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Article

Changes in Glaciers and Glacial Lakes in the Bosula Mountain Range, Southeast Tibet, over the past Two Decades

by
Jia Li
1,2,*,
Yunyang Gu
1,2,
Lixin Wu
1,2,
Lei Guo
1,2,
Haodong Xu
1,2 and
Zelang Miao
1,2
1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Laboratory of Geohazards Perception, Cognition and Prediction, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(15), 3792; https://doi.org/10.3390/rs14153792
Submission received: 18 June 2022 / Revised: 25 July 2022 / Accepted: 4 August 2022 / Published: 6 August 2022
Browse Figures
Figure 1
<p>Location and overview of the study area. The background of the main panel is the Sentinel-2 image acquired on 12 October 2021. The red rectangle in the inset panel denotes the location of the study area. Dashed red rectangles R1–R4 in the main panel denote the areas shown in <a href="#remotesensing-14-03792-f002" class="html-fig">Figure 2</a>a–d, respectively. Numbers 1–13 mark the glaciers mentioned in the text.</p> "> Figure 2
<p>Outlines of glaciers and moraine-dammed glacial lakes in 2000, 2013 and 2021. The location of areas shown in subfigures (<b>a</b>–<b>d</b>) is denoted by the dashed red rectangles R1–R4 in <a href="#remotesensing-14-03792-f001" class="html-fig">Figure 1</a>, respectively. Numbers over glacier areas mark the glaciers mentioned in the text. The background is the Sentinel-2 image acquired on 12 October 2021.</p> "> Figure 3
<p>Derived glacier surface elevation change rates in three observation periods. The observation period is marked at the upper-middle part of the subfigure. Black curves denote glacier outlines. Numbers over glacier areas marked the glaciers mentioned in the text. Insert panels in (<b>c</b>) are the zoomed-in view of the areas within black dotted rectangles. The background of (<b>a</b>–<b>c</b>) is the Landsat−8/OLI image acquired on 6 October 2015.</p> "> Figure 4
<p>(<b>a</b>) Glacier area and average glacier elevation change rate (AGECR) in each 50 m altitude band, and (<b>b</b>) the uncertainty of corresponding AGECR. The AGECRs in different observation periods are denoted by different symbols. The uncertainty of AGECR in each altitude band was estimated based on the NMAD of the elevation change rate in stable areas (see <a href="#sec4dot4-remotesensing-14-03792" class="html-sec">Section 4.4</a>). The last two altitude bands have no elevation change rate observations in stable areas, and the uncertainties of the AGECRs in these two altitude bands were assumed to be the same as that of the last but two.</p> "> Figure 5
<p>The derived two-dimensional glacier flow velocity during 2014–2021 (<b>a</b>–<b>h</b>), and the ground surface slope (<b>i</b>). The black curves are glacier boundaries. The red numbers in (<b>a</b>) mark the glaciers mentioned in the text. Rectangles A1–A4 in (<b>h</b>) mark the areas where glacier flow velocities in each observation period were averaged (see <a href="#remotesensing-14-03792-t003" class="html-table">Table 3</a>). The background of (<b>a</b>–<b>h</b>) is the Sentinel-2 image acquired on 12 October 2021.</p> "> Figure 6
<p>Development of glacial lakes surrounding glacier 4. The backgrounds of (<b>a</b>–<b>d</b>) are Landsat-5 images. The acquisition dates of the images are marked on the top of the panels. Black curves denote glacier outlines. Readers can find the location of the shown area by referring to the location of glacier 4 in <a href="#remotesensing-14-03792-f001" class="html-fig">Figure 1</a>.</p> "> Figure 7
<p>Development of the glacial lake ahead of glacier 2. The backgrounds of (<b>a</b>–<b>d</b>) are Landsat-7, Landsat-5, Landsat-5, and Landsat-8 images, respectively. The acquisition dates of the images are marked on the top of the panels. Black curves denote glacier outlines. Readers can find the location of the shown area by referring to the location of glacier 2 in <a href="#remotesensing-14-03792-f001" class="html-fig">Figure 1</a>.</p> ">
Versions Notes

Abstract

:
Glaciers and glacial lakes in the Bosula Mountain Range need special attention, because their instability may cause disastrous consequences to the downstream settlements and the Sichuan-Tibet Road. The latter is a pivotal traffic line in the Southeast Tibetan Plateau. In order to investigate the state of glaciers and glacial lakes in the Bosula Mountain Range, we estimated the changes in glacier/glacial lake boundaries, glacier surface elevation, and glacier flow velocity between 2000 and 2021 based on multisource remote sensing data. Our results showed that, from the period 2000–2013 to the period 2013–2021, the average shrinking rate of glacier area increased from 0.99 km2/a to 1.74 km2/a, and the average expanding rate of glacial lake area increased from 0.04 km2/a to 0.06 km2/a. From the period 1990–2011 to the period 2015–2019, the average thinning rate of glaciers increased from 0.83 m/a to 1.58 m/a. These results indicate the Bosula Mountain Range is one of the fastest melting glacierized regions in the High Mountain Asia, and the factors that account for this may include quick temperature rise, abundant summer rainfall, and thin debris cover. In spite of strong ice melting, the observed changes in glacier boundaries, surface elevation, and flow velocity show no sign of surge activity, and the frequency of glacier lake outburst has not increased since 1989. Currently, three proglacial lakes that expanded quickly during 2000–2021 are now prominent hazards. They are directly threatened by accidental ice calving and ice avalanche, and their outburst could cause considerable damage to the downstream settlements and the Sichuan-Tibet Road.

1. Introduction

The Southeast Tibetan Plateau hosts three magnificent, heavily glaciated mountain ranges, namely the Eastern Himalayas, Nyainqntanglha, and Hengduan. The Bosula is a secondary mountain range of the Hengduan Mountain Range. Glaciers in the Bosula Mountain Range deserve particular attention because they are located close to the critical part of the Sichuan–Tibet Road (Chinese National Road No. 318). Due to the extremely rough terrain and hostile environment in the Southeast Tibetan Plateau, the Sichuan–Tibet Road is currently viewed as a life-line that connects the towns in Southeast Tibet and those in the low-lying areas of China. The Sichuan–Tibet Road crosses the Bosula Mountain Range from north to south, and then extends towards the northwest along the Parlung Zangbo River. If this road were to be damaged by a glacial disaster or outburst flood, it would cause a serious issue for access to Southeast Tibet. The alternative route connecting Southeast Tibet and the cities in Sichuan Province (a province adjacent to Tibet) is thrice as long. Hence, the part of the Sichuan–Tibet Road near the Bosula Mountain Range is critical. Investigating the state of glaciers and glacial lakes in this region is of great significance.
Many moraine-dammed glacial lakes have developed in the Bosula Mountain Range [1]. The outburst of moraine-dammed glacial lakes is a common cause of mountain hazards. Due to the sharp altitude drop in the Bosula Mountain Range, once a glacial lake outburst flood (GLOF) takes place, the moving debris flow substances will have high gravitational potential energy. The studied glacierized area is half surrounded by the Sichuan–Tibet Road (Figure 1). No matter where the GLOF takes place, the Sichuan–Tibet Road can be affected. When the lake’s water level rises to a certain degree, a glacier surge can easily trigger the breach of the moraine dam and induce a GLOF [2]. Previously scholars established the ‘hydrological-control’ glacier surge trigger mechanism hypothesis, which proposes that glacier basal slip occurs after the water pressure in the distributed subglacial cavities rises to a certain level [3,4]. Glaciers in the Bosula Mountain Range, Southeast Tibetan Plateau belong to the maritime type [5]. For maritime glaciers, their basal temperature is at the melting point and surface meltwater can survive at the base [6]. Technically, the increase in the amount of meltwater stored at the glacier’s base will promote glacier surge. Hence, the incidence of glacier surge events in the Bosula Mountain Range is possibly elevated by intensified glacier ablation. It should be noted that not all glacier surge activities will induce glacier advance. Nevertheless, after the glacier surge activity causes a great volume of ice to accumulate at the middle or lower reaches, ice collapse that can trigger the breach of the moraine dam in a more violent way may happen subsequently [7].
It is generally known that monitoring the changes in glacier boundaries, surface elevation, and flow velocity can provide key information for judging the stability of glaciers. The geodetic method using DEM differencing offers glacier surface elevation change information with fine resolution, wide coverage, and satisfying accuracy. Recent global and large-scale regional geodetic glacier surface elevation change studies have covered the Bosula Mountain Range [8,9,10,11]. However, the reported results are described on a regional basis, and do not include a detailed analysis. For judging the stability of glaciers, the fine-scale region-wide or glacier-wide glacier surface elevation change is preferable. Furthermore, the knowledge on recent glacier flow velocity, glacier boundaries, and glacial lake boundaries is also limited in this region. In view of the above, we estimated the changes of glacier surface elevation during three periods (2000–2011, 2011–2015, and 2015–2019) through the geodetic method, derived the glacier flow velocity during 2014–2021 through the optical feature-tracking technique, and delineated the glacier outlines and moraine-dammed glacial lake outlines in 2000, 2013, and 2021. Our study sheds light on the recent state of glaciers and moraine-dammed glacial lakes in the Bosula Mountain Range, facilitating the hazard assessment of glaciers and glacial lakes in this region.

2. Study Area

The Bosula Mountain Range lies in the northwest–southeast direction (Figure 1). Due to its higher altitude, the western part of the Bosula Mountain Range has developed larger glaciers than the eastern part. Glaciers in the western part form two glacial centers; this study focuses on the eastern one, which is located closer to the Sichuan–Tibet Road. In October 2021, the study area had 160 glaciers and 83 moraine-dammed glacial lakes, occupying an area of 126.20 ± 2.93 km2 and 4.50 ± 0.46 km2, respectively (as delineated from Sentinel-2 images by the authors). The Kuangza Glacier (16.47 ± 0.14 km2 in 2021) and Lunglikoze Lake (0.45 ± 0.02 km2 in 2021) are the largest glacier and the largest moraine-dammed glacial lake in the study area, respectively. Yang et al. (2013) noted that glaciers in this area belong to the spring-accumulation type, and that the climate there is modulated by the southern westerlies, the Bay of Bengal vortex, and the Indian monsoon [12]. The annual precipitation recorded by the nearest national meteorological station–Bomi station (see the location in Figure 1) is about 900 mm, and the annual average air temperature is about 9 °C. The wet season usually lasts from March to October. Air temperature drops rapidly as the altitude increases, and the altitude zones around 4100 m typically have an annual temperature of 0 °C [13].
Remotesensing 14 03792 g001 550
Figure 1. Location and overview of the study area. The background of the main panel is the Sentinel-2 image acquired on 12 October 2021. The red rectangle in the inset panel denotes the location of the study area. Dashed red rectangles R1–R4 in the main panel denote the areas shown in Figure 2a–d, respectively. Numbers 1–13 mark the glaciers mentioned in the text.
Figure 1. Location and overview of the study area. The background of the main panel is the Sentinel-2 image acquired on 12 October 2021. The red rectangle in the inset panel denotes the location of the study area. Dashed red rectangles R1–R4 in the main panel denote the areas shown in Figure 2a–d, respectively. Numbers 1–13 mark the glaciers mentioned in the text.
Remotesensing 14 03792 g001

3. Datasets

3.1. Optical Images

Landsat-5/7/8 images and Sentinel-2 images were used to interpret the landscape change, to delineate the outlines of glaciers /glacial lakes, and to estimate the glacier two-dimensional flow velocity. Landsat-5 images, Landsat-7 ETM+ images, Landsat-8 OLI images, and Sentinel-2 images have a resolution of 30 m, 15 m, 15 m, and 10 m, respectively. In total, 5 Landsat-5 images, 2 Landsat-7 images, 3 Landsat-8 images, and 15 Sentinel-2 images were downloaded from the USGS (https://earthexplorer.usgs.gov, accessed on 10 October 2021). The details and usage of each image are listed in Table 1.

3.2. SAR Images

Three stripmap bistatic TanDEM-X CoSSC (co-registered single-look slant-range complex) images were used to generate the DEMs. In terms of DEM generation, the bistatic TanDEM-X image pair is not impacted by glacier motion, atmospheric variation, and changes in glacier surface scattering feature. Moreover, the X-band SAR images can penetrate the glacier surface, and can still be useful when the glacier surface is covered by purely white snow. Therefore, the bistatic TanDEM-X image pair is suitable for generating the glacier surface DEM. The stripmap TanDEM-X image has a swath of about 30 km × 50 km, with a resolution of about 2 m. The TanDEM-X images were obtained from DLR EOWEB GeoPortal (https://eoweb.dlr.de/egp/, accessed on 1 November 2021). Table 1 provides details of the bistatic TanDEM-X CoSSC images.
Table
Table 1. Satellite images used in this study.
Table 1. Satellite images used in this study.
ImageDateResolutionProduct IDUsage
Landsat-5/TM27 August 198930 mLT51340391989239BJC00Glacial lake change interpretation
04 September 1992LT51340391992248BJC00
09 October 1993LT51340391993282BJC01
12 May 1995LT51340391995224BJC00
12 July 2007LT51340392007193BJC00
12 June 2008LT51340392008164BKT00
Landsat-7/ETM16 July 200015 mLE71340392000198BJC01Glacier/glacial lake outline delineation
03 July 2001LE71340392001184SGS00
Landsat-8/OLI13 August 201315 mLC81340392013225LGN02Glacier/glacial lake outline delineation
22 December 2014LC81340392014356LGN01Glacier flow velocity estimation
24 February 2015LC81340392015055LGN01
Sentinel-212 October 202110 mS2A_MSIL1C_20211012T040721_N0301_R047_T47RKN_20211012T063225Glacier/glacial lake outline delineation
13 November 2015S2A_MSIL1C_20151113T041012_N0204_R047_T47RKN_20151113T041259Glacier flow velocity estimation
23 December 2015S2A_MSIL1C_20151223T041202_N0201_R047_T47RKN_20151223T041511
07 December 2016S2A_MSIL1C_20161207T041142_N0204_R047_T47RKN_20161207T041633
25 February 2017S2A_MSIL1C_20170225T040721_N0204_R047_T47RKN_20170225T041209
02 December 2017S2A_MSIL1C_20171202T041131_N0206_R047_T47RKN_20171202T074539
31 January 2018S2A_MSIL1C_20180131T041011_N0206_R047_T47RKN_20180131T074139
27 November 2018S2A_MSIL1C_20181127T041111_N0207_R047_T47RKN_20181127T065815
16 January 2019S2A_MSIL1C_20190116T041121_N0207_R047_T47RKN_20190116T071601
02 November 2019S2A_MSIL1C_20191102T040921_N0208_R047_T47RKN_20191102T070601
12 December 2019S2A_MSIL1C_20191212T041141_N0208_R047_T47RKN_20191212T070015
16 November 2020S2A_MSIL1C_20201116T041041_N0209_R047_T47RKN_20201116T062106
15 January 2021S2A_MSIL1C_20210115T041121_N0209_R047_T47RKN_20210115T061909
11 November 2021S2A_MSIL1C_20211111T041011_N0301_R047_T47RKN_20211111T051112
09 February 2022S2A_MSIL1C_20220209T040921_N0400_R047_T47RKN_20220209T060252
TanDEM-X/CoSSC17 March 20121.9 × 2.0 m *TDM-CoSSC-DEM:/dims_op_pl_dfd_XXXXB00000000293549951504DEM extraction
06 December 20151.9 × 2.0 m *TDM-CoSSC-Experimental:/dims_op_pl_dfd_XXXXB00000000406913473572
17 December 20192.4 × 2.0 m *TDM-CoSSC-DEM:/dims_op_pl_dfd_XXXXB00000000581163692072
* Resolution in the ground range direction × resolution in the azimuth direction.

3.3. NASADEM

C-band NASADEM_HGTv001 was used to represent the glacier surface elevation in February 2000, and to assist in the generation of new TanDEM-X DEMs. The NASADEM_HGTv001 was generated from original SRTM interferometric data. Due to the improved work flow, the NASADEM_HGTv001 has a better quality than the earlier version of SRTM DEM in most regions [14]. The NASADEM_HGTv001 has a pixel spacing of 1 arc second, corresponding to ~30 m at the equator. Each distributed tile of product has an extent of 1° × 1°. The NASADEM_HGTv001 was obtained from the Land Processes Distributed Active Archive Centre (https://lpdaac.usgs.gov/, accessed on 20 October 2021).

3.4. Meteorological Data

The daily temperature and precipitation data acquired at the Bomi meteorological station during 1971–2018 were used to interpret the glacier changes. The Bomi meteorological station is about 60 km west from our study area (see the location in Figure 1). It has a coordinate of 95°46′18″E, 29°51′23″N, and an altitude of 2730 m a.s.l. The data were downloaded from the China Meteorological Data Service Centre (http://www.nmic.cn/, accessed on 2 March 2021).

4. Methods

4.1. Estimation of Glacier Area and Glacial Lake Area

In order to estimate the changes in glacier area and glacial lake area, three sets of glacier and glacial lake outlines (2000, 2013, and 2020) were manually delineated from the optical images. The supraglacial debris cover in our study area is scarce, and therefore the delineation of the glacier outline was mainly impacted by the image shadow, cloud cover, and seasonal snow. Taking these factors into account, the images acquired at the end of the ablation season and having least cloud cover and seasonal snow were preferred. Nevertheless, multiple images acquired at adjacent times needed to be combined to minimize the impact of seasonal snow and cloud cover. The Second Chinese Glacier Inventory (CGI-2) was taken as the reference for glacier outline delineation [15]. New terms of glacier outline vectors were acquired via manual modification of the CGI-2 that was overlapped with the multi-bands fused Landsat or Sentinel-2 images. The glacial lake outlines were delineated directly over the multiband fused images. The glacial lakes in this study were divided into two types: moraine-dammed lakes and trough valley lakes. We paid attention to the changes of the former kind which are much more fragile and dangerous. Finally, the glacier area and glacial lake area were calculated based on the outline vectors.

4.2. Estimation of Change in Glacier Surface Elevation

The DEMs of the study area in 2011, 2015, and 2019 were generated from the TanDEM-X/CoSSC images through the InSAR technique. Due to the dense fringes in the interferograms of the mountain area, the interferometric phase was difficult to unwrap. Hence, we simulated a topographic phase based on the one arc-second NASADEM, and then subtracted it from the original interferogram. The differential interferometric phase was unwrapped and then converted to height difference. A new DEM was obtained by adding the height difference to NASADEM. The glacier surface elevation change was estimated by differencing two time-adjacent DEMs (e.g., TanDEM-X DEM in 2011 minus NASADEM, and TanDEM-X DEM in 2015 minus TanDEM-X DEM in 2011). All of the used TanDEM-X images were acquired in winter, as were the images used to generate the SRTM DEM. Hence, the impact of seasonal glacier surface elevation changes is absent in our study. All of the prepared DEMs had the same coordinate system (WGS84), projection way (UTM), and pixel spacing (20 m).
Two DEMs were co-registered before differencing, because in mountainous areas a small geolocation shift of the DEM can cause significant elevation change errors. Moreover, due to the InSAR baseline residuals, along with the discrepancy between the resolution of raw SRTM images and TanDEM-X images, the elevation difference maps contained systematic biases that were related to the planimetric position and the terrain curvature. The co-registration of DEMs and the removal of systematic biases in the elevation difference map followed the scheme described in [16]. In the case of differencing the NASADEM and the TanDEM-X DEM, the discrepancy between the penetration capabilities of the C-band and the X-band over the glacier surface was considered. The distinction between the penetration depths of the C-band and the X-band was estimated by differencing the simultaneously obtained SRTM-X DEM and SRTM-C DEM over the glaciers. Assuming that the distribution of penetration difference is correlated with the glacier surface altitude and slope, we regressed a function of penetration difference and applied it to the elevation difference map to correct the bias [17]. In glacierized areas, December, January, February, and March can be considered to belong to the same season. In this case, our observation periods of glacier surface elevation change can be taken as 1999–2011, 2011–2015, and 2015–2019. The average glacier surface elevation change was computed through the hypsometric method, i.e., dividing the glacial altitude range into N bands and computing the average glacier surface elevation change within each altitude band, and then computing the area-weighted average. The void glacier areas were assumed to have the same surface elevation changes as the average value of the altitude band to which they belonged [18]. An altitude band of 50 m was used.

4.3. Estimation of Glacier Flow Velocity

The horizontal glacier flow velocities were derived from the panchromatic band of Landsat-8 images and the NIR band of Sentinel-2 images through the image phase correlation function of the COSI-Corr software [19]. For the image phase correlation, the quality of output was mainly related to the matching window size, window moving step, and correlation threshold. We performed multiple experiments, and found that setting an initial matching window size of 64 × 64 pixels, a final matching window size of 16 × 16 pixels, a moving step of 2 pixels, and a correlation threshold of 0.9 could export relatively good displacement fields. The outliers in the displacement maps that were caused by sharp surface changes, cloud cover, image shadow, and the lack of texture contrast were manually removed by referring to the signal-to-noise ratio map. A non-local mean filtering was conducted to reduce the residual noises. Finally, the average horizontal flow velocity fields were derived by synthesizing the north and east displacement fields exported by the image phase correlation, and dividing the results with image intervals. The glacier flow in our study area is relatively slow (see Section 5.2), but the glacier surface melting is strong, and the debris cover is very thin. The accumulated surface displacement that is large enough to be detected by optical image phase correlation, and the basis of optical image phase correlation, i.e., the similarity of the surface features of two images, become contradictory. In summer the glacier surface textures vary quickly because of the strong melting. It is nearly impossible to derive reliable summer glacier flow velocity through optical image phase correlation. Hence, only the winter image pairs were used. Regarding the cloud cover of the archived images, we finally chose one pair of Landsat-8 OLI images and seven pairs of Sentinel-2 images to estimate the glacier flow velocity.

4.4. Uncertainty Analysis

The uncertainties of glacier and glacial lake area ( δ a ) were estimated following the approach of Fujita et al. (2009) [20]:
δ a = 1 2 × l × ρ
where l is the length of the glacier boundaries or glacial lake boundaries (unit: m), and ρ is the image pixel spacing (unit: m). The uncertainties of average glacier surface elevation change ( δ a e ) were estimated following the approach of Rolstad et al. (2009) [21]:
δ a e = δ e π d 2 5 A
where δ e is the uncertainty of the surface elevation change observations at the individual pixel scale, A is the observed glacier area, and d is the auto-correlation length of the elevation change observations over stable regions. d can be determined by fitting a spherical semivariogram model to the empirical semivariogram of the samples [21]. In our study area, d was about 600 m (30 pixels). δ e was denoted by the normalized median absolute deviation (NMAD) of the elevation change observations over stable regions. The NMAD is less sensitive to outliers, and can be deemed as a robust estimator of standard deviation [22]. The NMAD can be calculated through the following formula:
N M A D = 1.4826 × m e d i a n ( | Δ h i − m e d i a n ( Δ h ) | ) ( i = 1 , 2 , 3 , … , n )
where Δ h is the elevation change observation, and n is the number of elevation change observations. The uncertainties of glacier flow velocity ( δ v ) were scaled by the NMAD of the displacement velocity observation over stable regions.

5. Results

5.1. Changes in Glacier Area and Glacial Lake Area

The number and area of glaciers and moraine-dammed glacial lakes in 2000, 2013, and 2021 are shown in Table 2. Only the glaciers and lakes having an area larger than 0.01 km2 were counted. From 2000 to 2021, both the number and area of glaciers continually decreased, while both the number and area of moraine-dammed glacial lakes continually increased. The glacier shrinking rate during 2013–2021 was higher than that during 2000–2013 (1.73 km2/a vs. 0.99 km2/a), indicating that the glacier melting rate accelerated. The expansion rate of glacial lake area during 2013 to 2021 is also higher than that during 2000–2013 (0.06 km2/a vs. 0.04 km2/a). Figure 2 shows three phases of glacier boundaries and moraine-dammed glacial lake boundaries in four subregions of the study area. We can see that most of the glaciers shrank dramatically between 2000 and 2021. Between 2007 and 2008, a proglacial lake (refers to a glacial lake immediately connected to its parent glacier) ahead of glacier 2 burst (see Section 5.3). Between 2013 and 2021, the proglacial lakes ahead of glaciers 4, 6, and 13 expanded dramatically as their parent glaciers retreated, while the separated glacial lake (refers to a glacial lake losing contact with its parent glacier) ahead of glacier 12 shrank slightly as the moraine moved forward. Accordingly, the number of relatively big glacial lakes (over 0.10 km2) decreased by one between 2000 and 2013, but increased by two between 2013 and 2021. Most of the relatively big glacial lakes, such as the Lunglikoze Lake and Kuangza Lake, did not expand noticeably between 2000 and 2021. The emergence and expansion of small glacial lakes (area below 0.10 km2) was the main cause glacial lake area growth.
Remotesensing 14 03792 g002 550
Figure 2. Outlines of glaciers and moraine-dammed glacial lakes in 2000, 2013 and 2021. The location of areas shown in subfigures (a–d) is denoted by the dashed red rectangles R1–R4 in Figure 1, respectively. Numbers over glacier areas mark the glaciers mentioned in the text. The background is the Sentinel-2 image acquired on 12 October 2021.
Figure 2. Outlines of glaciers and moraine-dammed glacial lakes in 2000, 2013 and 2021. The location of areas shown in subfigures (a–d) is denoted by the dashed red rectangles R1–R4 in Figure 1, respectively. Numbers over glacier areas mark the glaciers mentioned in the text. The background is the Sentinel-2 image acquired on 12 October 2021.
Remotesensing 14 03792 g002

5.2. Change in Glacier Surface Elevation

Figure 3 shows the derived glacier surface elevation change rates. During the three consecutive observation periods (1999–2011, 2011–2015, and 2015–2019), the average glacier surface elevation change rates were −0.83 ± 0.02 m/a, −0.33 ± 0.02 m/a, and −1.58 ± 0.02 m/a, respectively, and the glacier area covered by effective elevation change measurements accounted for 59.3%, 57.7%, and 56.0% of the total glacier area, respectively. The first and second highest thinning rates (−8.80 m/a and −8.24 m/a, during period 2015–2019) were found in the glacier areas marked by rectangles R5 and R6, respectively (see Figure 3c). Combining Figure 2 and Figure 3, we found that both the first and second highest thinning occurred at the glacier termini that were directly connected to glacial lakes. Although the average glacier thinning rate during 2011–2015 was lower than that during 1999–2011, the thinning rates of the ablation zones of many glaciers (e.g., glaciers 1, 3, 5, and 9–11) during 2011–2015 were significantly higher than that during 1999–2011. The firn basins of glaciers 3, 5, and 7–9 experienced obvious thickening during period 2011–2015, and this explains the reason why the average glacier thinning rate decreased during period 2011–2015.
Figure 4 shows the average glacier surface elevation change rate in each altitude band. We can see that the surface lowering rates of glacier areas below 5050 m a.s.l. continually increased from periods 1 to 3, while the values of glacier areas above 5050 m a.s.l. decreased from periods 1 to 2. The surface elevation changes in glacier areas above 5400 m a.s.l. even shifted from lowering to heightening during period 2. However, during period 3 all of the altitude bands saw glacier surface lowering. The surface elevation change rates of glacier areas below 5050 m a.s.l. during periods 1–3 were −1.96 ±0.02 m/a, −2.14 ± 0.02 m/a, and −3.21 ± 0.02 m/a, respectively; while the values of glacier areas above 5050 m a.s.l. were −0.76 ± 0.02 m/a, −0.22 ± 0.02 m/a, and −1.49 ± 0.02 m/a, respectively. Furthermore, the thinning rates of glaciers facing the north (e.g., glaciers 3, 5, and 9) were higher than those facing the south (e.g., glaciers 7 and 8).

5.3. Changes in Glacier Flow Velocity

Figure 5 shows the derived two-dimensional glacier flow velocity fields during the winters of eight consecutive years (2014–2021), along with the ground surface slope. We can see that, within a glacier, the parts flowing at a prominent velocity have a relatively steep slope, and the flow velocities of big glaciers (e.g., glaciers 3, 5, and 7–10) are clearly higher than those of the small glaciers. In most observation periods, the big glaciers had a winter flow velocity of 15.0~25.0 cm/day, while that of the small glaciers was only 2.0~8.0 cm/day. Glacier 11 was exceptional, because its surface slope is relatively steep; it has a small size but a prominent winter flow velocity (12.0~20.0 cm/day). The highest flow velocity (40.0 cm/day) was observed in glacier 4 (i.e., Kuangza Glacier) in 2015. In general, the winter glacier flow velocity in our study area was stable. No glacier experienced constant acceleration during 2014–2021. To further investigate the changes in glacier motion, we chose four glacier areas that had prominent flow velocity throughout the observation period, and computed the average flow velocities within them (see rectangles A1–A4 in Figure 5h). Table 3 displays the average glacier flow velocity in these four areas. All four areas present no sharp acceleration over the past eight years. Area A2 has the highest average flow velocity, then followed by area A3. The uncertainties of the glacier flow velocity of the eight observation periods are ±2.8 cm/d, ±2.2 cm/d, ±2.0 cm/d, ±1.5 cm/d, ±1.8 cm/d, ±2.1 cm/d, ±1.4 cm/d, and ±1.8 cm/d, respectively (in chronological order).

6. Discussion

6.1. Characteristics of Glacier Changes

The observed changes in glacier boundaries indicate that the glacier receding rate in the Bosula Mountain Range accelerated during 2000–2021. The change pattern of glacier boundaries is consistent with the change pattern of surface elevation of lower glacier areas (between 4800 m a.s.l. and 5050 m a.s.l.). Unlike the thick debris covered glaciers that are characterized by down-wasting [23,24], the glaciers in our study receded quickly when their lower reaches experienced strong thinning. The glacier thinning rate in the Bosula Mountain Range is at a high level relative to other regions of High Mountain Asia. Hugonnet et al. (2021) derived the surface elevation change rate of global glaciers from 2000 to 2019 by interpolating a continuous elevation time series from multiple co-registered optical DEMs [11]. Among the investigated subregions, South Asia East (which contains our study area) had an average glacier thinning rate of 0.56 ± 0.08 m/a, noticeably higher than that of Central Asia (0.23 ± 0.04 m/a) and South Asia West (0.16 ± 0.06 m/a). Note that South Asia East, Central Asia, and South Asia West comprise High Mountain Asia. However, our estimation of the average glacier thinning rate during 1999–2019 was 0.88 ± 0.02 m/a, significantly higher than the average value of South Asia East. Brun et al. (2017) estimated the surface elevation changes of glaciers in High Mountain Asia during 2000–2016 by fitting a linear regression through time series of co-registered ASTER DEMs [8]; the subregion Nyainqntanglha which contains our study area, had an average glacier thinning rate of 0.73 ± 0.27 m/a, the highest among the 12 subregions. Our estimation of the average glacier thinning rate during 1999–2015 was 0.71 ± 0.02 m/a, consistent with the result of Brun et al. (2017) [8]. Furthermore, Shean et al. (2020) derived the 2000–2018 regional glacier mass balance across High Mountain Asia by generating a glacier surface elevation change trend from multiple optical DEMs [10]; their partition of subregions was finer than that of Brun et al. (2017) [8]. Our study area was also divided into subregion Nyainqntanglha, which had an average glacier thinning rate of 0.54 ± 0.16 m/a. The adjacent subregion Hengduan had the highest average glacier thinning rate among the 22 subregions, 0.75 ± 0.18 m/a; however, this was still lower than the 1999–2019 glacier thinning rate in our study area (0.88 ± 0.02 m/a).

6.2. Factors of Glacier Change

As shown in Figure 1 and Figure 2, the debris cover is very thin. It is well known that thin debris cover enhances the absorption of solar radiation and then intensifies the melting of ice. Hence, by combining Figure 1 and Figure 3, we can see that the glacier areas experiencing prominent thinning largely have a ‘dirty’-looking surface, such as the tongues of glaciers 3, 9, 10, and 11. However, not all of the glaciers having ’dirty’ surface experienced prominent thinning, such as glaciers 7 and 8. The moisture in our study is mainly brought by the Bay of Bengal vortex and the Indian monsoon, which moves upstream along the Parlung Zangbo River (from northwest to southeast; see Figure 1) [12]. Glaciers 7 and 8 are located in the southern slope which receives more moisture than the northern and eastern slopes, and therefore can benefit from the orographic precipitation. Their thinning rates were lower than those in the northern or eastern slopes (such as glaciers 3, 9, 10, and 11) during the three observation periods. When regional precipitation increased during the second observation period (elaborated below), the proportion of thickened area in glaciers 7 and 8 was greater than that in glaciers 3, 9, 10, and 11.
Another significant factor of the local glacier ablation is the connectivity of glacial lakes. The water of a proglacial lake can enter the glacier via the holes and crevasses at the ice front. In summer the thermal erosion caused by the relatively warm lake water can exacerbate the subglacial ablation. Therefore, the first and second strongest thinning was observed at glaciers 4 and 6, respectively, both of which have a big proglacial lake.
The macroscopic glacier changes are mainly impacted by climate change. The rise in temperature can intensity glacier ablation, and the decrease in precipitation can reduce glacier accumulation. The temperature and precipitation recorded at the Bomi meteorological station were used to interpret the glacier surface elevation changes (Table 4). The Bomi station is close to our study area. We assumed that the change patterns of the temperature and precipitation in the glacierized zones were similar to those at the Bomi station. The records of annual temperature and annual precipitation indicate that the climate in the Bosula Mountain Range showed an obvious warming and wetting trend during the last three decades of the 20th century (see Table 4). After entering the first surface elevation change observation period (1999–2011), the rate of warming increased dramatically, while the precipitation decreased sharply. In this case, all of the glacier areas in our study experienced obvious thinning during the first observation period. After entering the second observation period (2011–2015), the temperature rose slightly, and the precipitation increased significantly. Accordingly, the average thinning rate increased slightly (from 1.96 ± 0.02 m/a to 2.14 ± 0.02 m/a) at the lower parts of glaciers (between 4800 m a.s.l. and 5050 m a.s.l.), but decreased remarkably (from 0.76 ± 0.02 m/a to 0.22 ± 0.02 m/a) at the higher parts (between 5050 m a.s.l. and 5850 m a.s.l.). Since glacier firn bases have an advantage in accumulating mass, the surface elevation changes of glacier areas above 5400 m a.s.l. shifted from negative to positive (from −0.46 ± 0.02 m/a to 0.42 ± 0.02 m/a). However, after entering the third period (2015–2019), the temperature rose dramatically again, while the precipitation decreased again. In this case, all of the glacier areas experienced strong thinning during the third period. Previous studies have proven that the glacier mass balance is more sensitive to changes in temperature than to changes in precipitation [17,25]. Although the precipitation in the third period was richer than that in the first period, the sharp temperature rise rendered the glacier thinning rate in the third period 90.4% higher than that in the first period (−1.58 ± 0.02 m/a vs. −0.83 ± 0.02 m/a).
Moreover, as mentioned above, for maritime glaciers the surface meltwater and rainwater can enter the glacier body and survive in the inner drainage system. The relatively warm surface meltwater and rainwater can promote inner ablation via heat conduction. Hence, relative to continental and sub-continental glaciers, maritime glaciers are more sensitive to external temperature change. The records of the Bomi meteorological station indicate that about 60% of the annual precipitation in the Bosula Mountain Range occurs during May–September. For the lower glacier reaches the summer rainfall may bring heat flux and enhance the ablation [26]. In general, factors including thin debris cover, quick temperature rise, the characteristics of maritime glaciers, and abundant summer rainfall, place glacierized regions in the Bosula Mountain Range among the fastest-melting glacierized regions in High Mountain Asia.

6.3. Potential Impacts of Glacier Changes on Mountain Hazards

Surging mountain glaciers are characterized by extremely fast flow (10~1000 times faster than normal flow), sharp surface elevation changes in the longitudinal direction, and noticeable surface morphological changes [27]. The past twenty years of glacier surface elevation change observations and glacier boundary change observations showed no signs of glacier surging in our study area. There was no sharp mass transfer in the longitudinal direction, and no outstanding mass accumulation in the higher reaches. A hydrological-controlled surge may occur without mass gain in the higher reaches [28]. However, during the pre-surge phase, either hydrological-controlled or thermal-controlled surge-type glaciers accelerate drastically. The winter glacier flow velocity varied with relatively small gradients. The biggest increase in flow velocity occurred in area A4 between 2014 and 2015, but the increase was just 56% of the 2014 speed (Table 3). The observed erratic changes in glacier flow velocity are likely to be the result of climate changes over the course of a year. Hence, the glacier flow velocity change observations also showed no signs of glacier surging. In addition, fast-developing crevasses, a precursory characteristic of large-volume glacier detachment (i.e., ice collapse) [7], did not appear on the studied glaciers.
Visual changes in optical images indicate that GLOF occurred twice in our study area during 1990–2013. As shown in Figure 6a, in August 1989 there was a big proglacial lake in the left of the front of glacier 4. The surrounding glacial landforms indicate that this proglacial lake formed after the receding of the western branch of glacier 4. Since the glacier flows over a downslope bed, the eastern side of this proglacial lake was actually dammed by the eastern branch of glacier 4. Moreover, there was a moderate-sized moraine-dammed glacial lake in the downstream valley of glacier 4 (referred to as moraine-dammed glacial lake 1). Figure 6b shows that the ice-dammed glacial lake further expanded towards the eastern branch of glacier 4 between 1989 and 1992. By 9 October 1993, the ice-dammed glacial lake had already burst (see Figure 6c). The drainage of this ice-dammed glacial lake caused the outburst of moraine-dammed glacial lake 1. Subsequently, the flood in the downstream valley triggered a debris flow. A large volume of debris flow mass was deposited at the confluence with the Shuangdui Qu (Qu means river in Tibetan). Consequently, a large barrier lake formed at the west side of the deposits. After this outburst, the ice-dammed glacial lake turned into two moraine-dammed glacial lakes (2 and 3 in Figure 6c). Since the structure of the moraine dam was destroyed, glacial lake 1 continued bursting as the input of glacier meltwater increased (see Figure 6d). By 2021, moraine-dammed glacial lake 1 had already disappeared (see Figure 1), and the tongue of glacier 4 had transformed into a proglacial lake (see Figure 2a).
Furthermore, there was a moraine-dammed proglacial lake ahead of glacier 2 in July 2007 (see Figure 7b). As glacier 2 kept receding, this proglacial lake ought to have expanded. However, by 13 August 2013 this proglacial lake had shrunk considerably, and trace of debris flow could be clearly discerned in the downstream valley. By inspecting all of the archived Landsat images unaffected by cloud cover, we found that the trace of debris flow first appeared on the image acquired on 12 June 2008. All of the evidences indicates that this proglacial lake burst between 12 July 2007 and 12 June 2008.
The comparison of Landsat series images and Sentinel-2 images indicates that no more glacial lake outburst events occurred during 1989–2021. Table 2 shows that the numbers of relatively big moraine-dammed lakes (area above 0.10 km2) in 2000, 2013, and 2021 were 11, 10, and 12, respectively. Excluding the proglacial lake that burst between July 2007 and June 2008 (ahead of glacier 2), the other ten moraine-dammed lakes that were larger than 0.10 km2 in 2000 had a total area of 1.98 ± 0.15 km2, 2.03 ± 0.16 km2, and 2.08 ± 0.11 km2 in 2000, 2013, and 2021, respectively. The area of these ten lakes barely changed over the past two decades, indicating that the capacity of these lakes was already close to the upper limit in 2000, and that most of the increased meltwater flowed out of the lakes (e.g., overflow at the dam). The state of moraine-dammed glacial lakes in our study area was consistent with that reported by Veh et al. (2019) [29], who found that, in spite of the fast-growing moraine-dammed glacial lake area, the GLOF frequency in the Hindu Kush–Karakoram–Himalaya–Nyainqentanglha region (HKKHN) has actually not changed since the late 1980s.
Due to the plasticity of glacier ice and the thermal erosion of lake water, ice-dammed lakes are more prone to bursting than moraine-dammed lakes, especially for those dammed by maritime glacier ice. In terms of maritime ice-dammed lakes, lake water can enter the dam base. Once the limit of basal water pressure is exceeded, a rapid drainage channel is formed and the ice dam bursts. At present, there are no ice-dammed glacial lakes in our study area. Ice front calving and ice avalanche are the most common triggers of GLOFs [30]. Among the 38 GLOFs in HKKHN observed by Veh et al. (2019) [29], 22 had no ascertained triggers, but 16 of those 22 occurred in glacial lakes within 300 m of their parent glacier. Veh et al. (2019) pointed out that ice calving and avalanche became less relevant triggers as glacial lakes gradually lost contact with their parent glaciers, and therefore GLOF frequency decreased in spite of increasing glacial lake area [29]. This view makes sense in our study area, because the only burst moraine-dammed glacial lake in our study area during the past two decades was a proglacial lake (the one ahead of glacier 2). Furthermore, this proglacial lake is not surrounded by steep slopes, and its parent glacier did not advance when it burst. However, this does not mean that glacier melting has no impact on the hazards of glacial lakes. Due to glacier melting and receding, the proglacial lakes ahead of glaciers 4, 6, and 13 expanded quickly during 2000–2021, and their current areas are over 0.10 km2. Technically, these three proglacial lakes are dangerous, because they are directly threatened by the ice calving or avalanche, and their outburst could bring considerable damage to the downstream settlements and the Sichuan-Tibet Road (see Figure 1). The lake ahead of glacier 13 merits special attention, because its outburst could lead to the outburst of two other big moraine-dammed glacial lakes in the downstream valley, and worse still, this lake is very close to the Sichuan-Tibet Road.

7. Conclusions

In order to analyze the potential hazards of glaciers and glacial lakes in the Bosula Mountain Range, we estimated the changes in glacier/glacial lake boundaries, glacier surface elevation, and glacier flow velocity between 2000 and 2021 based on multisource remote sensing data. Our results show that in 2000, 2013, and 2021, the total glacier areas were 152.88 ± 4.89 km2, 140.04 ± 4.65 km2, and 126.15 ± 2.93 km2, respectively, and the total glacial lake areas were 3.57 ± 0.44 km2, 4.03 ± 0.56 km2, and 4.52 ± 0.39 km2, respectively. The shrinking of glaciers and the expanding of glacial lakes accelerated synchronously. The surface altitude of glacier areas ranged from 4800 m a.s.l. to 5850 m a.s.l. During the periods 1999–2000, 2000–2015, and 2015–2019, the surface elevation change rates of glacier areas below 5050 m a.s.l. were −1.96 ± 0.02 m/a, −2.14 ± 0.02 m/a, and −3.21 ± 0.02 m/a, respectively, while the values of glacier areas above 5050 m a.s.l. were −0.76 ± 0.02 m/a, −0.22 ± 0.02 m/a, and −1.49 ± 0.02 m/a, respectively. The changing pattern of surface elevation in lower glacier areas aligned very well with the changing pattern of temperature. Due to the significant increase in precipitation, the thinning rate of higher glacier area decreased noticeably in the second period. Moreover, the glaciers flow velocity was relatively stable during 2014–2021.
In spite of strong ice melting, the observed changes in glacier boundaries, glacier surface elevation, and flow velocity indicate that the probability of glacier surge due to systematic changes in the subglacial hydrological environment is very low in our study area. The fast melting and receding of glaciers have led to the increase in the number and area of glacial lakes. However, the frequency of GLOFs has not increased accordingly. Since 1989, only one ice-dammed glacial lake burst during 1989–1993, and one moraine-dammed proglacial lake burst during 2007–2008. Most of the big glacial lakes have gradually lost contact with their parent glaciers that continued receding. Providing the low probabilities of glacier surge and stable dam structure, these glacial lakes become safer, as the impacts of accidental ice calving and avalanche are greatly weakened. Nevertheless, new proglacial lakes continue to emerge and expand as glaciers keep melting and receding, and the GLOF risk is still severe in our study area.
The main scientific purpose of this paper was to investigate the recent changes in glaciers and glacial lakes in the Bosula Mountain Range based on multiple remote sensing data. The hazards of glaciers and moraine-dammed glacial lakes were analyzed in a qualitative manner, and therefore our conclusions are somewhat subjective. In future, quantitative evaluation methods that include multiple criteria should be used to perform more rigorous hazard assessment, especially for the moraine-dammed glacial lakes [31].

Author Contributions

Conceptualization, J.L. and L.W.; methodology, J.L., Y.G. and L.G.; validation, Y.G. and L.G.; formal analysis, J.L. and Z.M.; resources, J.L. and L.W.; data curation, J.L., Y.G. and H.X.; writing—original draft preparation, J.L.; writing—review and editing, L.W.; visualization, J.L., Y.G. and H.X.; supervision, L.W.; project administration, J.L. and Z.M.; funding acquisition, J.L. and Z.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA20100101), the National Natural Science Foundation of China (Nos. 41904006 and 42171084), the Innovation-Driven Plan of Central South University (No. 2020CX036).

Acknowledgments

The authors thank below institutions for providing materials for this study: USGS for Landsat series images and NASADEM; European Space Agency (ESA) for Sentinel-2 images, DLR for TanDEM-X (via Data Project No. jiali_XTI_GLAC6767) and SRTM-X DEM data, and China Meteorological Data Service Centre for climate data.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 3. Derived glacier surface elevation change rates in three observation periods. The observation period is marked at the upper-middle part of the subfigure. Black curves denote glacier outlines. Numbers over glacier areas marked the glaciers mentioned in the text. Insert panels in (c) are the zoomed-in view of the areas within black dotted rectangles. The background of (a–c) is the Landsat−8/OLI image acquired on 6 October 2015.
Figure 3. Derived glacier surface elevation change rates in three observation periods. The observation period is marked at the upper-middle part of the subfigure. Black curves denote glacier outlines. Numbers over glacier areas marked the glaciers mentioned in the text. Insert panels in (c) are the zoomed-in view of the areas within black dotted rectangles. The background of (a–c) is the Landsat−8/OLI image acquired on 6 October 2015.
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Figure 4. (a) Glacier area and average glacier elevation change rate (AGECR) in each 50 m altitude band, and (b) the uncertainty of corresponding AGECR. The AGECRs in different observation periods are denoted by different symbols. The uncertainty of AGECR in each altitude band was estimated based on the NMAD of the elevation change rate in stable areas (see Section 4.4). The last two altitude bands have no elevation change rate observations in stable areas, and the uncertainties of the AGECRs in these two altitude bands were assumed to be the same as that of the last but two.
Figure 4. (a) Glacier area and average glacier elevation change rate (AGECR) in each 50 m altitude band, and (b) the uncertainty of corresponding AGECR. The AGECRs in different observation periods are denoted by different symbols. The uncertainty of AGECR in each altitude band was estimated based on the NMAD of the elevation change rate in stable areas (see Section 4.4). The last two altitude bands have no elevation change rate observations in stable areas, and the uncertainties of the AGECRs in these two altitude bands were assumed to be the same as that of the last but two.
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Figure 5. The derived two-dimensional glacier flow velocity during 2014–2021 (a–h), and the ground surface slope (i). The black curves are glacier boundaries. The red numbers in (a) mark the glaciers mentioned in the text. Rectangles A1–A4 in (h) mark the areas where glacier flow velocities in each observation period were averaged (see Table 3). The background of (a–h) is the Sentinel-2 image acquired on 12 October 2021.
Figure 5. The derived two-dimensional glacier flow velocity during 2014–2021 (a–h), and the ground surface slope (i). The black curves are glacier boundaries. The red numbers in (a) mark the glaciers mentioned in the text. Rectangles A1–A4 in (h) mark the areas where glacier flow velocities in each observation period were averaged (see Table 3). The background of (a–h) is the Sentinel-2 image acquired on 12 October 2021.
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Figure 6. Development of glacial lakes surrounding glacier 4. The backgrounds of (a–d) are Landsat-5 images. The acquisition dates of the images are marked on the top of the panels. Black curves denote glacier outlines. Readers can find the location of the shown area by referring to the location of glacier 4 in Figure 1.
Figure 6. Development of glacial lakes surrounding glacier 4. The backgrounds of (a–d) are Landsat-5 images. The acquisition dates of the images are marked on the top of the panels. Black curves denote glacier outlines. Readers can find the location of the shown area by referring to the location of glacier 4 in Figure 1.
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Figure 7. Development of the glacial lake ahead of glacier 2. The backgrounds of (a–d) are Landsat-7, Landsat-5, Landsat-5, and Landsat-8 images, respectively. The acquisition dates of the images are marked on the top of the panels. Black curves denote glacier outlines. Readers can find the location of the shown area by referring to the location of glacier 2 in Figure 1.
Figure 7. Development of the glacial lake ahead of glacier 2. The backgrounds of (a–d) are Landsat-7, Landsat-5, Landsat-5, and Landsat-8 images, respectively. The acquisition dates of the images are marked on the top of the panels. Black curves denote glacier outlines. Readers can find the location of the shown area by referring to the location of glacier 2 in Figure 1.
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Table
Table 2. Changes in the number and area of glaciers and moraine-dammed glacial lakes.
Table 2. Changes in the number and area of glaciers and moraine-dammed glacial lakes.
YearGlacierMoraine-Dammed Glacial Lake
NumberArea (km2)Area Change Rate (km2/a)NumberArea(km2)Area Change Rate (km2/a)Samples in Different Area Range
0.01–0.10 km20.10–0.45 km2
NumberAreaNumberArea
2000169152.88 ± 4.89-----633.57 ± 0.44-----521.44 ± 0.27112.13 ± 0.16
2013164140.04 ± 4.65−0.99694.03 ± 0.56+0.04591.97 ± 0.36102.03 ± 0.16
2021160126.15 ± 2.93−1.74844.52 ± 0.39+0.06722.21 ± 0.27122.31 ± 0.12
Table
Table 3. Average winter glacier flow velocity in A1–A4 during 2014–2021 (cm/d).
Table 3. Average winter glacier flow velocity in A1–A4 during 2014–2021 (cm/d).
AreaYear
20142015201620172018201920202021
A113.94 ± 1.2015.06 ± 0.9516.43 ± 0.9313.36 ± 0.6711.54 ± 0.828.24 ± 0.9311.21 ± 0.6715.74 ± 0.77
A215.98 ± 2.7517.28 ± 2.1417.89 ± 2.0316.69 ± 1.4716.96 ± 1.8014.62 ± 2.0515.60 ± 1.3816.28 ± 1.74
A313.59 ± 3.2515.74 ± 2.7014.49 ± 2.6813.96 ± 1.7512.54 ± 2.1112.91 ± 2.4414.56 ± 1.6413.89 ± 2.09
A410.08 ± 1.7315.76 ± 1.3610.11 ± 1.279.63 ± 0.9511.72 ± 1.1212.10 ± 1.309.54 ± 0.879.18 ± 1.11
Table
Table 4. Annual precipitation and annual average temperature recorded at the Bomi meteorological station from 1971 to 2018.
Table 4. Annual precipitation and annual average temperature recorded at the Bomi meteorological station from 1971 to 2018.
Data TypeMean and Standard Deviation of the Values over Different Periods
1971–19801981–19901991–19991999–20112011–20152015–2018
Annual precipitation (mm)858.1 ± 128.1896.5 ± 147.9963.4 ± 153.5835.5 ± 118.9900.8 ± 163.4884.7 ± 251.4
Annual average temperature (°C)8.52 ± 0.278.76 ± 0.258.87 ± 0.409.38 ± 0.399.40 ± 0.199.97 ± 0.43
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Li, J.; Gu, Y.; Wu, L.; Guo, L.; Xu, H.; Miao, Z. Changes in Glaciers and Glacial Lakes in the Bosula Mountain Range, Southeast Tibet, over the past Two Decades. Remote Sens. 2022, 14, 3792. https://doi.org/10.3390/rs14153792

AMA Style

Li J, Gu Y, Wu L, Guo L, Xu H, Miao Z. Changes in Glaciers and Glacial Lakes in the Bosula Mountain Range, Southeast Tibet, over the past Two Decades. Remote Sensing. 2022; 14(15):3792. https://doi.org/10.3390/rs14153792

Chicago/Turabian Style

Li, Jia, Yunyang Gu, Lixin Wu, Lei Guo, Haodong Xu, and Zelang Miao. 2022. "Changes in Glaciers and Glacial Lakes in the Bosula Mountain Range, Southeast Tibet, over the past Two Decades" Remote Sensing 14, no. 15: 3792. https://doi.org/10.3390/rs14153792

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