Front. Earth Sci. Frontiers in Earth Science Front. Earth Sci. 2296-6463 Frontiers Media S.A. 748107 10.3389/feart.2021.748107 Earth Science Original Research Spatiotemporal Dynamics and Geodetic Mass Changes of Glaciers With Varying Debris Cover in the Pangong Region of Trans-Himalayan Ladakh, India Between 1990 and 2019 Majeed et al. Glacier Changes in Pangong Region Majeed Ulfat 1 Rashid Irfan 1 * Najar Nadeem Ahmad 1 Gul Nafeeza 2 1 Department of Geoinformatics, University of Kashmir, Srinagar, India 2 Department of Earth Sciences, University of Kashmir, Srinagar, India

Edited by: Aparna Shukla, Ministry of Earth Sciences, India

Reviewed by: Martin Truffer, University of Alaska Fairbanks, United States

Atanu Bhattacharya, University of Zurich, Switzerland

 *Correspondence: Irfan Rashid, irfangis@gmail.com, irfangis@kashmiruniversity.ac.in

This article was submitted to Cryospheric Sciences, a section of the journal Frontiers in Earth Science

 23 12 2021 2021 9 748107 28 07 2021 22 11 2021 Copyright © 2021 Majeed, Rashid, Najar and Gul. 2021 Majeed, Rashid, Najar and Gul

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Glaciers across the Himalayan arc are showing varying signs of recession. Glaciers in the eastern and western parts of the Himalayan arc are retreating more rapidly as compared to other regions. This differential retreat is often attributed to climatic, topographic, and geologic influences. The glaciers in the Trans-Himalayan region of Ladakh are believed to be relatively stable as compared to other parts of the western Himalaya. The present study ascertained the area changes and frontal retreat of 87 glaciers in the Pangong Region between 1990 and 2019 using satellite data. The geodetic mass changes were also assessed using SRTM and TanDEM-X digital elevation models of 2000 and 2012 respectively. Besides, the glacier outlines were delineated manually and compared with existing regional and global glacier inventories that are available over the region. The GlabTop model was used to simulate the glacier-bed overdeepenings of four glaciers that are associated with a proglacial lake. The study also analyzed the impact of topographic influences and varying debris cover on glacier recession. This analysis indicated deglaciation of 6.7 ± 0.1% (0.23% a−1) from 1990 to 2019 over the Pangong Region with clean-ice glaciers showing a higher retreat (8.4 ± 0.28%) compared to the debris-covered glaciers (5.7 ± 0.14%). However, the overall recession is lower compared to other parts of northwestern Himalayas. The glacier recession showed a positive correlation with mean glacier slope (r = 0.3) and debris cover (r = 0.1) with bigger size glaciers having retreated at a lesser pace compared to smaller ones. This underpins the need for in-situ data about debris thickness to precisely ascertain the role of debris on glacier recession in the Trans-Himalayan Ladakh where debris thickness data is absent. The mean glacier elevation did not indicate any influence on glacier recession. From 2000 to 12, the glaciers lost an ice mass amounting to 0.33 ± 0.05 m we. per year. The formation of four new proglacial lakes, although small (<6 ha), need to be monitored using remote sensing data while the infrastructure development activities should not be permitted given glacial lake outburst flood risk.

 glacier recession geodetic mass changes glacier inventories remote sensing pangong lake ladakh himalaya Introduction

Glaciers are important indicators of climate change (Haeberli and Hoelzle, 1995; Roe, 2011) and have significant impacts on water availability (Immerzeel et al., 2009). Changes in the glacier extent (Bolch et al., 2012; Kulkarni and Karyakarte, 2014; Rashid et al., 2017) and mass (Brun et al., 2017; Bandyopadhyay et al., 2019) have been very well documented in many parts of the Himalayas, but comparatively little attention has been paid to glacier monitoring in the Ladakh Region (Chudley et al., 2017; Schmidt and Nüsser, 2017), which besides being at a high altitude happens to be a rain-shadow region (Abdullah et al., 2020). For these reasons, it is also known as a cold desert (Romshoo et al., 2020). Owing to the remoteness of the area (Azam et al., 2018), there is only one field-based glaciological study (Soheb et al., 2020) that reported a negative mass balance of the neighbouring Stok Glacier between 1978 and 2019.

Remote sensing data products have been widely used to monitor the alpine cryosphere (Dar et al., 2014; Pratibha and Kulkarni, 2018; Chand and Watanabe, 2019; Liu et al., 2019; Sujakhu et al., 2019). Various approaches include automatic classification (Paul et al., 2002, 2004; Zhang et al., 2019; Lu et al., 2020), semi-automatic classification (Shukla et al., 2010; Rastner et al., 2014; Sahu and Gupta, 2018; Robson et al., 2020), object-based image algorithms, and manual digitization (Ye et al., 2006; Bhambri et al., 2011; Rashid and Majeed, 2020) have been widely used by remote sensing glaciologists. Manual digitization has been documented to be the most reliable since it takes the cognitive inputs from the analyst (Paul et al., 2013), however, it is time-consuming and subjective to the skill of the analyst. Besides areal changes, remote sensing has a capacity to map volumetric changes; the most important being the DEM products that are mostly stereo-products (Cogley, 2009; Gaddam et al., 2021). Owing to the lack of field data, rugged topography, and remote locations, the geodetic mass change assessments offer the easiest choice to estimate volume changes. However, such estimates are associated with uncertainties, which need to be quantified for reporting an accurate estimate.

While glacier recession is primarily attributed to atmospheric warming (Wang et al., 2017; Cook et al., 2019) and an increase in the anthropogenic footprint in the glacier and peri-glacier environments (Wang et al., 2019; Huggel et al., 2020), there could be several topographic and geological factors (Garg et al., 2017; Patel et al., 2018) that could affect glacier health. The variable debris cover on the glaciers could be a controlling factor (Barrand and Murray, 2006; Shukla and Qadir, 2016; Salerno et al., 2017), however, a recent study on the Karakoram (Muhammad et al., 2020) negates the role of thin debris cover on glacier recession.

The long-term records of the hydro-meteorological data are almost absent, which hampers quantifying any historic changes in the glaciers of the region. Although the use of gridded climate datasets for glaciohydrological assessments has been documented (Bhattacharya et al., 2021), such datasets need bias correction (Hussain et al., 2017; Kanda et al., 2020). The prevalent warming temperatures over the western Himalayas aid in glacier recession and also result in the formation of numerous proglacial lakes and expansion of already existing ones (Worni et al., 2013; Wang et al., 2015). These lakes are growing in both area and number (Nie et al., 2017; Rashid and Majeed, 2018). One of the most dangerous hazards that these proglacial lakes pose is that of a glacial lake outburst flood (Vuichard and Zimmermann, 1987; Liu et al., 2013; Allen et al., 2019). This calls for increased attention, as they can prove to be fatal in case of a sudden outburst. There are just a few studies (Govindha Raj, 2010; Mir et al., 2018; Rashid and Majeed, 2018) of these proglacial lakes in the region. This necessitates a need for continuous monitoring and detailed risk assessment for precisely quantifying the risk that these lakes pose to the downstream communities and infrastructure. To our knowledge, there is only one detailed study that reconstructed a GLOF event in the Ladakh region (Majeed et al., 2021). The receding glaciers and the damming of water behind the moraines could alter the hydrological regimes (Singh and Bengtsson, 2004; Immerzeel et al., 2010, 2012) in the region and cause water availability issues (Miller et al., 2012). This could have serious implications on the socio-economy of the region and threaten food security (Misra, 2014; Bocchiola et al., 2019).

This study aims to map the glaciers of the Pangong region, Trans-Himalaya Ladakh. Multi-temporal satellite datasets were used to assess the area changes and frontal retreat of the glaciers in the region. A qualitative comparison was carried out by comparing the glacier outlines delineated in this study with three existing global and regional glacier inventories. The topographic characteristics and debris cover were correlated with glacier area changes. The geodetic mass changes were quantified using multi-date DEMs of 2000 and 2012. In addition, the GlabTop model was used to simulate the proglacial lake expansion utilizing information about glacier bed overdeepenings.

 Study Area

This study focused on the glaciers of the Pangong Mountain Range (PMR), in the northern region of Ladakh. The PMR runs parallel to the Ladakh Range (Godwin-Austen, 1867) ∼100 km northwest from Chushul. The highest elevation in the range is 6,700 m and the northern slopes are heavily glaciated. This area is situated on the left bank of Pangong Lake but lies at an elevation of 1,400 m above the lake. Pangong Lake is an endorheic lake in the Himalayas (Rathour et al., 2020) situated at a height of about 4,350 m. It is 134 km long and extends from India to China. Approximately 60% of the length of the lake lies in China. The lake is 5 km wide at its broadest point and covers a total area of 604 km2. Geographically, it is not a part of the Indus River basin area and is a separate land-locked river basin without any outlet. While the total area of the watershed is 844 km2, the glaciated area is ∼60 km2 which amounts to a total of just ∼7%. There are a total of 87 glaciers with a cumulative area of 59.45 ± 6.86 km2 as mapped from satellite data of 1990. These glaciers are relatively small with an average size of 0.8 km2. The smallest glacier has an area of 0.29 ± 0.01 km2 while the largest has an area of 4.31 ± 0.35 km2. Most of the glaciers are debris-covered. The watershed lies between latitudes 33°36′ N—34°2′ N and longitudes 78°14′ E—78°39′ E (Figure 1). The mean elevation of the watershed is 5,109 m. The summers are mild, while the winters are extreme in the region (Srivastava et al., 2020). The lake freezes completely in winters. Pangong receives winter precipitation mostly by Mediterranean influences.

Location of the study area. Glaciers (yellow outlines) and proglacial lakes (pink stars) of the Pangong Mountain Range draped on the Sentinel 2A image (August 28, 2017). Inset image: Major locations around Pangong Region draped on 30-arc second GTOPO DEM. Red dot in the India map indicates Pangong Region.

 Materials and Methods Data

A repository of satellite data and DEMs were used to carry out this study. The snow-free images from 1990 to 2019 were used for areal changes of the glaciers while two different DEMs were used for quantifying the mass changes. The details of these datasets are provided in Table 1.

Details of the datasets used in this study.

Dataset  Scene/product/path and row ID Spatial resolution (m) Date of acquisition Source Usage of datasets
Satellite data
Landsat TM LT05_L1TP_146,037_19900802_20170129_01_T1 30 August 02, 1990 http://www.earthexplorer.usgs.gov Boundary delineation (1990)
Planet labs 20190919_052142_83_1066_3B_AnalyticMS.tif 3 September 19, 2019 https://www.planet.com/ Boundary delineation (2019)/Glacier dynamics, debris mapping, proglacial lake mapping
20190919_051248_1,105_3B_AnalyticMS.tif
20190919_051246_1,105_3B_AnalyticMS.tif
20190919_051247_1,105_3B_AnalyticMS.tif
20190919_035516_1,020_3B_AnalyticMS.tif
20190919_035515_1,020_3B_AnalyticMS.tif
20190919_035514_1,020_3B_AnalyticMS.tif
Topographic data
SRTM SRTM3N33E078V1 30 February 2000 http://www.earthexplorer.usgs.gov Geodetic mass balance, topographic characterization
SRTM3N34E078V1
 TanDEM-X N33E078 90 2012 https://download.geopservice.dlr.de/TDM90/ Geodetic mass balance
N34E078
ALOS AP_08149_FBD_F0660_RT1 12.5 2011 https://asf.alaska.edu/ GlabTop analysis
Methods Glacier Changes

The standard geometric correction (Jensen, 2005) was used to coregister the images. The images from 1990 to 2019 were coregistered to arrive at an accuracy of one pixel. The glaciers were manually digitized at a scale of 1:30,000. Mountain shadows, cloud cover, and debris cover limit the accuracy of the glacier mapping. However, alternate images from ±2 years were used to overcome this. All the glacier boundaries were validated using high-resolution Google Earth data. Proglacial lakes are also mapped from the same images. The precision of the glacier boundary delineation is within half a pixel (Bolch et al., 2010; Paul et al., 2013). For the 1990s outlines, we assumed a mapping inaccuracy of half a pixel (∼15 m) for Landsat TM data and one pixel (∼3 m) for Planet Cubesat data (Pieczonka and Bolch, 2015; Bhattacharya et al., 2016).

The uncertainties related to the area changes (EAC) was calculated considering the law of error propagation as (Hall et al., 2003): E A C = E A 1 2 + E A 2 2 where EAC is the uncertainty related to the change in the area between two time periods, EA1 is the uncertainty of glacier area at one point in time and EA2 is the uncertainty of glacier area at the second point in time.

The uncertainties related to snout changes between two times (ESC) is given by the following formula (Hall et al., 2003): E S C = λ 1 2 + λ 2 2 where ESC is the uncertainty in snout change, λ1 and λ2 are the spatial resolutions of the two images used for calculating the snout changes.

 Glacier Debris and Topographic Parameters

The supraglacial debris was manually delineated from the high-resolution Planet Cubesat images for the year 2019. Besides high-resolution Google Earth imagery was used to validate the debris cover. The topographic parameters that include slope, elevation, and southerly aspect of the glaciers were estimated using TanDEM-X in a GIS environment. The topographic parameters and debris cover were co-related with glacier recession rates to arrive at trends.

 Geodetic Mass Balance

The glaciological method to estimate mass loss is a challenging task given the costs involved and also the remoteness of glaciers (Rashid et al., 2021). Many glaciers in the Jammu and Kashmir region are inaccessible for direct measurements due to the ruggedness of the terrain (Immerzeel et al., 2009; Bolch et al., 2019). It is pertinent to mention that long-term glaciological mass balance measurements in the trans-Himalayan region of Ladakh do not exist, however, there have been some initiatives very recently that attempt to establish long-term field-based glacial mass balance records in Stok Kangri (Soheb et al., 2020) and Zanskar regions (Mehta et al., 2021). On the contrary, the geodetic mass balance assessment is a quick method to access mass losses in the glaciers. It simply involves the DEMs of two times and differencing to arrive at thickness changes (Braithwaite, 2002). This thickness change can be converted to corresponding mass loss using glacier area and density assumptions (Cogley, 2009; Muhammad et al., 2019; Rashid and Majeed, 2020). For quantifying the geodetic mass changes over the Pangong region, SRTM C band DEM of 2000 and TanDEM-X of 2012 were used. A correction factor of 3.4 m for SRTM C band penetration in glacier ice was used as suggested by Gardelle et al. (2013). TanDEM-X is a stacked product with data acquired over multiple seasons/years. Hence, a uniform radar penetration is unlikely. The potential radar penetration of the X band was ignored due to its high frequency and non-uniform penetration across different seasons (Huber et al., 2020). The DEMs were coregistered using the universal co-registration algorithm (Nuth and Kääb, 2011). Further, the outliers in the off-glacier (Supplementary Figure S1) and on-glacier elevation (Supplementary Figure S3) were identified and removed using the inter-quartile range approach suggested by Barbato et al. (2011) and adopted by Huber et al. (2020). The DEM differencing results were interpolated using Inverse Distance Weighted (IDW) algorithm over the voided areas (13.49%) of SRTM DEM. The elevation-dependent outliers in the on-glacier DEM difference image were ignored since the glaciers lie in a homogenous topographic zone (Mean glacier elevation: 5,613–6,534 m asl).

The uncertainty in geodetic mass balance (∇M) related to glacier area (A), ice density (ρ) assumed to be 850 ± 60 kg m−3, and surface elevation change (h) is expressed as (Ramsankaran et al., 2019): ∇ M M = ( ∇ d h ¯ d h ) 2 + ( ∇ ρ i c e ρ i c e ) 2 + ( ∇ A A ) 2 where ∇ represents the uncertainty in estimates.

The uncertainty in elevation change (σΔh) is calculated based on the following formula Huber et al. (2020): σ Δ H = σ d e m 2 + σ v o i d f i l l 2 + σ T D X d a t e 2 where σem is the uncertainty in DEM, σvoidfill is the uncertainty introduced by the void-filling approach, and σTDXdate is the date uncertainty of TanDEM-X and estimated to be ± 2 times the change of elevation per year. The uncertainty in the void filling was assumed negligible since the observed and predicted values for non-voided areas showed an insignificant bias (0.02 m).

The σdem was calculated after the approach suggested by McNabb et al. (2019) as: σ d e m = ( σ Δ z   A ) 2 + ( σ a r e a   Δ z ) 2 A where σΔz is the mean of glacier difference between the two DEMs after coregistration, A is the glacier area, σarea is the uncertainty in glacier area and ΔZ is the mean elevation difference of on-glacier pixels.

 Glacier Bed Overdeepenings

Glacier-bed overdeepenings and ice thickness were estimated for four glaciers associated with proglacial lakes in the Pangong region using the GlabTop model to check the possibility of expansion of existing proglacial lakes in the future. The input parameters to the model are glacier outlines, DEM, and the glacier flowlines. In this study, the glacier outlines and flowlines were manually delineated from the satellite data and also using a 3D perspective from Google Earth. The glacier bed overdeepenings were modeled using glacier outlines and flowlines and ALOS PALSAR DEM. The model has been extensively used for predicting ice thickness and glacier bed overdeepenings in the Himalayas with an uncertainty of 30% (Linsbauer et al., 2016; Rashid and Majeed, 2020; Sattar et al., 2021), however, the uncertainty in the ice thickness estimates could be reduced to 17% using better quality DEMs and improved shape factor (Ramsankaran et al., 2018; Majeed et al., 2021). The model assumes constant basal stress along the central flowline of a glacier (Linsbauer et al., 2012) and quantifies ice-thickness using a slope-dependent approach (Haeberli and Hoelzle, 1995).

 Comparison With Existing Glacier Inventories

The various glacier inventories available for this region include the Randolph Glacier Inventory (RGI), Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM), and International Council for Integrated Mountain Development (ICIMOD) Glacier Inventories. A comparative analysis is made between these inventories and the glacier inventory generated in this study.

 Results Glacier Area and Frontal Changes

Satellite data revealed a total of 87 glaciers in the study area covering an area of 54.8 ± 2.53 km2 in 1990. The glacier cover reduced to 51.1 ± 1.03 km2 by 2019 indicating a 6.7 ± 0.1% area loss at a rate of 0.23% a−1 (Figure 2; Table 2). While the clean-ice glaciers retreated by 8.4 ± 0.28% (1.6 km2), the debris-covered glaciers lost 5.7 ± 0.14% (2.03 km2) at a rate of 0.29% a−1 and 0.2% a−1 respectively. The glacier size varies between 3.5 and 353.4 ha with an average size of 58.8 ha. The snout recession, with an uncertainty of ± 30.1 m, varied between 0 m (GLIMS Id G33717E78538N) to 557 m (GLIMS Id G33725E78536N) at an average of 126 m (4.3 m a−1).

Area changes and frontal retreat of Pangong group of glaciers from 1990 to 2019. Background image: Sentinel 2A of August 28, 2017.

Area and frontal changes of Pangong Group of glaciers from 1990 to 2019.

GLIMS id T.S id Area 1990 (ha) Area 2019 (ha) Debris 2019 (ha) Area change (2019–1990) Snout change (m) M.G.E Aspect Frontal slope(o) Overall slope(o) Mean surface lowering (ma−1)
G33706E78579N 1 21.92 19.16 0 −2.76 −135 5914 NE 16 22.4 0.11
G33708E78575N 2 86.31 79.04 7.28 −7.27 −80 6234 NE 19 17.8 0.29
G33712E78568N 3 38.78 37.93 0.25 −0.85 −108 6286 N 16.8 21 0.14
G33708E78556N 4 46.03 40.31 0 −5.72 −54 5906 SE 17.8 28.9 0.52
G33717E78538N 5 20.5 20.62 0 0.12 −4 5963 SE 19.5 25.7 0.26
G33715E78523N 6 357.5 353.43 0.82 −4.07 −150 5898 SE 13.2 17.6 0.43
G33736E78504N 7 51.64 50.75 1.12 −0.89 −26 6012 W 11.8 23.3 0.25
G33723E78503N 8 35.89 31.67 3.3 −4.22 −200 5746 W 21.7 25.9 0.69
G33758E78524N 9 169.26 166.45 0.85 −2.81 −57 5849 NE 11.5 15.3 0.50
G33763E78492N 10 30.08 28.14 0 −1.95 −106 6072 N 14.5 24.6 0.42
G33764E78519N 11 91.42 83.23 3.58 −8.19 −57 5863 NE 13.8 17.2 0.46
G33771E78515N 12 98.76 94.09 1.32 −4.67 −106 5838 NE 12.6 15.2 0.62
G33782E78514N 13 194.31 180.08 1.03 −14.23 −148 6155 NE 14.8 15.1 0.48
G33797E78499N 14 125.07 125.53 0.71 0.46 −110 5818 NE 6.5 13.8 0.45
G33809E78456N 15 119.32 117.29 0 −2.03 −9 6141 SW 13.8 18 0.21
G33807E78481N 16 124.8 119.94 5.55 −4.85 −98 6244 E 10.7 17.1 0.45
G33821E78487N 17 45.37 41.75 0.72 −3.62 −128 6534 NE 17.5 26.2 0.49
G33822E78449N 18 60.48 56.85 0 −3.63 −100 5740 N 17.4 19.6 0.36
G33831E78449N 19 28.09 23.8 0 −4.29 −60 5969 W 14.3 20 0.17
G33837E78466N 20 83.31 78.52 4.39 −4.79 −48 6007 NE 16.3 18.4 0.45
G33870E78431N 21 27.06 24.25 8.02 −2.81 −143 5953 NW 11.7 19.3 0.61
G33877E78423N 22 79.26 72.51 3.12 −6.75 −283 5692 NW 17.1 23 0.44
G33880E78410N 23 42.63 35.98 0 −6.66 −110 5846 NE 14.1 18.2 0.35
G33897E78392N 24 97.72 92.19 20.9 −5.53 −372 5764 NW 18.5 23.9 0.45
G33912E78372N 25 255.96 243.44 8.13 −12.52 −56 5802 NE 16 17.8 0.52
G33920E78352N 26 241.82 233.28 2.22 −8.55 −136 5659 N 17.3 19.4 0.38
G33926E78341N 27 110.71 105.75 0 −4.96 −60 6039 NW 21.1 24.1 0.25
G33900E78359N 28 9.14 7.46 0 −1.68 −100 6068 SE 30.3 39 0.69
G33918E78368N 29 13.34 11.86 0 −1.47 −156 6000 N 15.5 22.8 0.55
G33921E78371N 30 36.63 31.68 0.31 −4.95 −42 6055 NE 25.9 25.2 0.78
G33726E78568N 31 19.49 16.05 0 −3.44 0 5997 N 19.8 22.3 0.28
G33911E78356N 32 6.27 5.66 0 −0.6 −148 5960 SW 18.6 18.2 0.20
G33903E78384N 33 51.47 51.83 0 0.36 −64 5742 NE 16.6 22.8 0.48
G33713E78546N 34 97.75 86.03 0 −11.72 −56 5777 S 15.3 19.5 0.26
G33757E78496N 35 33.49 28.53 0 −4.95 −192 6000 SW 28.6 29.4 0.14
G33725E78536N 36 13.37 12.67 0 −0.7 −557 6096 E 32.3 32.3 0.17
G33883E78398N 37 91.91 84.74 3.59 −7.17 −53 6098 NE 10.4 16.9 0.71
G33853E78444N 38 39.44 31.23 0.96 −8.21 −108 6059 NE 18.2 27.6 0.28
G33853E78454N 39 65.88 59.72 0 −6.15 −171 6187 NE 18.4 20.9 0.24
G33748E78531N 40 195.02 182.93 3.78 −12.1 −225 6107 NE 13.3 13.8 0.40
G33791E78505N 41 148.19 143.79 0.63 −4.4 −95 6197 NE 12.1 13.6 0.58
G33842E78455N 42 89.87 82.32 0.55 −7.54 −60 6093 NE 11 19 0.45
G33747E78498N 43 129.21 125.83 0 −3.38 −148 6016 SW 15.2 14.1 0.25
G33847E78436N 44 89.01 81.47 0 −7.54 −170 6055 NW 18.6 20.8 0.07
G33719E78564N 45 30.68 29.34 1.08 −1.35 −79 6025 NE 16.7 22.5 0.28
G33727E78553N 46 127.14 120.37 0 −6.77 −174 5997 N 13.8 16.9 0.22
G33865E78438N 47 62.05 58.47 0 −3.58 −82 5959 NE 13 26.5 0.51
G33872E78401N 48 45.44 42.98 0.6 −2.47 −92 6099 NE 14.8 16.6 0.24
G33737E78537N 49 210.9 206.95 2.04 −3.95 −50 6053 NE 9.9 14 0.26
G33822E78468N 50 59.37 57.62 0.54 −1.76 −63 5800 NW 7.7 19.6 0.30
G33953E78326N 51 52.16 45.26 1.28 −6.9 −86 5867 N 19.4 22.9 0.34
G33958E78316N 52 66.01 61.76 0.5 −4.25 −61 5811 N 11.3 30.5 0.32
G33965E78307N 53 34.31 30.46 1.62 −3.85 −230 5793 NE 21.8 28.6 0.37
G34005E78247N 54 40.41 37.51 0.2 −2.9 −434 5814 NW 28.3 28.3 0.03
G33937E78335N 55 28.65 23.97 0 −4.67 −65 5773 E 18.8 24.3 0.50
G33942E78335N 56 31.56 23.7 0.71 −7.86 −356 5925 NE 23.9 30.9 0.77
G33933E78361N 57 26.62 24.21 0 −2.41 −57 5763 N 25.7 31.5 0.09
G33932E78372N 58 56.48 47.92 0 −8.56 −178 5834 N 28.2 28.6 0.50
G33962E78323N 59 46.51 44.43 0 −2.08 −178 5798 N 29.2 30.5 0.19
G33971E78297N 60 27.63 22.98 1.1 −4.65 −63 5745 N 26.5 31.9 0.17
G33996E78265N 61 37.29 31.16 2.76 −6.14 −178 5907 NE 29.5 38.5 0.46
G33740E78486N 62 112.54 106.79 0 −5.75 −254 5949 NW 26.4 35.7 0.27
G33740E78557N 63 36.65 34.18 0 −2.47 −202 5880 NE 32.4 30.4 0.37
G33819E78442N 64 32.75 27.98 0 −4.76 −202 5763 NW 31 34.8 0.13
G33814E78487N 65 16.9 15.91 0 −0.99 −35 5870 E 25.7 34.4 0.72
G33857E78438N 66 13.51 11.06 0 −2.44 −122 6133 E 29.1 35.4 0.47
G33880E78393N 67 15.71 15.21 0 −0.5 −58 5917 NE 15.8 31.7 0.48
G33967E78300N 68 19.29 17.01 0.5 −2.28 −77 5710 NE 31.8 32.5 0.60
G33977E78291N 69 20.46 17.6 0 −2.86 −280 5905 N 27.2 34.4 0.28
G33987E78277N 70 4.79 3.49 0 −1.3 −62 5722 NE 24.5 35.1 0.45
G33998E78260N 71 19.42 14.63 1.18 −4.79 −310 5820 N 31.2 40 0.23
G33954E78309N 72 24.42 21.62 0 −2.8 −70 5802 NW 32.9 34.7 0.01
G33909E78339N 73 8.9 8.37 0 −0.53 −172 5878 SE 30 40.9 0.58
G33906E78357N 74 8.61 6.71 0 −1.89 −148 5736 W 29 28.2 0.03
G33854E78436N 75 6.56 3.7 0 −2.86 −60 5889 NW 10.2 25.6 0.00
G33798E78452N 76 50.97 49.36 0 −1.61 −24 5645 S 39.9 39 0.58
G33801E78459N 77 41.65 40.09 0 −1.56 −43 5775 E 13.6 33.2 0.43
G33714E78509N 78 31.19 28.74 0 −2.45 −20 5773 NW 23.1 34.8 0.13
G33692E78583N 79 13.12 11.51 0 −1.61 −258 5727 E 25.3 26.4 0.31
G33868E78390N 80 21.15 17.56 0 −3.59 −14 5773 W 23.9 35.5 0.04
G33890E78395N 81 10.78 10.21 4.06 −0.56 −52 5647 NE 29.8 33.7 0.39
G33927E78334N 82 18.98 17.14 0 −1.84 −170 5731 E 29.4 32.2 0.60
G33922E78327N 83 14.48 10.18 0 −4.29 −136 5875 NW 30.4 39.8 0.45
G33702E78581N 84 48.14 41.49 0.92 −6.65 −41 6170 NE 18.3 29.9 0.36
G33911E78334N 85 55.56 46.44 0 −9.12 −98 5716 NW 19.7 31.8 0.09
G33770E78494N 86 26.11 22.55 0 −3.56 −70 5613 W 18.2 19.3 0.29
G33902E78358N 87 10.06 7.99 0 −2.07 −229 5931 W 28.4 30.4 0.16

The mean glacier elevation for all the glaciers is more than 5,600 m asl ranging from 5,613 m asl to 6,534 m asl. Analysis of glacier area changes does not show any significant correlation (r = -0.14) with the mean glacier elevation (Figure 3A). The mean glacier slope varied between 13.6° and 40.9°. The mean glacier slope showed a positive correlation (r = 0.3) with glacier recession (Figure 3B) which was further researched by ascertaining the frontal slope of each glacier. The frontal slope was calculated for the 400 m area from the snout of each glacier. The mean frontal slope for the glaciers of the Pangong Region varied between 6.5° and 39.9°. The frontal slope also showed a similar positive correlation (r = 0.31) with glacier recession.

Correlation of glacier area changes with (A) Mean glacier elevation, (B) Mean glacier slope, (C) Debris cover, (D) Glacier area, and (E) Slope-aspect.

Based on the debris cover, the glaciers were categorized into clean-ice and debris-covered glaciers (Figure 3C). This analysis indicated 48 (∼55%) out of 87 glaciers were clean-ice glaciers and 39 (45%) were debris-covered with debris-cover varying from 0.2–39.8%. This analysis indicated that glacier recession has a slightly positive correlation with debris cover (r = 0.1) which needs to be further investigated. The glacier area showed an inverse relation (r = -0.47) with glacier recession (Figure 3D) indicating smaller glaciers recede faster than larger ones. Moreover, the role of slope-aspect was correlated with glacier recession (Figure 3E). Analysis of slope-aspect indicated that glaciers having west and northwest-facing slopes lose area slightly faster compared to south-facing glaciers.

 Geodetic Mass Balance

DEM differencing indicated that the Pangong group of glaciers, on average, lost 4.59 ± 0.64 m at a rate of 0.38 ± 0.05 m a−1 between 2000 and 2012 with the highest thickness loss of 1.45 m a−1 and the highest gain of 0.22 m a−1 (Figure 4). While the surface of clean-ice glaciers lowered by 0.28 m a−1 the ice loss for debris-covered glaciers was estimated to be 0.43 m a−1. Glacier-wise the highest mean surface lowering of 0.78 m a−1 was observed in TS ID 30 (GLIMS ID G33921E78371N) and the highest surface gain of 0.002 m a−1 was observed for TS ID 75 (GLIMS ID G33854E78436N). The mass loss corresponding to surface lowering for these glaciers is calculated to be 199.48 Mt from 2000 to 2012. This is indicative of ice loss at the rate of 16.62 Mt a−1. The equivalent water loss is estimated to be 3.9 ± 0.54 m we. at a rate of 0.33 m w. e. per year. For clean-ice glaciers, the estimated equivalent water loss is 0.24 m we. per year. However, for debris-covered glaciers, the equivalent water loss is 0.37 m we. per year.

Geodetic mass changes of glaciers in Pangong region from 2000–2012.

 Glacier Thickness and Bed Overdeepenings

Out of the total 87 glaciers mapped in the Pangong Region, four glaciers (GLIMS ID: G33715E78523N, G33723E78503N, G33763E78492N, and G33897E78392N) are associated with a proglacial lake. These lakes have an area of 5.74 ± 0.16 ha, 0.42 ± 0.04 ha, 0.23 ± 0.03 ha and 0.56 ± 0.05 ha, respectively, GlabTop model analysis simulated a mean glacier ice thickness of 58 m with a maximum and minimum thickness of 1 and 145 m, respectively (Figure 5). It further indicated that the proglacial lake associated with G33715E78523N can further grow by 1.72 ha reaching a maximum area of 7.46 ha. The model suggests the formation of eight additional glacier bed overdeepenings for the glacier most of which lie in the upper ablation zone with areas less than a hectare. For G33723E78503N the ice thickness varies between 1 and 48 m with a mean thickness of 19 m. The model does not simulate any expansion of the proglacial lake, however, it predicted the formation of a new lake with an area of 1.06 ha around the middle of the ablation zone of the glacier. For G33763E78492N the ice thickness varies between 1 and 34 m with a mean of 17 m. The model predicted expansion of the existing proglacial lake by 0.36 ha reaching a maximum area of 0.59 ha. The model indicated the formation of two more bed overdeepenings with an area of 0.03 and 0.06 ha in the upper ablation zone. For G33897E78392N the ice thickness varies between 1 and 75 m with a mean of 31 m. The model pointed to the expansion of the existing proglacial lake by 0.87 ha reaching a maximum area of 1.43 ha. Further, the model predicted three additional bed overdeepenings in the upper ablation zone.

GlabTop model simulations indicating ice-thickness and glacier-bed overdeepenings.

 Comparison With Existing Glacier Inventories

There is a huge uncertainty concerning the total number of glaciers in the Himalayas (Bolch et al., 2012; Azam et al., 2021), the objective of comparison of existing global and regional inventories was made with the inventory generated in this study. Of the three glacier inventories—CIMOD, GAMDAM, and RGI—available for the region, GAMDAM glacier inventory captured all the glaciers that were delineated in this study (Figure 6; Table 3). The total number of glaciers in the GAMDAM inventory for the Pangong Region is 132 which cover an area of 72.7 km2 indicating an overestimation by 51% (45 glaciers) in the number of glaciers and 42.3% (21.6 km2) in glacier area. The smallest glacier area mapped in this study has an area of 3.49 ha compared to 1.03 ha mapped in the GAMDAM inventory. Similarly, the largest glacier area mapped in this study is 353.4 ha compared to 418.5 ha in GAMDAM. The other two inventories, ICIMOD and RGI, grossly underestimate the total number of glaciers and total glacier area in the Pangong Region. The ICIMOD glacier inventory suggests 44 glaciers spread over 19.9 km2 indicating an underestimation of 49.4% (43 glaciers) total number of glaciers and 61.1% (31.3 km2) in the glacier area. The RGI suggests 50 glaciers spread over 26.6 km2 indicating an underestimation of 42.5% (37 glaciers) total number of glaciers and 61.1% (24.6 km2) in the glacier area.

Comparison of glacier outlines delineated in this study with Randolph Glacier Inventory (RGI), GAMDAM glacier inventory, and ICIMOD glacier inventory.

Comparison of the present inventory (TS) with the existing inventories (RGI, ICIMOD and GAMDAM).

GLIMS ID Area (ha) TS-RGI TS-ICIMOD TS-GAMDAM
RGI ICIMOD GAMDAM This Study (TS)
G33987E78277N NA NA 3.19 3.49 X X 0.30
G33854E78436N NA 7.44 4.27 3.70 X −3.74 −0.57
G33911E78356N NA NA 4.89 5.66 X X 0.77
G33906E78357N NA NA 10.41 6.71 X X −3.70
G33900E78359N NA 12.83 NA 7.46 X −5.36 X
G33902E78358N 15.02 8.17 15.02 7.99 −7.03 −0.18 −7.03
G33909E78339N NA 7.23 NA 8.37 X 1.14 X
G33922E78327N 43.2 NA 43.25 10.18 −33.01 X −33.07
G33890E78395N NA NA 9.83 10.21 X X 0.38
G33857E78438N NA NA 17.66 11.06 X X −6.60
G33692E78583N NA NA 11.67 11.51 X X −0.16
G33918E78368N NA NA 12.59 11.86 X X −0.73
G33725E78536N NA NA 11.91 12.67 X X 0.76
G33998E78260N 24.44 NA 24.44 14.63 −9.81 X −9.81
G33880E78393N NA NA NA 15.21 X X X
G33814E78487N NA NA 15.13 15.91 X X 0.78
G33726E78568N NA NA 27.74 16.05 X X −11.69
G33967E78300N 20.51 16 20.51 17.01 −3.49 1.01 −3.50
G33927E78334N 43.25 18.14 43.25 17.14 −26.11 −1.00 −26.11
G33868E78390N 21.6 NA 21.6 17.56 −4.04 X −4.04
G33977E78291N 20 10.39 20 17.60 −2.40 7.21 −2.40
G33706E78579N NA NA 14.03 19.16 X X 5.13
G33717E78538N 20.85 22.89 20.85 20.62 −0.23 −2.27 −0.23
G33954E78309N 21.11 NA 21.11 21.62 0.51 X 0.51
G33770E78494N 29 25.67 35.5 22.55 −6.45 −3.11 −12.95
G33971E78297N 31.47 17.59 31.47 22.98 −8.49 5.39 −8.49
G33942E78335N NA 21.68 NA 23.70 X 2.02 X
G33831E78449N 29.7 13.25 31.41 23.80 −5.89 10.55 −7.61
G33937E78335N NA 28.5 70.89 23.97 X −4.53 −46.92
G33933E78361N NA 24.33 25.06 24.21 X −0.12 −0.85
G33870E78431N NA NA 47.53 24.25 X X −23.28
G33819E78442N 28.6 12.68 28.6 27.98 −0.61 15.30 −0.62
G33763E78492N 44.51 27.6 48.21 28.14 −16.37 0.54 −20.07
G33757E78496N 29.81 32.29 29.81 28.53 −1.27 −3.75 −1.28
G33714E78509N 48.12 18.9 38.02 28.74 −19.38 9.84 −9.28
G33719E78564N NA NA 58.34 29.34 X X −29.00
G33965E78307N 37.28 33.42 37.28 30.46 −6.82 −2.96 −6.82
G33996E78265N 35.5 NA 35.5 31.16 −4.34 X −4.34
G33853E78444N NA NA 45.32 31.23 X X −14.09
G33723E78503N 41.27 23.8 41.27 31.67 −9.60 7.87 −9.60
G33921E78371N NA NA 42.56 31.68 X X −10.88
G33740E78557N NA NA 34.39 34.18 X X −0.21
G33880E78410N NA NA 66 35.98 X X −30.02
G34005E78247N 44.18 NA 44.18 37.51 −6.67 X −6.67
G33712E78568N NA NA 86.01 37.93 X X −48.08
G33801E78459N 40.08 34.75 38.12 40.09 0.01 5.34 1.97
G33708E78556N 42.68 42.86 42.68 40.31 −2.37 −2.55 −2.37
G33702E78581N NA NA 44.78 41.49 X X −3.29
G33821E78487N NA NA 79.55 41.75 X X −37.80
G33872E78401N 59.74 49.38 59.74 42.98 −16.76 −6.40 −16.76
G33962E78323N 43.48 40.82 43.48 44.43 0.95 3.61 0.95
G33953E78326N 68.57 41.2 68.57 45.26 −23.31 4.06 −23.31
G33911E78334N 61.73 NA 61.73 46.44 −15.29 X −15.29
G33932E78372N NA NA 50.64 47.92 X X −2.72
G33798E78452N 48.41 36.2 45.8 49.36 0.95 13.16 3.56
G33736E78504N 68.83 58.62 73.76 50.75 −18.08 −7.87 −23.01
G33903E78384N NA NA 95.94 51.83 X X −44.11
G33822E78449N 76.21 52.2 76.21 56.85 −19.36 4.64 −19.36
G33822E78468N NA NA 189.54 57.62 X X −131.92
G33865E78438N NA NA 82.82 58.47 X X −24.35
G33853E78454N NA NA 87.39 59.72 X X −27.67
G33958E78316N 94.55 31.4 89.36 61.76 −32.79 30.36 −27.60
G33877E78423N NA NA 95.11 72.51 X X −22.60
G33837E78466N NA NA 110.28 78.52 X X −31.76
G33708E78575N NA NA 112.74 79.04 X X −33.70
G33847E78436N 99.5 77.08 99.51 81.47 −18.03 4.39 −18.04
G33842E78455N NA NA 127.82 82.32 X X −45.50
G33764E78519N NA NA 342.11 83.23 X X −258.88
G33883E78398N NA NA 139.96 84.74 X X −55.22
G33713E78546N 102.16 96.13 102.16 86.03 −16.13 −10.10 −16.13
G33897E78392N NA NA 140.23 92.19 X X −48.04
G33771E78515N NA NA 126.69 94.09 X X −32.60
G33926E78341N 126.01 74.03 126.23 105.75 −20.26 31.72 −20.48
G33740E78486N 112.47 54.33 112.47 106.79 −5.68 52.46 −5.68
G33809E78456N 161.37 118.4 117.28 117.29 −44.08 −1.11 0.00
G33807E78481N 190.62 NA 193.66 119.94 −70.68 X −73.72
G33727E78553N NA NA 192.75 120.37 X X −72.38
G33797E78499N NA NA 190.88 125.53 X X −65.35
G33747E78498N 170.56 143.82 173.61 125.83 −44.73 −17.97 −47.78
G33791E78505N NA NA 201.91 143.79 X X −58.12
G33758E78524N NA NA 342.11 166.45 X X −175.66
G33782E78514N NA NA 240.01 180.08 X X −59.93
G33748E78531N NA NA 255.31 182.93 X X −72.39
G33737E78537N NA NA 306.17 206.95 X X −99.22
G33920E78352N NA 242.96 281.62 233.28 X −9.68 −48.34
G33912E78372N NA NA 325.47 243.44 X X −82.03
G33715E78523N 399.63 358.08 418.51 353.43 −46.19 −4.65 −65.08
Discussion

This analysis indicated that the glaciers in Pangong Region have been retreating like the other glaciated basins in northwestern Himalaya. However, the rate of retreat is lower since the study area is located in the Trans-Himalayan Ladakh region-a rain shadow area (Raj and Sharma, 2013; Kumar et al., 2017). Based on the existing knowledge and compared to other Trans-Himalayan glaciated regions, the area experienced the second-lowest retreat (Figure 7, Supplementary Table S1). The frontal changes of glaciers in the Pangong Region, also on the lower side, are consistent with the previously documented studies over the region (Majeed et al., 2021).

Comparison of glacier retreat of Pangong region with neighbouring areas in western Himalaya. Red bar indicates total loss (as %) and blue bar indicates loss per decade (as %).

The glacier recession in Pangong was correlated with topographic parameters and debris cover to better understand the influence of these parameters on glacier melt. This analysis indicated that the mean glacier elevation does not have any impact on the recession at higher elevations. It is pertinent to mention that the mean glacier elevation of the Pangong group of glaciers lies above ∼5,600 m asl. Analysis of mean glacier slope shows a positive correlation with glacier recession. This anomalous behavior was further analyzed by taking into consideration the frontal slope of each glacier which was generally flatter than the mean glacier slope indicating that the glaciers with flatter frontal slopes retreated faster. Analysis of debris cover indicated a weak positive correlation with glacier melt. Although there are no in-situ debris thickness data the visual analysis generally indicated a thin debris layer on the Pangong group of glaciers. It is pertinent to mention that Muhammad et al. (2020) also suggested that the thin debris layers do not influence the melting of glaciers in the high-altitude Karakoram. Glacier area showed a negative correlation with glacier recession which has been established by many previous studies (Ceballos et al., 2006; Debnath et al., 2019; Zhao et al., 2020).

While this analysis indicated that the Pangong group of glaciers thinned at a rate of 0.38 ± 0.05 m a−1 between 2000 and 2012, Abdullah et al. (2020) report a similar retreat of 0.46 m a−1 for the Pangong region. The uncertainty estimates of surface lowering and mass change in this study are higher given the fact that the ice loss is lower. Some other mass balance studies over the region suggest comparatively lesser mass changes. For instance, a study by Brun et al. (2017), who used ASTER stereo pairs for estimating the mass changes of the glaciers, suggested a loss of ∼0.5 m a−1 of glacier ice. A relatively older study by Kääb et al. (2012) also puts the ice loss estimate for this region at ∼0.5 m a−1. Vijay and Braun (2018) have put up similar estimates for the glaciers located towards the eastern part of the Pangong region. The differences in the ice loss estimates could be a manifestation of the different DEMs, analysis techniques and the time period for which the mass changes were estimated.

GlabTop model simulations indicate that out of the four glaciers associated with a proglacial lake, three lakes could expand. While the proglacial lake associated with G33723E78503N might not expand, there is a formation of a 1.06 ha lake in the midst of the ablation zone also evident on satellite data and Google Earth imagery of August 2013 and June 2016. It is pertinent to mention that the model has been tested in different glaciated regions of the European Alps (Linsbauer et al., 2012; Magnin et al., 2020) and Himalayas (Linsbauer et al., 2016; Pandit and Ramsankaran, 2020) including Trans-Himalayan Ladakh (Rashid and Majeed, 2018; Majeed et al., 2021) for ice-thickness and glacier-bed overdeepenings. Given the prevailing glacier melt scenario over the seismically-active Jammu and Kashmir region, the development of new proglacial lakes and the formation of already existing ones would pose a significant glacial lake outburst (GLOF) risk to the downstream communities and infrastructure. Studies indicate that there have already been a few incidences of GLOF (Schmidt et al., 2020) and cloudburst (Kumar et al., 2012) in the Trans-Himalayan Ladakh.

Another important aspect of this study was to compare the glacier inventory generated in this study with the existing global and regional inventories. This analysis indicated that only the GAMDAM glacier inventory captured all the glaciers that were delineated in the current effort. This could be attributed to the fact that while ICIMOD and RGI inventories have been synthesized by automatic and semiautomatic methods, the glaciers in GAMDAM inventory have been delineated manually which allows for cognitive inputs from the analyst, unlike automated methods. The overestimation in both area and number of glaciers in GAMDAM glacier inventory could be attributed to the mapping of smaller seasonal or perennial snowpacks without any proper ablation and accumulation zones having been delineated as glaciers in the region.

 Conclusion

This analysis utilized remote sensed data including satellite images and DEMs to quantify glacier recession, frontal retreat, and geodetic mass changes of the Pangong group of glaciers in Trans-Himalayan Ladakh. Satellite data analysis revealed that the glaciers retreated conservatively at 0.23% per year between 1990 and 2019 as compared to other regions of the western Himalaya. The geodetic mass change estimates for the selected glaciers indicated a surface lowering of 0.38 ± 0.05 m a−1. While clean-ice and debris-covered glaciers respectively lost 8.4 and 5.7% of the area, the surface lowering was more for debris-covered glaciers (0.43 m a−1) compared to clean-ice glaciers (0.28 m a−1). However, this needs to be researched further through extensive ground surveys aimed at collecting debris thickness measurements in the study area. It is pertinent to mention that debris-thickness measurements are absent in Trans-Himalayan Ladakh and scanty in the Indian Himalaya. Further, glacier recession was correlated with topographic parameters and debris cover which indicated that mean glacier elevation does not influence recession patterns in the Pangong region owing to the relatively higher elevation of glaciers (∼5,600 asl) in the study area. There is an inverse relation between glacier size and recession which has been widely reported in previous studies. The mean glacier slope showed a positive correlation with glacier recession indicating that the glaciers with flatter tongues are retreating faster compared to steeper ones. The glacier outlines mapped manually in this study were compared with the existing three regional/global glacier inventories (ICIMOD, GAMDAM, and RGI). Although GAMDAM glacier inventory captured most of the glaciers it overestimated the glacier area and number owing to the fact that smaller snowpacks were delineated as glaciers. The GlabTop model simulations indicated expansion of three proglacial lakes and formation of a new periglacial lake which could be potential lake outburst hotspots given that the region is experiencing a temperature warming scenario and the glaciers lie in a seismically active area. There is a potential for catastrophic glacial floods and any infrastructure development in the downstream areas should not proceed without a detailed land-use plan.

 Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

 Author Contributions

IR conceptualized and designed the study. UM, IR, NN, and NG performed data analysis. IR wrote the first draft of the manuscript. UM and NN wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

The authors are thankful to USGS for making the Landsat data freely available to users. The authors also express gratitude to Andreas Linsbauer, University of Zurich for providing the GlabTop model. The first author also acknowledges the support of Department of Science and Technology, Government of India (DST, GoI) for INSPIRE fellowship (Grant Number: IF180682) for pursuing Ph.D. The comments and suggestions from the two reviewers greatly improved the manuscript structure and content.

 Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2021.748107/full#supplementary-material

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