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

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.

FIGURE 1

FIGURE 1. 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.

TABLE 1

TABLE 1. Details of the datasets used in this study.

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 (E_AC) was calculated considering the law of error propagation as (Hall et al., 2003):

$E_{AC}=\sqrt{E_{A1}^2+E_{A2}^2}(1)$

where E_AC is the uncertainty related to the change in the area between two time periods, E_A1 is the uncertainty of glacier area at one point in time and E_A2 is the uncertainty of glacier area at the second point in time.

The uncertainties related to snout changes between two times (E_SC) is given by the following formula (Hall et al., 2003):

$E_{SC}=\sqrt{{\lambda }_1^2+{\lambda }_2^2}(2)$

where E_SC 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):

$\frac{\nabla M}{M}=\sqrt{{\left(\frac{\nabla \overline{dh}}{dh}\right)}^2+{\left(\frac{\nabla {\rho }_{ice}}{{\rho }_{ice}}\right)}^2+{\left(\frac{\nabla A}{A}\right)}^2}(3)$

where ∇ represents the uncertainty in estimates.

The uncertainty in elevation change (σ_Δh) is calculated based on the following formula Huber et al. (2020):

${\sigma }_{\Delta H}=\sqrt{{\sigma }_{dem}^2+{\sigma }_{voidfill}^2+{\sigma }_{TDXdate}^2}(4)$

where σ_em is the uncertainty in DEM, σ_voidfill is the uncertainty introduced by the void-filling approach, and σ_TDXdate 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 σ_dem was calculated after the approach suggested by McNabb et al. (2019) as:

${\sigma }_{dem}=\frac{\sqrt{{\left({\sigma }_{\Delta z }A\right)}^2+{({\sigma }_{area} \Delta z)}^2}}{A}(5)$

where σ_Δz is the mean of glacier difference between the two DEMs after coregistration, A is the glacier area, σ_area 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).

FIGURE 2

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

TABLE 2

TABLE 2. Area and frontal changes of Pangong Group of glaciers from 1990 to 2019.

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.

FIGURE 3

FIGURE 3. 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.

FIGURE 4

FIGURE 4. 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.

FIGURE 5

FIGURE 5. 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.

FIGURE 6

FIGURE 6. Comparison of glacier outlines delineated in this study with Randolph Glacier Inventory (RGI), GAMDAM glacier inventory, and ICIMOD glacier inventory.

TABLE 3

TABLE 3. Comparison of the present inventory (TS) with the existing inventories (RGI, ICIMOD and GAMDAM).

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).

FIGURE 7

FIGURE 7. 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.

Acknowledgments

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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