Multi-temporal inventories of glacierised regions provide an
improved understanding of water resource availability. In this study, we
present a Landsat-based multi-temporal inventory of glaciers in four Upper
Indus sub-basins and three internal drainage basins in the Ladakh region for
the years 1977, 1994, 2009 and 2019. The study records data on 2257 glaciers
(of individual size
The Himalaya is the largest storehouse of snow and ice outside the Polar
Regions. This large reserve of water plays a crucial role in the
hydro-economy of the region
(Bolch,
2019; Frey et al., 2014; Maurer et al., 2019; Pritchard, 2019). Any change
to the Himalayan cryosphere would have a direct impact on the hydrology,
further influencing the communities downstream whose livelihood and economy
relies on, and are supported by, the major river systems e.g. the
Brahmaputra, Ganges and Indus, among others. In high altitude arid regions
like Ladakh, where the majority of glaciers are small and restricted to
higher altitudes, meltwater serves as an important driver of the economy,
especially in years with low winter precipitation when glacier melt becomes
the major (or only) source of water
(Schmidt and Nüsser, 2012,
2017). Recent studies have reported that Himalayan glaciers are retreating
at an alarming rate
(Azam
et al., 2021; Bolch, 2019; Kääb et al., 2015; Maurer et al., 2019;
Pritchard, 2019; Shean et al., 2020, among others) with glaciers of the
Western Himalayas showing less shrinkage than the glaciers of the central
and eastern parts
(Azam
et al., 2021; Shukla et al., 2020; Singh et al., 2016). Glaciers in the
nearby Karakoram region display long-term irregular behaviour with frequent
glacier advances/surges and minimal shrinkage, which is yet to be fully
understood
(Azam
et al., 2021; Bhambri et al., 2013; Bolch et al., 2012; Kulkarni, 2010; Liu
et al., 2006; Minora et al., 2013; Negi et al., 2021). Glaciers of the
Karakoram region experienced an increase in area post-2000, due to
surge-type glaciers. In just the upper Shayok Valley, as many as 18
glaciers, occupying more than one-third of the glacierised area, showed
surge-type behaviour
(Bhambri
et al., 2011, 2013; Negi et al., 2021). However, not all regions of Ladakh
have been analysed at the same level of spatio-temporal detail. In
particular, our knowledge of glacier dynamics and their response to climate
change is still incomplete in the cold–arid, high-altitude Ladakh region
(
The advent of remote sensing technologies has permitted the mapping and measuring of various glacier attributes even in the absence of sufficient in situ observations (Bhardwaj et al., 2015). Glacierised area estimations have often relied on global and regional glacier inventories such as the Randolph Glacier Inventory (RGI), Global Land Ice Measurements from Space (GLIMS), Geological Survey of India (GSI) inventory and Space Application Centre India (SAC) inventory, among others (Chinese Glacier Inventory (CGI), Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM), International Centre for Integrated Mountain Development (ICIMOD)). However, given the large scale of these inventories, automated techniques are employed, in most of the cases, to map and calculate glacier extent with differing levels of success. Additionally, the varying quality of satellite imagery acquired from different time periods are sometimes necessitated in high mountain areas, such as Ladakh. Together, these two factors can lead to over- or under-estimation of glacier areas leading to erroneous information on temporal change. Moreover, there is no multi-temporal glacier inventory available for the entire Ladakh region, which can inform us on the changes in the natural frozen water reserves which have put the water security of this entire cold–arid region under significant stress during recent years. The residents of Ladakh have witnessed a decrease in agricultural yields, the main driver of economic development of the region, due to a decrease in water resources (Barrett and Bosak, 2018). The water scarcity together with an increase in tourism footprint (four times more tourists (327 366) in 2018 than 2010, a number that is more than the entire population of Ladakh) has led to a shift in livelihood from agriculture to other commercial activities (Müller et al., 2020), though even the latter relies heavily on water resources. In order to cope with water scarcity, some people of Ladakh have developed new water management techniques, commonly known as “ice reservoirs” or “ice stupas”, to supplement agricultural activities (Nüsser et al., 2019a, b).
This study presents a new multi-temporal glacier inventory for the Union
Territory of Ladakh, India, covering 42 years of change between 1977 and
2019. This new dataset and analyses of glacier distribution will help to
improve understanding of the glacier dynamics and the impact of ongoing
climate change on water resources in the Ladakh region, where glaciers are
the only source of water in the dry season. The inventories are entirely
based on Landsat images acquired mostly during late summer with additional
quality control provided through high-resolution PlanetScope and Google
Earth imagery. We further establish a comparison with the existing
inventories and data available in recent studies from the region. The
dataset produced in this study can be viewed and downloaded from
PANGAEA,
This study focuses on glaciers in the Upper Indus Basin (UIB) upstream of
Skardu and three internal drainage/endorheic basins (IDBs) within Ladakh,
namely Tsokar, Tsomoriri and Pangong basins. The geographic extent of the
study area lies within a latitude of 31.1 to 35.6
Location map of the study area: the boundaries of studied Upper
Indus Basin and internal drainage basins are outlined in black and red on
the digital elevation model (DEM) and in the inset map. Inset map shows the
study area with respect to the Himalayan and Karakoram region. Black dots
and stars represent the respective basins' major settlements and field-investigated glaciers. The background image (ASTER GDEM) courtesy:
NASA/METI/AIST/Japan Space Systems and U.S./Japan ASTER Science Team,
The Ladakh region has a cold–arid climate due to the rain shadow and
elevation effects of the Himalaya and Karakoram mountains
(Schmidt and Nüsser, 2017). Mean annual air
temperature and annual precipitation range between 0 to 10
Mean annual
This study utilises multiple Landsat level-1 precision and terrain (L1TP)
corrected scenes (63 scenes in total) from four different periods:
Information on the satellite imagery used in this study (detailed information in Table S1).
Basin delineation was carried using ASTER GDEM V003 and the hydrology tool in ArcGIS. The input DEM was first analysed to fill in all sinks with careful consideration of the potential for basin area over-estimation (Khan et al., 2014). UIB was delineated using a pour point selected at the Indus River in Skardu as we aimed to assess all the tributary basins of the Ladakh region. The UIB obtained by this approach was further divided into second-order tributary basins, i.e. Shayok, Suru, Zanskar and Leh basins. A small portion of the leftover area from UIB after second-order tributary basin delineation was merged into the Leh Basin in order to investigate the UIB upstream of Skardu. Delineation of the three endorheic basins (IDBs) that lie partially or completely in the Ladakh region, i.e. Tsokar, Tsomoriri and Pangong basins, was also carried out using the same method with the help of respective lakes as a pour point. The digitisation of the three lakes (Tsokar, Tsomoriri and Pangong lakes) was carried out manually for the years 1977, 1994, 2009 and 2019 using Landsat imagery.
Glaciers were mapped using a two-way approach, closely following the Global
Land Ice Measurements from Space (GLIMS) guidelines
(Paul et al., 2009): (1) automatic mapping of the clean glacier and (2) manually correcting the
glacier outlines and digitisation of debris cover. First, a band ratio
approach between NIR (near infrared) and SWIR (shortwave infrared)
(as
suggested by Paul et al., 2002, 2015; Racoviteanu et al., 2009; Bhardwaj et
al., 2015; Schmidt and Nüsser, 2017; Smith et al., 2015; Winsvold et al.,
2014, 2016) with a threshold of 2.0 (NIR/SWIR
The glacier outlines from 2019 were used as a starting point for the
subsequent digitisation of glacier areas in 2009, 1994 and 1977. Glacier
length was measured using a semi-automatic approach, by employing the DEM to
identify a central flow line for each mapped glacier
(Ji et al., 2017; Le Bris and
Paul, 2013). Further manual corrections were undertaken to account for the
flow lines of glaciers that have multiple tributaries and multiple
highest/lowest points. Furthermore, some mapping errors are still expected
to be present in this inventory due to a possible misinterpretation of
glacier features, and the quantification of such errors are difficult owing
to the lack of reliable reference in situ data in the Ladakh region. Such
errors were minimised by keeping a fixed map–scale of
Other specific glacier attributes were also extracted including new glacier IDs, Global Land Ice Measurements from Space (GLIMS)-0IDs, Randolph Glacier Inventory (RGI 6.0)–IDs, coordinates (latitude and longitude), elevation (maximum, mean and minimum), aspect (mean), slope (mean), area, length (maximum), area uncertainty and length uncertainty.
This study involves the use of satellite imagery to extract various glacier parameters. It is therefore subject to uncertainties which may arise mainly from four different sources: (1) the quality of the image (with potential issues due to seasonal snow, shadows and cloud cover), (2) sensor characteristics (spatial/spectral resolution), (3) interpretation of glacial features and methodology used and (4) post-processing techniques (Le Bris and Paul, 2013; Paul et al., 2013, 2017; Racoviteanu et al., 2009, 2019). Error due to sources 1, 3 and 4 are generally minor and can be visually identified and corrected (Sect. 3.3), but an exact quantification is difficult due to the lack of reference data available from the region (Racoviteanu et al., 2009; Shukla et al., 2020). Type 4 errors are significant and have an impact on both glacier area and length estimation. Therefore, we applied a buffer-based assessment to glacier areas with the buffer width set to one pixel for debris-covered and a half pixel for clean ice (Bolch et al., 2010; Granshaw and Fountain, 2006; Mölg et al., 2018; Paul et al., 2017; Racoviteanu et al., 2009; Shukla et al., 2020; Tielidze and Wheate, 2018), given that the level 1TP Landsat images were corrected to sub-pixel geometric accuracy (Bhambri et al., 2013). A buffer-based method provides the maximum and minimum estimates of uncertainty with respect to glacier size, where the values vary with size of the glacier and spatial resolution of the imagery used. Thus, it is more specific to the dataset and most recommended when there are no reliable reference data available (Paul et al., 2017; Racoviteanu et al., 2009; Shukla et al., 2020). The same approach was also followed to estimate the uncertainties in lake areas with one pixel as the buffer width.
The associated uncertainty for smaller glaciers (
Uncertainties related to other attributes (mean elevation, mean slope and
mean aspect) of the inventory are difficult to estimate due to the use of
the ASTER GDEM product in this study, which was developed using a collage of
archived scenes acquired between 2000 and 2013. In addition, the local
undulations and surface change over time will have only marginal effects on
parameters (elevation, slope and aspect) that are averaged over the entire
glacier as averaging compensates for most of the changes
(Frey and Paul, 2012). However, for parameters like maximum
and minimum elevations, where one cell is used and no averaging is applied,
the uncertainty is
In total, 2257 glaciers (
Glacierised areas and population in the Ladakh region vary across basins.
Shayok Basin has the largest distribution of glacierised area and population
(74 % and 56 %), whereas the Tsokar Basin has the least (0.04 % and
0.1 %), respectively (Table 2). Based on size distribution, the glacier
area category of 1–5 km
Figure 3iii and iv show the glacier elevations and hypsometry with 100 m
elevation intervals of seven basins of the Ladakh region. The highest and
lowest glacier elevation are 7740 and 3249 m a.s.l., both in the Shayok
Basin. Whereas mean elevation of the glacier ranges between 4345–6355 m a.s.l. (Fig. 3iii). Small glaciers mainly occupy the higher elevations
above 5500, and vice versa. The majority (73 %, 5810 km
Basin-wide glacier information of Ladakh region based on present study for the year 2019.
General statistics of the glaciers in the Ladakh region:
orientation of glaciers
The multitemporal inventory of glaciers (
When using this dataset it is important to understand the key limitations of
such regional-scale glacier inventories. Some of the key user limitations of
the dataset are: (1) glaciers smaller than 0.5 km
Basin and class-wise comparison of the glacierised area between the present study and other inventories (RGI 6.0, ICIMOD and GAMDAM).
The glacier inventory presented here has several improvements compared to
the existing regional and global inventories. Firstly, it covers the
glaciers (
Differences in estimates of the glacierised areas are meaningful as they can
lead to an over or underestimation of the available water resources.
Therefore, correctly estimating glacier area over time is necessary for
understanding glacier dynamics, future response to climate forcing and the
water resources they provide. Table 3 presents a comparison between the
present inventory and the Randolph Glacier Inventory (RGI) 6.0
(Pfeffer et al., 2014), the International
Centre for Integrated Mountain Development (ICIMOD) inventory
(Bajracharya et al., 2011, 2019; Williams, 2013)
and the Glacier Area Mapping for Discharge in Asian Mountains (GAMDAM)
inventory
(Guo
et al., 2015; Nuimura et al., 2015; Sakai, 2019) for the Ladakh region. The
comparison involves glacier outlines for 2009 from the present study and
excludes glaciers smaller than 0.5 km
The comparison showed a higher glacierised area in the RGI/GAMDAM
inventories and lower in the ICIMOD inventory (Table 3) than the present
inventory, with most of the differences contributed by the basins having the
higher glacierised areas (Shayok and Zanskar) and from the larger glaciers
(
Comparison of inventories on the field-investigated glaciers of
the Ladakh region:
The data from the recent spatio-temporal change studies from different
sub-regions of Ladakh (Fig. 5) are not in the public domain, except from
Shukla et al. (2020). Hence, it is not possible to use these to validate our
results. Therefore, our comparison mostly focuses on the rate of change for
some of the individual glaciers (
No significant difference was observed in rate of change of glacierised areas between the present study and other studies in the Leh, Tsomoriri, Zaskar and Suru basins. In contrast, the number of glaciers and glacierised area vary among these studies (present and others) but paint a similar picture of relatively lower retreat in the Shayok Basin (Bhambri et al., 2013; Negi et al., 2021), higher in Leh, Tsokar, Tsomoriri (Chudley et al., 2017; Schmidt and Nüsser, 2012, 2017), and moderate in Zanskar and Suru basins (Garg et al., 2022a, b; Shukla et al., 2020).
Presenting the spatial extent of different studies undertaken in Ladakh region. Black stars represent the individual glaciers. The background image (ASTER GDEM) courtesy: NASA/METI/AIST/Japan Space Systems and U.S./Japan ASTER Science Team,
Comparison between the present study and other studies undertaken in different basins of Ladakh region over different time periods.
The entire dataset of the Landsat-based multitemporal inventory of glaciers,
larger than 0.5 km
We compiled a new glacier inventory of the Ladakh region for 1977, 1994,
2009 and 2019 based on 63 Landsat (MSS, TM and OLI) images, with least
cloud/snow cover, acquired during the summertime (July–October). The
inventory includes 2257 glaciers, larger than 0.5 km
The new multi-temporal inventory presented here will assist in planning the management of water resources, and for guiding scientific research focusing on glacier mass balance, hydrology and glacier change within the region. The detailed information and multi-temporal nature of this inventory will also aid in improving the existing global and regional glacier inventories especially in the cold–arid Ladakh region where the majority of the population is highly dependent on glacier-derived melt water resources for domestic, irrigation and hydropower generation needs.
The supplement related to this article is available online at:
MoS, AR and AB conceptualised and designed the study. MoS, AB and MC did the analysis. MoS wrote and AR, AB, MaS, BRR, SS and LS edited the article. All the authors have equally contributed to interpretation of the results.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors are thankful to the School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India, for the lab facilities and the United States Geological Survey for the Landsat and ASTER imageries. The authors also thank Planet Labs and Google for the high resolution PlanetScope and Google Earth imageries. We are also thankful to the Scottish Funding Council and the University Of Aberdeen, United Kingdom for financially supporting our work.
This paper was edited by Kenneth Mankoff and reviewed by Marcus Nüsser and Pratima Pandey.