The original Glacier Area Mapping for Discharge from the Asian
Mountains (GAMDAM) glacier inventory was the first methodologically
consistent dataset for high-mountain Asia. Nonetheless, the GAMDAM inventory
underestimated glacier area, as it did not include steep ice- and
snow-covered slopes or shaded components. During revision of the inventory,
Landsat imagery free of shadow, cloud, and seasonal snow cover was selected
for the period 1990–2010, after which >90 % of the glacier
area was delineated. The updated GAMDAM inventory, comprised of 453 Landsat
images, includes 134 770 glaciers with a total area of 100693±11790 km2.
Introduction
Glaciers in high-mountain Asia (HMA) play a significant role as a water
resource for people living downstream (Immerzeel et al., 2010; Bolch et al.,
2012). Glacier recession in recent decades has contributed to sea level
rise, and this trend is anticipated to continue in the future (Huss and
Hock, 2015; Marzeion et al., 2018; Radić and Hock, 2013). Recent
analysis of surface elevation change has revealed that glaciers in HMA
exhibit contrasting behaviour (Brun et al., 2017; Gardner et al., 2013;
Kääb et al., 2012, 2015): those in the Himalaya and the eastern
Nyainqêntanglha Mountains are shrinking rapidly, while the Karakoram and West
Kunlun glaciers are in balance or show a slight mass gain. Accordingly, a
recent climate analysis for those areas demonstrated that the Karakoram and
West Kunlun regions are relatively stable under global warming conditions,
being less sensitive to temperature change (Sakai and Fujita, 2017). This
assessment of both glacier volume and climatic conditions is based on a
large-scale glacier inventory, highlighting the need for accurate,
high-quality coverage of the entire HMA region. Specifically, precise
glacier inventories are needed for modelling total glacier volume (Frey et
al., 2014; Farinotti et al., 2019), deriving volume change from altimetry
and digital elevation maps (DEMs, e.g. Brun et al., 2017) and surface-flow velocity (Dehecq et al.,
2019), establishing changes in snow cover and albedo (Naegeli et al., 2019),
catchment- and regional-scale hydrologic modelling (e.g. Immerzeel et al.,
2010), projecting future glacier configuration (Huss and Hock, 2015; Shannon
et al., 2019), and assessing uncertainty in estimates of glacier-surface
elevation change (e.g. Nuimura et al., 2012; Bolch et al., 2017).
While the Randolph Glacier Inventory (RGI) (Arendt et al., 2015; RGI
Consortium, 2017) was the first database with global coverage, the record
exhibits considerable variability in accuracy even within HMA. Regional
databases include the second Chinese glacier inventory (hereafter the CGI2),
produced by automatic delineation with manual correction (Guo et al., 2015),
and the NM18 inventory for the Karakoram and Pamir region (Mölg et al.,
2018), derived from automated digital mapping and corrected manually by the
coherence of synthetic aperture radar (SAR) imagery for debris-covered
glaciers (Frey et al., 2012). The latter study also made separate
delineations for all debris-covered areas.
Between February 2011 and March 2014, the Glacier Area Mapping for Discharge
from the Asian Mountains (GAMDAM) project compiled a glacier inventory for
HMA, covering the region between 27.0 and 54.9∘ N in latitude and
67.4 and 103.9∘ E in longitude. In its first iteration,
published in 2015, the GAMDAM glacier inventory (GGI) did not include steep
ice- and snow-covered slopes. Moreover, where wintertime imagery was
employed to avoid summer monsoon cloud cover, shaded areas of glacier
surfaces were excluded from the inventory (Fig. S1a in the Supplement). To help address these
shortcomings, I present a revised glacier inventory for HMA based on
summertime (May–September) imagery, exhibiting clear glacier boundaries for
steep, snow-covered slopes and shaded areas. The abbreviated terms GGI15 and
GGI18 refer to the first version of the GGI (Nuimura et al., 2015) and the
current, updated version (this study), respectively.
Data
I utilized a total of 453 Landsat 5 Thematic Mapper™ and
Landsat 7 Enhanced Thematic Mapper Plus (ETM+) level 1T scenes derived from 196
USGS EarthExplorer path–row sets (http://earthexplorer.usgs.gov/, last access: 17 July 2019). Landsat
ID and acquisition dates were used to delineate glacier outlines and are
summarized in Table S1 in the Supplement. Due to the challenge of obtaining summertime imagery
for the 1999–2003 setting period (Nuimura et al., 2015) that is free of
clouds, seasonal snow cover, and shadows, the annual search range was
expanded to 1990–2010 and the monthly search range to May–September (i.e.
the high-solar-angle season). Where part of a glacier surface was obscured
by cloud or snow, the Landsat archive was searched for more viable images
covering that particular site; for glaciers with steep headwalls, images
were selected with the most clearly defined glacier outlines (full details
of this methodological approach are given in Sect. 3). As a result, the
GGI18, like its predecessor, contains single path–row scenes comprised of
multiple images (Fig. 1). Finally, the GGI18 employs the ASTER-GDEM2 to
analyse the glacier aspect in each 90 m×90 m grid.
Footprints of the Landsat scenes used in the GGI18. Colours
indicate the number of scenes used to delineate glacier outlines.
Methods
Unlike seasonal snow cover, glaciers are considered to be permanent snow and
ice. It is vital, therefore, that seasonal snow coverage is excluded from
each glacier polygon. In addition, to help quantify the glacial contribution
to sea level change and water resources, polygons must include all areas in
which fluctuations in surface elevation reflect changes in ice mass.
Selection of Landsat imagery
As detailed in Sect. 2, I expanded the search period to obtain Landsat
images in which glacier outlines are depicted clearly. Figure S1, for
example, shows the five images selected to delineate glacier outlines in the
accumulation zone of the Khumbu Glacier, in the Nepalese Himalaya. While the
cloud-free image in Fig. S1a was utilized for the GGI15, large areas of
the glacier surface lie in shadow, thus precluding accurate delineation.
Therefore, during revision for the GGI18, I selected an additional two
images (Fig. S1b and c) with minimal snow and no cloud cover over the target
glaciers (Fig. S1). Focusing on the steep
snow-covered headwalls of the Khumbu Glacier (purple ellipses in right
panels, Fig. S1), the image displayed in Fig. S1b exhibits the least
seasonal snow cover and provides the sharpest boundaries among the four
additional images, and thus this was utilized in the GGI18.
Ultimately, the degrees of cloud and snow cover and the clarity of glacier
outlines are the key factors in selecting suitable Landsat imagery for
glacier delineation. The most challenging sites are those for which the
glacier headwall comprises at least part of the accumulation area; to
delineate such glaciers accurately, I focused on unambiguous boundaries on
north-facing walls. Nonetheless, in regions dominated by summer monsoonal
precipitation, such as the eastern Himalaya and eastern Nyainqêntanglha
Mountains, the approach described here was inadequate to locate appropriate
imagery (see Sect. 4.3).
Manual delineation
Owing to the many debris-covered glaciers in HMA (e.g. Herreid et al.,
2015; Minora et al., 2016; Nagai et al., 2016; Ojha et al., 2017), for which
automatic detection using the band ratio method is not possible (Paul et
al., 2002), all glacier outlines included in the GGI18 were delineated
manually. Using the newly selected Landsat imagery, I modified the GGI15
glacier polygons following the method described by Nuimura et al. (2015)
but with two important differences. First, whereas glaciers of <0.05 km2 in area were excluded from the GGI15 (Nuimura et al., 2015),
the minimum glacier area in the GGI18 is 0.01 km2 so as to account for
the numerous small glaciers separated by dividing ridges. Furthermore, I
included small glaciers as much as possible during the revision process. A total of 10
grid cells (=0.009 km2) were used as a guide for measuring area. In
contrast to the GGI15, in which glacier outlines were delineated manually by
11 individuals (Nuimura et al., 2015), all of the delineation for the GGI18
was conducted by a single person.
The second methodological difference between the GGI15 and the GGI18 relates
to steep headwalls. Nuimura et al. (2015) excluded steep snow- and
ice-covered slopes from the GGI15, arguing that glaciers on high-angle
headwalls generally do not undergo changes in surface elevation related to
mass fluctuations. Those authors also underestimated the scale of upper
glacier headwalls that are mantled with snow or ice. In contrast, since I
was able to obtain comparatively distinct outlines for those glaciers with
relatively thick ice on steep headwalls, the GGI18 includes the snow- or
ice-covered parts of the glacier surface. For instance, Fig. S2a depicts
the high-angle, avalanche-prone headwall of the Trakarding Glacier in 2016,
on which hanging glaciers are clearly visible. Thanks to their distinct
outlines, these features are also identifiable on the 1999 Landsat image
(arrows, Fig. S2b), indicating that they are long-term components of the
glacier system and thus need to be included in the inventory.
The correct distinction between debris-covered glaciers and rock
glaciers is a challenge, as gradual transitions can exist under permafrost conditions
(Mölg et al., 2018). Rock glaciers have terrain with ridges and furrow
surface patterns (Bodin et al., 2010), while debris-covered glaciers have
ponds surrounded by ice cliffs. Those detailed topographies were difficult to
detect via Landsat imagery because of its relatively low resolution.
Therefore, debris-covered areas were determined from high-resolution Google
Earth imagery. Specifically, those portions of the glacier surface
exhibiting rock-glacier-like topography (e.g. flow lobes) were identified
visually and omitted (see Fig. S3). As for the debris-covered glaciers in
the eastern Himalaya and eastern Nyainqêntanglha Mountains, crevassed surfaces
can be detected even in the snow-covered glacier surface using
high-resolution Google Earth imagery. For regions where high-resolution
Google Earth imagery is unavailable (e.g. eastern Himalaya and eastern
Nyainqêntanglha Mountains) or the glacier surface is obscured by seasonal
snow cover (e.g. Karakoram and Pamir), I employed a combination of contours
and surface–colour difference between glacier areas and glacier-free areas to
delineate debris-covered glaciers.
Uncertainties in glacier area
Revision of glacier outlines and subsequent delineation testing were both
performed by the author. Delineation tests were conducted on 10
debris-covered glaciers and 12 debris-free glaciers using a total of 10
Landsat images (listed in Table S4), which included shaded (winter),
snow-covered, and partially cloud-covered scenes. Since fully cloud-obscured
images were not used in the delineation process, I did not select such
glacier outlines in the testing process. Furthermore, I did not utilize
Google Earth imagery since the resolution is not regionally uniform
throughout HMA (see Sect. 3.2). For each Landsat image, I created a single
glacier outline and calculated the normalized standard deviation (NSD:
standard deviation divided by average glacier area) for each glacier area
(e.g. Fig. S4). For each area class, the NSD increases with decreasing
glacier area (Fig. S5). Moreover, NSD values are higher for debris-covered
glaciers than for debris-free glaciers (particularly for smaller glaciers),
although the GGI18 does not classify debris-covered and debris-free
glaciers.
The proportion of debris-covered glaciers in each area class in the eastern
Himalaya (27.5–29.0∘ N, 85.0–92.0∘ E) (Ojha et al., 2017) (Fig. S6) was applied for all of the study
areas (HMA), then they were used to calculate the number-weighted average NSD
of glacier area for each glacier area class, including both debris and
debris-free glaciers (Fig. S6). Here, the NSDs of the glacier area were
assumed to be 15 % for smaller (<0.25 km2) debris-free
glaciers and 30 % for smaller (<2 km2) debris-covered
glaciers based on Fig. S5. NSD for all glaciers in Fig. S6 was assumed to be
the uncertainty in glacier area for all types of glacier (including
debris-covered and debris-free). Finally, the maximum NSD 19 % was found
for glaciers of 1–2 km2 in area (Fig. S6).
Results and discussion
The GGI15 reported a total glacier area of 91263±13689 km2
(Nuimura et al., 2015), which included the combined area of holes in glacier
polygons. Excluding polygon holes, I recalculated the total glacier area in
the GGI15 as 87583±3137 km2 (Table 1), while the GGI18 is comprised of
134 770 glaciers with a total area of 100693±11790 km2 (Table 1). Hence, the total glacier area and glacier number for HMA are 13 % and
35 % greater in the GGI18 than in the GGI15, respectively.
Comparison of the GGI15 and GGI18 inventories in terms of the total
glacier area, glacier area ratio based on summer imagery, and based on
imagery acquired between 1999 and 2001.
Minimum glacierTotal glacierTotal numberNumber ofGlacier areaGlacier area basedarea (km2)area (km2)of glaciersLandsat imagesbased on summeron images acquiredemployed(JJAS) images (%)from 1999 to 2001 (%)GGI15 (Nuimura et al., 2015)0.0587583±1313787 0843566973GGI18 (this study)0.01100693±11790134 7704539548Comparison with the GGI15
Following the region delimitation of RGI 6.0 (Arendt et al., 2015; RGI
Consortium, 2017), the aggregated polygon files for the GGI18 are divided
into four regions: Central Asia, South Asia (east), South Asia (west), and North
Asia (limited by the Sayan and Altai mountains). Regional differences in
glacier area among the GGI18, GGI15, and RGI 6.0 are summarized in Table S2
(note that the RGI 6.0 incorporated part of the GGI15; RGI Consortium, 2017). For all regions, glacier area in the GGI18 is >10 %
greater than in the GGI15, with the greatest differences in eastern South Asia
(+18 %) and western South Asia (+16 %). Both eastern and western South Asia
cover portions of the high Himalaya, including abundant high-relief
glaciated headwalls, indicating that the GGI15 underestimated glacier area
most in shaded areas. In the present study, I replaced glacier outlines
delineated from winter imagery (GGI15) with those based on summer imagery
(GGI18), with the result that glacier area ratios based on summer images
increased from 69 % to 95 % (Table 1). Figure 2 provides a comparison of
a glacier outline included in both the GGI15 and GGI18 inventories. In the
former, glacier delineation was based on low-solar-angle, heavily shaded
imagery; in the latter, such areas have been substituted with delineations
based on high-solar-angle imagery (Fig. S7).
Comparison of glacier outlines used in the GGI15, GGI18, and NM18
inventories at 38.9236∘ N, 72.4217∘ E (path 151, row 33
of WRS2). Backgrounds are false-colour (bands 7, 4, and 2 as RGB) composite
Landsat images taken on 28 September 2001 (a) and 26 July 2001 (b). Glacier
outlines of the GGI15 (yellow lines) were delineated based on the strongly
shaded image on the left, whereas those of the GGI18 (white lines) were
delineated using the less-shaded image on the right. Glacier outlines of the
NM18 (pink lines; Mölg et al., 2018) are also shown for comparison.
Total glacier area in the GGI18 includes components on north-facing slopes
(Fig. S8). However, the acquisition dates of the imagery are variable. For
instance, the glacier area ratio derived from images acquired between 1999
and 2001 decreased from 73 % in the GGI15 to 48 % in the GGI18 (Table 1). For both inventories, glacier area distributions for specific
acquisition dates (month and year) are compared and summarized in Fig. S9.
Glaciers located in monsoon-dominated regions were delineated primarily from
non-summer (January–May and October–December) imagery in the GGI15 (Fig. S9a and b), whereas the majority of the total glacier area (>90 %: Table 1) was extracted from summer (June–September) Landsat imagery
(Fig. S9c).
According to the area–elevation distributions shown in Fig. S10a, total
glacier area between 5000 and 6000 m elevation is greater in the GGI18 than
in the GGI15. While glacier area in the GGI18 is measurably larger across
all area classes (Fig. S10c), the greatest increase in glacier number is
observed for small (<0.0625 km2) glaciers (Fig. S10b). Glacier
polygons were aggregated for each 1∘×1∘ grid
based on the barycentre of each glacier polygon for each inventory to
assess regional differences (see Fig. S10d). Compared with the GGI15, the
GGI18 exhibits higher glacier-area values in all regions except the Tibetan
Plateau (Fig. S10d), where the general absence of high-relief terrain
minimizes the magnitude of topographic shading.
Comparison with the CGI2 and NM18 inventories
To assess the GGI18 relative to the CGI2 (Guo et al., 2015) and NM18
(Mölg et al., 2018) inventories, I extracted the two components of the
GGI18 covered by the respective domains of the other datasets. A direct
comparison of the three reveals that the GGI18-derived glacier area is
smaller than that of the CG12 for elevations of 4000–5500 m (Fig. S11a) and
lower than that of the NM18-derived estimate for elevations of 4500–6000 m
(Fig. S12a). In contrast, the GGI18 reports a greater number of smaller
glaciers than the CG12 and the NM18, and larger glaciers comprise a
smaller total area in the GGI18 (Figs. S11b, c and S12b, c). This pattern is
likely due to the greater division in the GGI18 of large ice masses into
multiple glaciers relative to the NM18 and CGI2.
For each 1∘×1∘ grid cell, glacier polygons
for all three inventories were aggregated based on the polygon barycentre,
thereby enabling regional differences to be calculated (Figs. S11d and S12d).
According to this comparison, glacier areas provided by the GGI18 and CG12
are regionally consistent (Fig. S11d), with the exception of the
Nyainqêntanglha Mountains, for which the CGI2 was not updated following the
first Chinese glacier inventory. In contrast, compared to the NM18, the
GGI18 prescribes a slightly smaller glacier area for most regions (Fig. S12d). This disparity is potentially linked to the inclusion of seasonal
snow in the NM18, due to the automatic band-ratio method employed over
debris-free zones (Mölg et al., 2018), whereas the GGI18 tends to omit
such small glaciers. Finally, I evaluated the degree of consistency between
the GGI18 and the other two inventories using an overlapping ratio. This
assessment provided an overlapping ratio of 87 % for the GGI18 and NM18,
and a ratio of 86 % for the GGI18 and CGI2 to the total GGI18 over their
respective domains (NM18/CGI2) (Table S3), indicating a high degree of
consistency among the three inventories.
Glacier outlines requiring further revision
Clouds, seasonal snow cover, and strong shadows all hamper the detection of
glacier outlines from Landsat imagery. Consequently, the number of scenes
required to delineate glacier outlines for each path–row varies widely (Fig. 1), with monsoon-dominated regions utilizing the most imagery. Example of
glacier outlines within such a limited area delineated using multiple images
were shown in Fig. S13. Therefore, the number of images in Fig. 1
represents the degree of delineation accuracy.
As satellite imagery that is cloud-free and has the least seasonal snow becomes available
from existing sources other than Landsat in the future, the glacier
outlines delineated here from multiple images need to be revisited and, if
necessary, revised. Sentinel-2 imagery, for instance, might prove a suitable
alternative owing to its high resolution and shorter acquisition interval
(≤5 d) relative to Landsat.
Summary
The updated version of the GAMDAM glacier inventory, the GGI18, incorporates
all of HMA and includes 134 770 glaciers covering 100693±11790 km2. Although nearly 95 % of the total glacier area was delineated
from summer images, the acquisition date of source imagery varies widely.
Relative to its predecessor (GGI15), the total glaciated area in HMA is
∼15 % greater in the GGI18, due primarily to the inclusion
of glaciated north-facing slopes. Owing to cloud, seasonal snow cover, and
topographic shading, a number of path–row scenes required multiple Landsat
images to delineate glacier outlines fully and thus should be revisited in
the future as higher-quality imagery becomes available.
Data availability
Data can be downloaded from the following sources.
GAMDAM glacier inventory for high-mountain Asia:
Area–altitude distribution for Central Asia,
10.1594/PANGAEA.891415 (Sakai, 2018a).
GAMDAM glacier inventory for high-mountain Asia:
Area–altitude distribution for North Asia,
10.1594/PANGAEA.891416 (Sakai, 2018b).
GAMDAM glacier inventory for high-mountain Asia:
Area–altitude distribution for South Asia East,
10.1594/PANGAEA.891417 (Sakai, 2018c).
GAMDAM glacier inventory for high-mountain Asia:
Area–altitude distribution for South Asia West,
10.1594/PANGAEA.891418 (Sakai, 2018d).
GAMDAM glacier inventory for high-mountain Asia:
Central Asia in ArcGIS (shapefile) format,
10.1594/PANGAEA.891419 (Sakai, 2018e).
GAMDAM glacier inventory for high-mountain Asia: North
Asia in ArcGIS (shapefile) format,
10.1594/PANGAEA.891420 (Sakai, 2018f).
GAMDAM glacier inventory for high-mountain Asia: South
Asia East in ArcGIS (shapefile) format,
10.1594/PANGAEA.891421 (Sakai, 2018g).
GAMDAM glacier inventory for high-mountain Asia: South
Asia West in ArcGIS (shapefile) format,
10.1594/PANGAEA.891422 (Sakai, 2018h).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-13-2043-2019-supplement.
Competing interests
The author declares that there is no conflict of
interest.
Acknowledgements
This project was supported by a grant from the Grants-in-Aid
for Scientific Research (26257202) of the Japan Society for the Promotion of
Science. I wish to thank all members of the GAMDAM project for their
valuable support in producing the first version of the GAMDAM glacier
inventory.
Financial support
This research has been supported by the Funding Program for Next
Generation World-Leading Researchers (grant no. GR052).
Review statement
This paper was edited by Tobias Bolch and reviewed by Frank Paul and Wanqin Guo.
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