacier Expansion and the 'Elevation
Effect' Karakoram Himalaya, Mt. Res. Dev., 25, 332–340, 2005.Hewitt, K.: Glacier Change, Concentration, and Elevation Effects in the
Karakoram Himalaya, Upper Indus Basin, Mt. Res. Dev., 31, 188–200,
10.1659/MRD-JOURNAL-D-11-00020.1, 2011.
Holzer, N., Golletz, T., Buchroithner, M., and Bolch, T.: Glacier Variations
in the Trans Alai Massif and the Lake Karakul Catchment (Northeastern Pamir)
Measured from Space, in: Climate Change, Glacier Response, and Vegetation
Dynamics in the Himalaya, edited by: Singh, R. B., Schickhoff, U., and Mal,
S., Springer International Publishing, Cham, 139–153, 2016.Huss, M. and Hock, R.: A new model for global glacier change and sea-level
rise, Front. Earth Sci., 3, 382, 10.3389/feart.2015.00054, 2015.Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate change
will affect the Asian water towers, Science, 328, 1382–1385,
10.1126/science.1183188, 2010.Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M., and Bierkens, M. F.
P.: Reconciling high-altitude precipitation in the upper Indus basin with
glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 19, 4673–4687,
10.5194/hess-19-4673-2015, 2015.Iturrizaga, L.: Trends in 20th century and recent glacier fluctuations in the
Karakoram Mountains, Z. Geomorphol. Supp., 55, 205–231,
10.1127/0372-8854/2011/0055S3-0059, 2011.Kääb, A., Berthier, E., Nuth, C., Gardelle, J., and Arnaud, Y.:
Contrasting patterns of early twenty-first-century glacier mass change in the
Himalayas, Nature, 488, 495–498, 10.1038/nature11324, 2012.Kääb, A., Treichler, D., Nuth, C., and Berthier, E.: Brief
Communication: Contending estimates of 2003–2008 glacier mass balance over
the Pamir–Karakoram–Himalaya, The Cryosphere, 9, 557–564,
10.5194/tc-9-557-2015, 2015.Kääb, A., Winsvold, S., Altena, B., Nuth, C., Nagler, T., and Wuite,
J.: Glacier Remote Sensing Using Sentinel-2. Part I: Radiometric and
Geometric Performance, and Application to Ice Velocity, Remote Sensing, 8,
598, 10.3390/rs8070598, 2016.Khromova, T. E., Osipova, G. B., Tsvetkov, D. G., Dyurgerov, M. B., and
Barry, R. G.: Changes in glacier extent in the eastern Pamir, Central Asia,
determined from historical data and ASTER imagery, Remote Sens. Environ.,
102, 24–32, 10.1016/j.rse.2006.01.019, 2006.Kienholz, C., Hock, R., and Arendt, A. A.: A new semi-automatic approach for
dividing glacier complexes into individual glaciers, J. Glaciol., 59,
925–937, 10.3189/2013JoG12J138, 2013.Kienholz, C., Herreid, S., Rich, J. L., Arendt, A. A., Hock, R., and Burgess,
E. W.: Derivation and analysis of a complete modern-date glacier inventory
for Alaska and northwest Canada, J. Glaciol., 61, 403–420,
10.3189/2015JoG14J230, 2015.Kirkbride, M. P. and Deline, P.: The formation of supraglacial debris covers
by primary dispersal from transverse englacial debris bands, Earth Surf.
Proc. Land., 38, 1779–1792, 10.1002/esp.3416, 2013.Kotlyakov, V. M., Osipova, G. B., and Tsvetkov, D. G.: Monitoring surging
glaciers of the Pamirs, central Asia, from space, Ann. Glaciol., 48,
125–134, 10.3189/172756408784700608, 2008.Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F., and Immerzeel, W.
W.: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's
glaciers, Nature, 549, 257–260, 10.1038/nature23878, 2017.Le Bris, R., Paul, F., Frey, H., and Bolch, T.: A new satellite-derived
glacier inventory for western Alaska, Ann. Glaciol., 52, 135–143,
10.3189/172756411799096303, 2011.Lee, C., Oh, J., Hong, C., and Youn, J.: Automated Generation of a Digital
Elevation Model Over Steep Terrain in Antarctica From High-Resolution
Satellite Imagery, IEEE T. Geosci. Remote, 53, 1186–1194,
10.1109/TGRS.2014.2335773, 2015.Lin, H., Li, G., Cuo, L., Hooper, A., and Ye, Q.: A decreasing glacier mass
balance gradient from the edge of the Upper Tarim Basin to the Karakoram
during 2000–2014, Scientific Reports, 7, 6712,
10.1038/s41598-017-07133-8, 2017.Lutz, A. F., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.:
Consistent increase in High Asia's runoff due to increasing glacier melt and
precipitation, Nat. Clim. Change, 4, 587–592, 10.1038/nclimate2237,
2014.Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and
Finkelnburg, R.: Precipitation Seasonality and Variability over the Tibetan
Plateau as Resolved by the High Asia Reanalysis*, J. Climate, 27,
1910–1927, 10.1175/JCLI-D-13-00282.1, 2014.Minora, U., Bocchiola, D., D'Agata, C., Maragno, D., Mayer, C., Lambrecht,
A., Vuillermoz, E., Senese, A., Compostella, C., Smiraglia, C., and
Diolaiuti, G. A.: Glacier area stability in the Central Karakoram National
Park (Pakistan) in 2001–2010, Prog. Phys. Geog., 40, 629–660,
10.1177/0309133316643926, 2016.Mölg, N., Bolch, T., Rastner, P., Strozzi, T., and Paul, F.: Glacier
inventory of Pamir and Karakoram, 10.1594/PANGAEA.894707,
in review, 2018.Monnier, S. and Kinnard, C.: Reconsidering the glacier to rock glacier
transformation problem: New insights from the central Andes of Chile,
Geomorphology, 238, 47–55, 10.1016/j.geomorph.2015.02.025, 2015.Nicholson, L. and Benn, D. I.: Calculating ice melt beneath a debris layer
using meteorological data, J. Glaciol., 52, 463–470,
10.3189/172756506781828584, 2006.Nuimura, T., Sakai, A., Taniguchi, K., Nagai, H., Lamsal, D., Tsutaki, S.,
Kozawa, A., Hoshina, Y., Takenaka, S., Omiya, S., Tsunematsu, K., Tshering,
P., and Fujita, K.: The GAMDAM glacier inventory: a quality-controlled
inventory of Asian glaciers, The Cryosphere, 9, 849–864,
10.5194/tc-9-849-2015, 2015.Paul, F.: Revealing glacier flow and surge dynamics from animated satellite
image sequences: examples from the Karakoram, The Cryosphere, 9, 2201–2214,
10.5194/tc-9-2201-2015, 2015.Paul, F. and Kääb, A.: Perspectives on the production of a glacier
inventory from multispectral satellite data in Arctic Canada: Cumberland
Peninsula, Baffin Island, Ann. Glaciol., 42, 59–66,
10.3189/172756405781813087, 2005.Paul, F., Kääb, A., Maisch, M., Kellenberger, T., and Haeberli, W.:
The new remote-sensing-derived Swiss glacier inventory: I. Methods,
Ann. Glaciol., 34, 355–361, 10.3189/172756402781817941, 2002.
Paul, F., Kääb, A., and Haeberli, W.: Mapping of rock glaciers with optical
satellite imagery, in: Permafrost: Extended Abstracts Reporting Current
Research and new Information, edited by: Haeberli, W. and Brandová, D.,
International Conference on Permafrost, Zurich, Switzerland, 20.-25. July,
Glaciology and Geomorphodynamics Group, Department of Geography, University
of Zurich, 125–126, 2003.Paul, F., Huggel, C., and Kääb, A.: Combining satellite multispectral
image data and a digital elevation model for mapping debris-covered glaciers,
Remote Sens. Environ., 89, 510–518, 10.1016/j.rse.2003.11.007, 2004.Paul, F., Frey, H., and Le Bris, R.: A new glacier inventory for the European
Alps from Landsat TM scenes of 2003: Challenges and results, Ann. Glaciol.,
52, 144–152, 10.3189/172756411799096295, 2011.Paul, F., Barrand, N. E., Baumann, S., Berthier, E., Bolch, T., Casey, K.,
Frey, H., Joshi, S. P., Konovalov, V., Le Bris, R., Mölg, N., Nosenko,
G., Nuth, C., Pope, A., Racoviteanu, A., Rastner, P., Raup, B., Scharrer, K.,
Steffen, S., and Winsvold, S.: On the accuracy of glacier outlines derived
from remote-sensing data, Ann. Glaciol., 54, 171–182,
:10.3189/2013AoG63A296, 2013.Paul, F., Bolch, T., Briggs, K., Kääb, A., McMillan, M., McNabb, R.,
Nagler, T., Nuth, C., Rastner, P., Strozzi, T., and Wuite, J.: Error sources
and guidelines for quality assessment of glacier area, elevation change, and
velocity products derived from satellite data in the Glaciers_cci project,
Remote Sens. Environ., 203, 256–275, 10.1016/j.rse.2017.08.038, 2017.Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner,
A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S.,
Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B.
H., Rich, J., and Sharp, M. J.: The Randolph Glacier Inventory: A globally
complete inventory of glaciers, J. Glaciol., 60, 537–552,
10.3189/2014JoG13J176, 2014.Quincey, D. J., Glasser, N. F., Cook, S. J., and Luckman, A.: Heterogeneity
in Karakoram glacier surges, J. Geophys. Res.-Earth, 120, 1288–1300,
10.1002/2015JF003515, 2015.Racoviteanu, A. and Williams, M. W.: Decision Tree and Texture Analysis for
Mapping Debris-Covered Glaciers in the Kangchenjunga Area, Eastern Himalaya,
Remote Sensing, 4, 3078–3109, 10.3390/rs4103078, 2012.Ragettli, S., Pellicciotti, F., Immerzeel, W. W., Miles, E. S., Petersen, L.,
Heynen, M., Shea, J. M., Stumm, D., Joshi, S., and Shrestha, A.: Unraveling
the hydrology of a Himalayan catchment through integration of high resolution
in situ data and remote sensing with an advanced simulation model, Adv.
Water Resour., 78, 94–111, 10.1016/j.advwatres.2015.01.013, 2015.Ragettli, S., Bolch, T., and Pellicciotti, F.: Heterogeneous glacier thinning
patterns over the last 40 years in Langtang Himal, Nepal, The Cryosphere, 10,
2075–2097, 10.5194/tc-10-2075-2016, 2016.Rankl, M. and Braun, M.: Glacier elevation and mass changes over the central
Karakoram region estimated from TanDEM-X and SRTM/X-SAR digital elevation
models, Ann. Glaciol., 57, 273–281, 10.3189/2016AoG71A024, 2016.Rankl, M., Kienholz, C., and Braun, M.: Glacier changes in the Karakoram
region mapped by multimission satellite imagery, The Cryosphere, 8, 977–989,
10.5194/tc-8-977-2014, 2014.Raper, S. C. B. and Braithwaite, R. J.: Glacier volume response time and its
links to climate and topography based on a conceptual model of glacier
hypsometry, The Cryosphere, 3, 183–194, 10.5194/tc-3-183-2009, 2009.Rastner, P., Bolch, T., Notarnicola, C., and Paul, F.: A Comparison of Pixel-
and Object-Based Glacier Classification With Optical Satellite Images, IEEE
J. Sel. Top. Appl., 7, 853–862, 10.1109/JSTARS.2013.2274668, 2014.Raup, B. and Khalsa, S. J. S.: GLIMS Analysis Tutorial, Global Land Ice
Measurement from Space, available at: https://www.glims.org/MapsAndDocs/guides.html
(last access: 5 October 2018), 2007.RGI Consortium: Randolph
Glacier Inventory – A Dataset of Global Glacier Outlines, Version 6.0,
Technical Report, Global Land Ice Measurements from Space, Colorado, USA,
Digital Media, 10.7265/N5-RGI-60 (last access: 31 March 2018), 2017.Robson, B., Hölbling, D., Nuth, C., Stozzi, T., and Dahl, S.: Decadal
Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria)
Determined by Object-Based Image Analysis, Remote Sensing, 8, 67,
10.3390/rs8010067, 2016.Rowan, A. V., Egholm, D. L., Quincey, D. J., and Glasser, N. F.: Modelling
the feedbacks between mass balance, ice flow and debris transport to predict
the response to climate change of debris-covered glaciers in the Himalaya,
Earth Planet. Sc. Lett., 430, 427–438, 10.1016/j.epsl.2015.09.004,
2015.Sakai, A., Nuimura, T., Fujita, K., Takenaka, S., Nagai, H., and Lamsal, D.:
Climate regime of Asian glaciers revealed by GAMDAM glacier inventory, The
Cryosphere, 9, 865–880, 10.5194/tc-9-865-2015, 2015.Sarıkaya, M. A., Bishop, M. P., Shroder, J. F., and Ali, G.: Remote-sensing
assessment of glacier fluctuations in the Hindu Raj, Pakistan, Int. J. Remote
Sens., 34, 3968–3985, 10.1080/01431161.2013.770580, 2013.Satgé, F., Bonnet, M. P., Timouk, F., Calmant, S., Pillco, R., Molina,
J., Lavado-Casimiro, W., Arsen, A., Crétaux, J. F., and Garnier, J.:
Accuracy assessment of SRTM v4 and ASTER GDEM v2 over the Altiplano watershed
using ICESat/GLAS data, Int. J. Remote Sens., 36, 465–488,
10.1080/01431161.2014.999166, 2015.Scherler, D., Bookhagen, B., and Strecker, M. R.: Hillslope-glacier coupling:
The interplay of topography and glacial dynamics in High Asia, J. Geophys.
Res., 116, F02019, 10.1029/2010JF001751, 2011a.Scherler, D., Bookhagen, B., and Strecker, M. R.: Spatially variable response
of Himalayan glaciers to climate change affected by debris cover, Nat.
Geosci., 4, 156–159, 10.1038/NGEO1068, 2011b.Seong, Y. B., Owen, L. A., Yi, C., and Finkel, R. C.: Quaternary glaciation
of Muztag Ata and Kongur Shan: Evidence for glacier response to rapid climate
changes throughout the Late Glacial and Holocene in westernmost Tibet, Geol.
Soc. Am. Bull., 121, 348–365, 10.1130/B26339.1, 2009.Shangguan, D., Liu, S., Ding, Y., Ding, L., Xiong, L., Cai, D., Li, G., Lu,
A., Zhang, S., and Zhang, Y.: Monitoring the glacier changes in the Muztag
Ata and Konggur mountains, east Pamirs, based on Chinese Glacier Inventory
and recent satellite imagery, Ann. Glaciol., 43, 79–85,
10.3189/172756406781812393, 2006.Shangguan, D., Liu, S., Ding, Y., Guo, W., XU, B., Xu, J., and Jiang, Z.:
Characterizing the May 2015 Karayaylak Glacier surge in the eastern Pamir
Plateau using remote sensing, J. Glaciol., 62, 944–953,
10.1017/jog.2016.81, 2016.Shea, J. M., Immerzeel, W. W., Wagnon, P., Vincent, C., and Bajracharya, S.:
Modelling glacier change in the Everest region, Nepal Himalaya, The
Cryosphere, 9, 1105–1128, 10.5194/tc-9-1105-2015, 2015.Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R.,
Porter, C., and Morin, P.: An automated, open-source pipeline for mass
production of digital elevation models (DEMs) from very-high-resolution
commercial stereo satellite imagery, ISPRS J. Photogramm., 116, 101–117,
10.1016/j.isprsjprs.2016.03.012, 2016.
Singh, P., Ramasastri, K. S., and Kumar, N.: Topographical Influence on
Precipitation Distribution in Different Ranges of Western Himalayas, Nord.
Hydrol., 26, 259–284, 1995.Stokes, C. R., Popovnin, V., Aleynikov, A., Gurney, S. D., and Shahgedanova,
M.: Recent glacier retreat in the Caucasus Mountains, Russia, and associated
increase in supraglacial debris cover and supra-/proglacial lake development,
Ann. Glaciol., 46, 195–203, 10.3189/172756407782871468, 2007.
Tran, T. A., Raghavan, V., Masumoto, S., Vinayaraj, P., and Yonezawa, G.: A
geomorphology-based approach for digital elevation model fusion – case study
in Danang city, Vietnam, Earth Surf. Dynam., 2, 403–417,
10.5194/esurf-2-403-2014, 2014.United States Geological Survey: ASTER GDEM Version 2, available at:
https://gdex.cr.usgs.gov/gdex/, last access: 1 January 2018a.United States Geological Survey: SRTMGL30, available at:
https://gdex.cr.usgs.gov/gdex/, last access: 1 January 2018b.
Vaughan, D. G., Comiso, J. C., Allison, I., Carrasco, J., Kaser, G., Kwok,
R., Mote, P., Murray, T., Paul, F., Ren, J., Rignot, E., Solomina, O.,
Steffen, K., and Zhang, T.: Observations: Cryosphere, in: Climate Change
2013: Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
Wake, C. P.: Glaciochemical investigations as a tool for determining the
spatial and seasonal variation of snow accumulation in the central Karakoram,
northern Pakistan, Ann. Glaciol., 13, 279–284, 1989.Wendt, A., Mayer, C., Lambrecht, A., and Floricioiu, D.: A Glacier Surge of
Bivachny Glacier, Pamir Mountains, Observed by a Time Series of
High-Resolution Digital Elevation Models and Glacier Velocities, Remote
Sensing, 9, 388, 10.3390/rs9040388, 2017.Winiger, M., Gumpert, M., and Yamout, H.: Karakorum-Hindukush-western
Himalaya: Assessing high-altitude water resources, Hydrol. Process., 19,
2329–2338, 10.1002/hyp.5887, 2005.Zech, R., Abramowski, U., Glaser, B., Sosin, P., Kubik, P. W., and Zech, W.:
Late Quaternary glacial and climate history of the Pamir Mountains derived
from cosmogenic 10Be exposure ages, Quaternary Res., 64, 212–220,
10.1016/j.yqres.2005.06.002, 2005.