Abstract
The purpose of this study was to determine the glacier displacement, velocity, and thickness of seven major glaciers of Bhutan and predicted potential glacier lake formation site with its depth. We named the glacier identification (ID) number 1–7 for seven glaciers. From the study, the glacier velocity between the central trunk and snout saw rapid fluctuations in 1976–1978 with an average uncertainty velocity of ± 27.10 m/year and a decreasing velocity trend. The year 2013–2014 has the lowest uncertainty in glacier velocity, with a value of ±1.24 m/year. The glacier velocity progressively increases from the snout to the main central trunk for all the glaciers with a value of 0 to 98.63 m/year. The glacier with the highest average velocity is glacier ID 5, which has a velocity of 25.58 m/year. From 2000 and 2022, all of the glaciers’ thicknesses significantly decreased from 0 to 468.2 m. The thickness of glacier ID 6 was lowered by −192.3 ± 1.99 m, making it the highest among the seven glaciers. In the future, a glacier lake is predicted to form at the base of each glacier. Glacier ID 6 is predicted to form the largest lake with a surface area of 2.572 km2 and a depth of 208.5 m.
HIGHLIGHTS
The paper is related to glacier dynamics in Bhutan.
Research related to glacier dynamics was never published until now.
This paper presents glacier displacement, velocity, and thickness change over the year.
This paper also presents predicted potential glacier lake formation sites with its possible depth.
Seven largest glaciers of Bhutan were considered as a study area for this study.
INTRODUCTION
Glaciers are a piece of huge ice moving over land mass due to their weight and steep underlying terrain (Shreve 1985). Glaciers are normally found in the high mountains and polar regions of the world. The glaciers are retreating or advancing owing to ablations and accumulation (Hooker & Fitzharris 1999). A gradual retreat may result in the disappearance of the glacier in the future (Paul et al. 2007). According to numerous studies, the majority of the world's glaciers are significantly shrinking as a result of the increase in average global temperature (Dyurgerov & Meier 2000). The melting of the glaciers might lead to environmental and economic problems at local, regional, continental, and even global scales (Haeberli & Weingartner 2020).
Glaciers hold 70% of the freshwater worldwide (Hasnain 1999). The Himalayan Mountain is also known as the third pole and is the home of many glaciers. Many rivers in South Asian countries are fed by snow and glaciers in the Himalayan region (Khajuria et al. 2022). The rivers that originated from the glaciers are crucial for economic growth (Lal 2000). Nonetheless, the glaciers are undergoing several changes, including recession, the formation of glacier lakes, a decrease in thickness, and changes to the glacial mass balance (Wang et al. 2013). Similarly, global warming is causing the Himalayan glaciers to retreat at an alarming rate. Global warming-induced glacier retreat may result in dwindling water supplies and an increase in the danger of glacier lake outburst flood (Hasnain 1999). Kumar et al. (2022) stated a 2 °C rise in temperature led to a 53% increase in catchment discharge in the Himalayan region. The knowledge of the dimensions of Himalayan glaciers and their behavior in response to climate change is still limited, due to their remoteness, inaccessibility, and harsh topography (Momblanch et al. 2019). The advancement of remote sensing, digital elevation models, and geographic information systems (GIS) provides an effective approach to assessing and quantifying glacier changes. Remote sensing also plays an instrumental role in tracking glacier geometry, study of glacial lake formation, determination of equilibrium line altitude (ELA) (Garg et al. 2022), monitoring annual mass balance changes, and estimating various movement rates (Racoviteanu et al. 2008).
Glaciers in Bhutan constitute about 1.64% (630 sq. km) of the total area (NCHM 2019). As per the Bhutan Glacier Inventory 2018, there are 700 glaciers in Bhutan (NCHM 2019). The Bhutanese national newspaper Kuensel reported that 1,146.16 million tons of glacier ice are lost every year (Dema 2022). The south-facing glaciers encounter increasing retreats every year compared to north-facing glaciers (Iwata 2010). Bhutanese glaciers are very important for economic development such as hydropower generation, irrigation, and agriculture. Concerns regarding the melting of glaciers in Bhutan have been expressed by climate experts and decision-makers. However, despite these concerns, there is a lack of concrete evidence regarding glacial recession. This knowledge gap underscores the need for comprehensive research on glacier dynamics to address the lack of empirical data. While the significance of glaciers in Bhutan has been acknowledged through verbal discussions, there is a notable scarcity of publications focusing on the dynamics of glaciers, such as glacier velocity, thickness, and potential glacier lakes. Only a few studies exist that cover glacier velocity in Bhutan, including Luggye glaciers (16 m/year) in 1988–1993 (Ageta & Iwata 1998), Tarina Glacier (30–35 m/year) in 1976–1998 (Ageta & Iwata 1998), and Gangju La glacier (17 m/year) in 2013–2014 (Tshering & Fujita 2016). However, no prior research has specifically investigated the dynamics of glaciers in Bhutan. Furthermore, the challenging terrain characterized by ruggedness, high elevation, large area, and severe weather conditions makes traditional field-based techniques for studying glacier dynamics impractical. In light of these challenges, the use of satellite data and GIS is presented as the most feasible and practical method for studying such complex terrains. To address these gaps and challenges, this study aims to calculate glacier displacement and velocity over the period from 1976 to 2022, analyze variations in glacier thickness between 2000 and 2022, and identify potential sites for glacier lake formation using remote sensing data combined with GIS techniques. The research aims to provide valuable insights for policymakers, enabling them to make informed decisions regarding glacier-related concerns. Additionally, the study result is expected to serve as a warning system for downstream communities who may be affected by glacier-related hazards.
MATERIALS AND METHODS
Study area
Glacier ID . | Location . | GLIMS_ID . | Basin . | Elevation (m) . | Slope (degree) . | Area (km2) . | |||
---|---|---|---|---|---|---|---|---|---|
Latitude . | Longitude . | Minimum . | Maximum . | Minimum . | Maximum . | ||||
1 | 28.133 | 89.996 | G089996E28133N | Phochu | 4,386 | 7,084 | 0 | 69 | 28.07 |
2 | 28.125 | 90.161 | G090161E28125N | Phochu | 4,085 | 6,663 | 0 | 74 | 36.55 |
3 | 28.116 | 90.081 | G090081E28116N | Phochu | 4,155 | 6,485 | 0 | 61 | 10.56 |
4 | 28.005 | 90.476 | G090476E28005N | Mangdechu | 4,679 | 6,706 | 0 | 54 | 14.71 |
5 | 28.022 | 90.42 | G090420E28022N | Mangdechu | 4,943 | 7,231 | 1 | 69 | 20.252 |
6 | 28.031 | 90.521 | G090521E28031N | Chamkhar | 4,700 | 6,171 | 0 | 63 | 15.58 |
7 | 28.026 | 90.786 | G090786E28026N | Chamkhar | 4,864 | 5,886 | 0 | 56 | 5.84 |
Glacier ID . | Location . | GLIMS_ID . | Basin . | Elevation (m) . | Slope (degree) . | Area (km2) . | |||
---|---|---|---|---|---|---|---|---|---|
Latitude . | Longitude . | Minimum . | Maximum . | Minimum . | Maximum . | ||||
1 | 28.133 | 89.996 | G089996E28133N | Phochu | 4,386 | 7,084 | 0 | 69 | 28.07 |
2 | 28.125 | 90.161 | G090161E28125N | Phochu | 4,085 | 6,663 | 0 | 74 | 36.55 |
3 | 28.116 | 90.081 | G090081E28116N | Phochu | 4,155 | 6,485 | 0 | 61 | 10.56 |
4 | 28.005 | 90.476 | G090476E28005N | Mangdechu | 4,679 | 6,706 | 0 | 54 | 14.71 |
5 | 28.022 | 90.42 | G090420E28022N | Mangdechu | 4,943 | 7,231 | 1 | 69 | 20.252 |
6 | 28.031 | 90.521 | G090521E28031N | Chamkhar | 4,700 | 6,171 | 0 | 63 | 15.58 |
7 | 28.026 | 90.786 | G090786E28026N | Chamkhar | 4,864 | 5,886 | 0 | 56 | 5.84 |
Datasets used
The Landsat datasets were downloaded from USGS earth explorer for Landsat 2 MSS, Landsat 5 TM, Landsat 7 ETM, and Landsat 8 OLI from 1976 to 2022. September to November are the optimum months to use Landsat images for glacier studies since there is little cloud cover, less seasonal snow cover, and the glaciers are fully exposed to their actual positions. The best images that are available for this study span from September 30 to December 17 as listed in Table 2. The Landsat images were used to derive glacier displacement and velocity while SRTM DEM and Copernicus DEM were used to derive glacier thickness in conjunction with the derived velocity. ICIMOD Glacier Inventory 2010 shapefile was downloaded from ICIMOD regional database (http://rds.icimod.org/Home/). The glacier inventory was used to extract the study area.
Sensor . | Date . | Row . | Path . | Band used . | Resolution (m) . |
---|---|---|---|---|---|
Landsat 8 OLI | 19-11-2021 | 41 | 138 | Pan | 15 |
Landsat 8 OLI | 21-10-2022 | Pan | 15 | ||
Landsat 8 OLI | 12-10-2013 | Pan | 15 | ||
Landsat 8 OLI | 21-10-2014 | Pan | 15 | ||
Landsat 7 ETM + | 30-09-2000 | Pan | 15 | ||
Landsat 7 ETM + | 07-11-2002 | Pan | 15 | ||
Landsat 5 TM | 09-11-1994 | Red | 30 | ||
Landsat 5 TM | 30-11-1996 | Red | 30 | ||
Landsat 5 TM | 11-11-1989 | Red | 30 | ||
Landsat 5 TM | 14-11-1990 | Red | 30 | ||
Landsat 2 MSS | 17-12-1976 | Red | 60 | ||
Landsat 2 MSS | 07-12-1978 | Red | 60 |
Sensor . | Date . | Row . | Path . | Band used . | Resolution (m) . |
---|---|---|---|---|---|
Landsat 8 OLI | 19-11-2021 | 41 | 138 | Pan | 15 |
Landsat 8 OLI | 21-10-2022 | Pan | 15 | ||
Landsat 8 OLI | 12-10-2013 | Pan | 15 | ||
Landsat 8 OLI | 21-10-2014 | Pan | 15 | ||
Landsat 7 ETM + | 30-09-2000 | Pan | 15 | ||
Landsat 7 ETM + | 07-11-2002 | Pan | 15 | ||
Landsat 5 TM | 09-11-1994 | Red | 30 | ||
Landsat 5 TM | 30-11-1996 | Red | 30 | ||
Landsat 5 TM | 11-11-1989 | Red | 30 | ||
Landsat 5 TM | 14-11-1990 | Red | 30 | ||
Landsat 2 MSS | 17-12-1976 | Red | 60 | ||
Landsat 2 MSS | 07-12-1978 | Red | 60 |
Estimation of glacier displacement and velocity using an optical feature tracking method
Glacier surface displacement was calculated using the sub-pixel correlation of a pair of Landsat images on a sliding window of a phase plane. The pair of Landsat images consist of pre- and post-event images with low snow and cloud coverage. The only limitation of the feature tracking method is that it used optical satellite images and the cloud coverage may result in misinterpretation of the images. Therefore, this study used Landsat images with cloud coverage less than 10%. The correlation of Landsat images is estimated using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr). The COSI-Corr is freely available from http://www.tectonics.caltech.edu/ and it is patched in ENVI software. In the COSI-CORR, the two images are iteratively cross-correlated with an initial window size of 64 and a final window size of 32 pixels. A total of four iterations were conducted. Three outputs were obtained, namely: east-west displacement (E-W), north-south displacement (N-S), and signal-to-noise ratio (SNR). As per Gopika et al. (2021), SNR is used to estimate the correlation accuracy, and all the pixels with SNR values less than 0.9 are considered erroneous. Therefore, SNR values less than 0.9 are discarded in this study. The remaining pixels can be used for the calculation of horizontal and vertical displacements.
Estimation of glacier thickness and its bed topography
To estimate the glacier thickness, SRTM DEM of 2000 and Copernicus DEM of 2019 were used. The DEMs were reclassified into 100 m contour intervals and converted to contour lines with a vertical contour interval of 100 m. Glacier boundary and moraine are also required to find the glacier thickness. The flowlines were digitized along the central trunks of the glaciers. All the datasets were projected to UTM45N. The projected datasets were inserted into HIGHTHIM python package to estimate the glacier thickness.
Uncertainties of glacier velocity and thickness
RESULTS AND DISCUSSION
Glacier displacement and velocity
For the years 2001–2022, the maximum displacement and velocity are located in the upper central trunk with a value of 89.63 m and 98.63 m/year, respectively. The displacement and velocity gradually decrease toward the glacier terminus. The findings of our study on glacier displacement and velocity are consistent with previous research conducted on Himalayan glaciers by Gantayat et al. (2014) and Singh et al. (2021).
Table 3 shows the detailed comparison of individual glacier velocities. The average velocity for individual glaciers was calculated by averaging different year glacier velocities. The average velocity for individual glaciers was calculated to compare the degree of glacier dynamics between the different glaciers. From Table 3, it is observed that glacier ID 5 undergone the highest glacier average velocity (25.58 m/year), followed by glacier ID 1 (19.62 m/year), glacier ID 3 (18.28 m/year), glacier ID 2 (15.56 m/year), and glacier ID 6 (13.44 m/year) and it indicates these five glaciers are undergoing tremendous glacier change between 1976 and 2022. Glacier ID 7 and glacier ID 4 are undergoing the least alteration with an average velocity of 7.70 and 9.19 m/year, respectively.
Glacier ID . | Image acquisition date . | Time interval (days) . | Displacement (m) . | Velocity (m/year) . | Average velocity (1976–2022) . |
---|---|---|---|---|---|
1 | 17/12/1976–7/12/1978 | 720 | 43.66 | 47.86 | 19.62 |
11/11/1989–14/11/1990 | 368 | 20.76 | 19.73 | ||
9/11/1994–30/11/1996 | 751 | 27.94 | 13.28 | ||
30/9/2000–7/11/2002 | 768 | 20.19 | 9.81 | ||
12/10/2013–31/10/2014 | 384 | 20.21 | 20.05 | ||
19/11/2021–21/10/2022 | 333 | 13.81 | 7.00 | ||
2 | 17/12/1976–7/12/1978 | 720 | 45.49 | 23.06 | 15.56 |
11/11/1989–14/11/1990 | 368 | 19.98 | 19.82 | ||
9/11/1994–30/11/1996 | 751 | 22.10 | 10.74 | ||
30/9/2000–7/11/2002 | 768 | 31.67 | 15.05 | ||
12/10/2013–31/10/2014 | 384 | 14.34 | 13.63 | ||
19/11/2021–21/10/2022 | 333 | 10.11 | 11.08 | ||
3 | 17/12/1976–7/12/1978 | 720 | 39.07 | 19.81 | 18.28 |
11/11/1989–14/11/1990 | 368 | 23.16 | 22.97 | ||
9/11/1994–30/11/1996 | 751 | 25.85 | 12.56 | ||
30/9/2000–7/11/2002 | 768 | 28.42 | 13.51 | ||
12/10/2013–31/10/2014 | 384 | 20.63 | 19.61 | ||
19/11/2021–21/10/2022 | 333 | 19.37 | 21.23 | ||
4 | 17/12/1976–7/12/1978 | 720 | 37.65 | 19.09 | 9.19 |
11/11/1989–14/11/1990 | 368 | 11.79 | 11.69 | ||
9/11/1994–30/11/1996 | 751 | 13.26 | 6.44 | ||
30/9/2000–7/11/2002 | 768 | 13.06 | 6.21 | ||
12/10/2013–31/10/2014 | 384 | 7.74 | 7.36 | ||
19/11/2021–21/10/2022 | 333 | 3.96 | 4.34 | ||
5 | 17/12/1976–7/12/1978 | 720 | 34.47 | 17.47 | 25.58 |
11/11/1989–14/11/1990 | 368 | 35.21 | 34.92 | ||
9/11/1994–30/11/1996 | 751 | 40.13 | 19.50 | ||
30/9/2000–7/11/2002 | 768 | 42.43 | 20.17 | ||
12/10/2013–31/10/2014 | 384 | 33.52 | 31.86 | ||
19/11/2021–21/10/2022 | 333 | 26.94 | 29.53 | ||
6 | 17/12/1976–7/12/1978 | 720 | 26.44 | 13.40 | 13.44 |
11/11/1989–14/11/1990 | 368 | 18.92 | 18.77 | ||
9/11/1994–30/11/1996 | 751 | 27.14 | 13.19 | ||
30/9/2000–7/11/2002 | 768 | 25.71 | 12.22 | ||
12/10/2013–31/10/2014 | 384 | 12.67 | 12.04 | ||
19/11/2021–21/10/2022 | 333 | 10.03 | 10.99 | ||
7 | 17/12/1976–7/12/1978 | 720 | 25.19 | 12.77 | 7.70 |
11/11/1989–14/11/1990 | 368 | 11.58 | 11.49 | ||
9/11/1994–30/11/1996 | 751 | 12.60 | 6.12 | ||
30/9/2000–7/11/2002 | 768 | 13.73 | 6.53 | ||
12/10/2013–31/10/2014 | 384 | 6.69 | 6.36 | ||
19/11/2021–21/10/2022 | 333 | 2.68 | 2.94 |
Glacier ID . | Image acquisition date . | Time interval (days) . | Displacement (m) . | Velocity (m/year) . | Average velocity (1976–2022) . |
---|---|---|---|---|---|
1 | 17/12/1976–7/12/1978 | 720 | 43.66 | 47.86 | 19.62 |
11/11/1989–14/11/1990 | 368 | 20.76 | 19.73 | ||
9/11/1994–30/11/1996 | 751 | 27.94 | 13.28 | ||
30/9/2000–7/11/2002 | 768 | 20.19 | 9.81 | ||
12/10/2013–31/10/2014 | 384 | 20.21 | 20.05 | ||
19/11/2021–21/10/2022 | 333 | 13.81 | 7.00 | ||
2 | 17/12/1976–7/12/1978 | 720 | 45.49 | 23.06 | 15.56 |
11/11/1989–14/11/1990 | 368 | 19.98 | 19.82 | ||
9/11/1994–30/11/1996 | 751 | 22.10 | 10.74 | ||
30/9/2000–7/11/2002 | 768 | 31.67 | 15.05 | ||
12/10/2013–31/10/2014 | 384 | 14.34 | 13.63 | ||
19/11/2021–21/10/2022 | 333 | 10.11 | 11.08 | ||
3 | 17/12/1976–7/12/1978 | 720 | 39.07 | 19.81 | 18.28 |
11/11/1989–14/11/1990 | 368 | 23.16 | 22.97 | ||
9/11/1994–30/11/1996 | 751 | 25.85 | 12.56 | ||
30/9/2000–7/11/2002 | 768 | 28.42 | 13.51 | ||
12/10/2013–31/10/2014 | 384 | 20.63 | 19.61 | ||
19/11/2021–21/10/2022 | 333 | 19.37 | 21.23 | ||
4 | 17/12/1976–7/12/1978 | 720 | 37.65 | 19.09 | 9.19 |
11/11/1989–14/11/1990 | 368 | 11.79 | 11.69 | ||
9/11/1994–30/11/1996 | 751 | 13.26 | 6.44 | ||
30/9/2000–7/11/2002 | 768 | 13.06 | 6.21 | ||
12/10/2013–31/10/2014 | 384 | 7.74 | 7.36 | ||
19/11/2021–21/10/2022 | 333 | 3.96 | 4.34 | ||
5 | 17/12/1976–7/12/1978 | 720 | 34.47 | 17.47 | 25.58 |
11/11/1989–14/11/1990 | 368 | 35.21 | 34.92 | ||
9/11/1994–30/11/1996 | 751 | 40.13 | 19.50 | ||
30/9/2000–7/11/2002 | 768 | 42.43 | 20.17 | ||
12/10/2013–31/10/2014 | 384 | 33.52 | 31.86 | ||
19/11/2021–21/10/2022 | 333 | 26.94 | 29.53 | ||
6 | 17/12/1976–7/12/1978 | 720 | 26.44 | 13.40 | 13.44 |
11/11/1989–14/11/1990 | 368 | 18.92 | 18.77 | ||
9/11/1994–30/11/1996 | 751 | 27.14 | 13.19 | ||
30/9/2000–7/11/2002 | 768 | 25.71 | 12.22 | ||
12/10/2013–31/10/2014 | 384 | 12.67 | 12.04 | ||
19/11/2021–21/10/2022 | 333 | 10.03 | 10.99 | ||
7 | 17/12/1976–7/12/1978 | 720 | 25.19 | 12.77 | 7.70 |
11/11/1989–14/11/1990 | 368 | 11.58 | 11.49 | ||
9/11/1994–30/11/1996 | 751 | 12.60 | 6.12 | ||
30/9/2000–7/11/2002 | 768 | 13.73 | 6.53 | ||
12/10/2013–31/10/2014 | 384 | 6.69 | 6.36 | ||
19/11/2021–21/10/2022 | 333 | 2.68 | 2.94 |
Uncertainty of glacier velocity
The overall uncertainty of glacier velocity was calculated using Equation (6) for all seven glaciers, and the results are presented in Table 4. The velocity uncertainties vary across different periods, reflecting the quality and availability of data. In the year 1976–1978, a higher velocity uncertainty of ±27.10 m/year is observed, followed by 5.78 m/year in 1989–1990, 2.57 m/year in 1994–1996, 2.08 m/year in 2000–2002, 1.24 m/year in 2013–2014, and 2.15 m/year in the year 2021–2022. This can be attributed to several factors, including the limited availability of data during that period and the coarser spatial resolution compared to more recent Landsat data.
Sl No . | Year . | MED . | SD . | No. of points . | SE (m/year) . | Error (m/year) . |
---|---|---|---|---|---|---|
(m/year) . | (m/year) . | |||||
1 | 1976–1978 | 25.87 | 98.98 | 135 | 8.08 | 27.10 |
2 | 1989–1990 | 5.76 | 4.46 | 150 | 0.36 | 5.78 |
3 | 1994–1996 | 2.56 | 2.94 | 150 | 0.24 | 2.57 |
4 | 2000–2022 | 2.07 | 1.20 | 150 | 0.10 | 2.08 |
5 | 2013–2014 | 1.24 | 0.92 | 150 | 0.08 | 1.24 |
6 | 2021–2022 | 2.15 | 2.15 | 150 | 0.18 | 2.15 |
Sl No . | Year . | MED . | SD . | No. of points . | SE (m/year) . | Error (m/year) . |
---|---|---|---|---|---|---|
(m/year) . | (m/year) . | |||||
1 | 1976–1978 | 25.87 | 98.98 | 135 | 8.08 | 27.10 |
2 | 1989–1990 | 5.76 | 4.46 | 150 | 0.36 | 5.78 |
3 | 1994–1996 | 2.56 | 2.94 | 150 | 0.24 | 2.57 |
4 | 2000–2022 | 2.07 | 1.20 | 150 | 0.10 | 2.08 |
5 | 2013–2014 | 1.24 | 0.92 | 150 | 0.08 | 1.24 |
6 | 2021–2022 | 2.15 | 2.15 | 150 | 0.18 | 2.15 |
The graph (Figure 4) and Table 3 clearly demonstrate the abruptness in estimated velocity and the uncertainty of velocity during the years 1976–1978. It is important to note that the reliability of the data during this period may be compromised due to limited availability and lower quality. The spatial resolution of the data used for that time period might have been coarser, further contributing to the higher uncertainty observed.
To improve the accuracy and reduce uncertainty in glacier velocity estimation, it is crucial to utilize high-quality and high-resolution data. Recent advancements in remote sensing technology, such as the availability of higher-resolution satellite imagery, can provide more precise measurements and reduce uncertainties in glacier velocity calculations.
Estimation of glacier thickness
Glacier bed topography and prediction of future glacier formation sites
The formation of glacier lakes is influenced by factors such as glacier recession and the topography of the glacier's base. Remya et al. (2019) observed that glacier lakes typically arise when glaciers recede and the bottom topography becomes overdeepened. The size and depth of the glacier lake vary depending on the depth of the depression in the bed topography and the size of the glacier (Sergienko 2013).
Glacier lakes that are adjacent to a glacier's toe can accelerate local glacier melt (Nick et al. 2010). However, the presence of glacier lakes also poses a significant risk of glacier lake outburst, which is a major disaster threat downstream (Bhushan et al. 2017). Therefore, it is crucial to monitor the development of glacier lakes to provide early warnings to downstream residents and enable adequate preparation.
In this study, it is anticipated that glacier lakes will form in the lower reaches of all seven glaciers examined. Based on the findings presented in Table 5, glacier ID 6 is expected to give rise to the largest glacier lake, with an area of 2.572 km2 and a depth of 208.5 m. On the other hand, glacier ID 2 is expected to form a smaller lake, with an area of 0.194 km2 and a depth of 41.3 m.
Glacier ID . | Predicted area of the glacier lake (km2) . | Predicted maximum depth of the glacier lake (m) . |
---|---|---|
1 | 0.863 | 135.7 |
2 | 0.194 | 41.3 |
3 | 0.563 | 143.3 |
4 | 0.413 | 71.4 |
5 | 1.348 | 80.3 |
6 | 2.572 | 208.5 |
7 | 0.550 | 122.4 |
Glacier ID . | Predicted area of the glacier lake (km2) . | Predicted maximum depth of the glacier lake (m) . |
---|---|---|
1 | 0.863 | 135.7 |
2 | 0.194 | 41.3 |
3 | 0.563 | 143.3 |
4 | 0.413 | 71.4 |
5 | 1.348 | 80.3 |
6 | 2.572 | 208.5 |
7 | 0.550 | 122.4 |
To validate these expectations, a comparison was made between the anticipated glacier lake and panchromatic and Google images. This comparison revealed the presence of previously discovered small lake patches, suggesting that a large lake will indeed form in the future.
Validation of glacier velocity
In the absence of high-resolution data on glacier depth, the validation of glacier thickness was not conducted in this study. However, global data for glacier velocity was available and downloaded from the website https://nsidc.org/apps/itslive/. Table 6 presents the global velocity data alongside the estimated velocity derived from the current study.
Glacier ID . | Estimated velocity . | Global velocity . |
---|---|---|
1 | 16.92 | 14.58 |
2 | 15.56 | 9.99 |
3 | 18.28 | 16.68 |
4 | 9.2 | 5.97 |
5 | 25.58 | 33.22 |
6 | 13.44 | 8.5 |
7 | 7.7 | 2.98 |
Glacier ID . | Estimated velocity . | Global velocity . |
---|---|---|
1 | 16.92 | 14.58 |
2 | 15.56 | 9.99 |
3 | 18.28 | 16.68 |
4 | 9.2 | 5.97 |
5 | 25.58 | 33.22 |
6 | 13.44 | 8.5 |
7 | 7.7 | 2.98 |
Furthermore, the Pearson correlation coefficient was calculated using Equation (8) to quantify the correlation between the estimated and global velocities. The obtained correlation coefficient was found to be 0.965, which is close to 1. This high correlation coefficient indicates a strong relationship between the estimated and global glacier velocities, providing additional evidence that the estimated velocity is accurate.
CONCLUSION
Glaciers are sources of many rivers in Bhutan that are used for hydropower generation, agriculture, industry, and water for consumption. Apart from Bhutan, some parts of India also benefit from rivers that flow from Bhutan. The glaciers are important for the Bhutanese economy but its undergoing tremendous changes due to climate change. Yet, there is no concrete evidence that climate change is causing the glaciers to retreat. This study offers small evidence of glacier alteration in Bhutan's seven largest glaciers. Since there are no names for these glaciers in any database or papers, we named these glaciers from glacier ID 1 to glacier ID 7. The study presented the glacier changes in terms of displacement, velocity, and thickness. This study also covers predicted future possible glacier lake formation sites with their depth.
From the study, it was found that the glaciers are undergoing major changes in velocity in 1976–1978, and the effect of the change is decelerated in recent times. The speed of glaciers increases from the lower reach to the main central trunk for all the glaciers. Among the seven glaciers, glacier ID 5 is undergoing major changes in terms of speed. It is also noted the glaciers shrunk considerably in thickness between 2000 and 2022. Glacier ID 6 has undergone a maximum decline in its thickness with a reduction of −192.3 m between 2000 and 2022. From the study, it is observed that all the glaciers will form a glacier lake at its base. Glacier ID 6 is expected to form the largest lake with an area of 2.572 km2 and 208.5 m depth.
The next phase of the study is to study the variation of ELA and glacier mass balance. The annual ELA and annual glacier mass balance will give information on the health of the glaciers in the mountains. It is also recommended to do the study on glacier lake outburst flood risk assessment of the potentially dangerous glacier lake in Bhutan.
ACKNOWLEDGEMENTS
The authors are thankful to the Indian Science and Research Fellowship for sponsoring 3 months fellowship and the Indian Institute of Remote Sensing for hosting 3 months to do research. Their gratitude also goes to the National Centre for Hydrology and Meteorology, Bhutan, and the International Centre for Integrated Mountain Development (ICIMOD) for sharing glacier inventory, earth explorer for providing free Landsat data, and https://nsidc.org/apps/itslive/ for proving free dataset for global glacier velocity. The authors also thank organizations and individuals who developed and shared COSI-Corr and HIGTHIM software. The principal author also would like to sincerely thank Dr Praveen K. Thakur for his guidance throughout the research period.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.