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Article

A Comprehensive Examination of the Medvezhiy Glacier’s Surges in West Pamir (1968–2023)

by
Murodkhudzha Murodov
1,2,3,4,
Lanhai Li
1,2,5,6,*,
Mustafo Safarov
3,7,
Mingyang Lv
8,
Amirkhamza Murodov
9,
Aminjon Gulakhmadov
1,3,4,10,11,
Kabutov Khusrav
4 and
Yubao Qiu
1,2,5,6
1
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Research Center for Ecology and Environment of Central Asia, Dushanbe 734063, Tajikistan
4
State Scientific Institution, Center for the Study of Glaciers of the National Academy of Sciences of Tajikistan, Dushanbe 734025, Tajikistan
5
Tianshan Snowcover and Avalanche Observation and Research Station of Xinjiang, Yili 835800, China
6
Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone, Urumqi 830011, China
7
State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
8
Institute of Space Earth Science, Nanjing University, Suzhou 215163, China
9
Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences, Beijing 100101, China
10
Department of Hydraulics and Hydro Informatics “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers”, National Research University, Tashkent 60111496, Uzbekistan
11
Institute of Water Problems, Hydropower and Ecology, National Academy of Sciences of Tajikistan, Dushanbe 734042, Tajikistan
 
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Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(10), 1730; https://doi.org/10.3390/rs16101730
Submission received: 9 March 2024 / Revised: 25 April 2024 / Accepted: 3 May 2024 / Published: 14 May 2024
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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Abstract

:
The Vanj River Basin contains a dynamic glacier, the Medvezhiy glacier, which occasionally poses a danger to local residents due to its surging, flooding, and frequent blockages of the Abdukahor River, leading to intense glacial lake outburst floods (GLOF). This study offers a new perspective on the quantitative assessment of glacier surface velocities and associated lake changes during six surges from 1968 to 2023 by using time-series imagery (Corona, Hexagon, Landsat), SRTM elevation maps, ITS_LIVE, unmanned aerial vehicles, local climate, and glacier surface elevation changes. Six turbulent periods (1968, 1973, 1977, 1989–1990, 2001, and 2011) were investigated, each lasting three years within a 10–11-year cycle. During inactive phases, a reduction in the thickness of the glacier tongue in the ablation zone occurred. During a surge in 2011, the flow accelerated, creating an ice dam and conditions for GLOF. Using these datasets, we reconstructed the process of the Medvezhiy glacier surge with high detail and identified a clear signal of uplift in the surface above the lower glacier tongue as well as a uniform increase in velocities associated with the onset of the surge. The increased activity of the Medvezhiy glacier and seasonal fluctuations in surface runoff are closely linked to climatic factors throughout the surge phase, and recent UAV observations indicate the absence of GLOFs in the glacier’s channel. Comprehending the processes of glacier movements and related changes at a regional level is crucial for implementing more proactive measures and identifying appropriate strategies for mitigation.

1. Introduction

The role of glaciers in high mountain regions are vital for mankind and provide a sources for drinking water, irrigation, and energy generation (hydrostations), but they also pose occasional hazards such as glacial lake outburst floods (GLOFs) and ice avalanches, causing human casualties and economic losses in downstream areas [1,2,3]. Such types of glaciers are absent in flat and relatively low plateaus such as Antarctica and the Alps [4] but are concentrated in regions like the Alaskan–Canadian Arctic, Svalbard, and the Pamir–Karakoram Plateau [4,5]. A thorough study of the dynamics and behavior of surging glaciers, particularly during their surge stage, remains a rare occurrence in scientific research [6]. In many cases, surging glaciers completely cover valleys, forming pressure lakes behind ice dams and reaching tens and hundreds of meters in height, and their eventual rupture causes floods, which may take on a destructive nature [7]. Traditional markers like looped moraines or terminus shift [8] and current elevation changes [9,10] are used to identify surge-type glaciers. At the same time, the occurrence of glacier surging can pose significant risks to areas downstream, leading to events like glacial lake outburst floods (GLOFs), glacial meltwater debris flows, and glacier collapses [11,12,13], all stemming from natural disaster events. The constant monitoring of surging glaciers with aerial photography and satellite imagery is required to avoid dangerous consequences and achieve their possible prevention. Consequently, understanding the dynamics of surging glaciers can aid in forecasting the behavior of glaciers and the emergence of hazards in advance.
Climate change has had a significant impact on glaciers worldwide, and Tajikistan’s surging glaciers are no exception [14,15,16,17,18,19]. Surging glaciers in Tajikistan move periodically/or pulsate unpredictably, which is triggered by complex interactions between ice and water, which in turn is influenced by temperature, precipitation, and ice fracturing [16]. Studies have shown that these glaciers are retreating due to climate change, which poses severe threats to Tajikistan’s environment, economy, and water security. The Pamirs are home to glaciers covering an area of about 12,500 km2 [20], with these glaciers accounting for more than 60% of the 7493 km2 total glaciated area of the given place. One of the largest projects carried out by former Soviet glaciologists in the 1960s–1980s was the creation of the USSR Glacier Inventory [21]. It consists of 69 books [22] and covers 23 glacial systems [23]. To some extent, some problems were revealed by the catalog of surging (unstable) Pamir glaciers that was compiled in 1998 [8], where 630 of the total number of 6730 glaciers [5] turned out to be unstable. Unfortunately, this catalog is mainly based on field analysis and does not consider in detail some important aspects such as surface elevation change, velocity, correlation between climate observing data, glaciers size change, and the overall impact of several factors in glaciers surging [24]. Many surge-type glaciers have been identified in the Pamir Mountain Range. According to Kotlyakov et al. [5], the observed 215 glaciers show signs of recent instability, and 55 of them exhibited in period surges between the years of 1973 and 2006. These observations are based on optical space photographs. From this point of view, the morphology and dynamics of surging glaciers are of particular interest [25].
The present article incorporates the literature data on surging glaciers, drawing from different sources, including [8,14,15,18,19,24,26,27,28], as well as separate articles by [29] and other authors who have conducted observations of such glaciers in the Pamirs. Goerlich updated the Pamir glacier surge inventory using a systematic analysis of Landsat time series (1988 to 2018) and high-resolution imagery (Corona, Hexagon, Bing Maps, Google Earth, and DEM differences [30]. The primary purpose of the digital elevation model (DEM) is to investigate changes in the surging glacier’s surface elevation [31,32]. A surging glacier’s surface features change rapidly due to its development remote; thus, remote sensing analysis is one of the most useful tools for the detection of glacier surges and the factors that serve as the main triggers. Remote sensing is mostly utilized to detect and track changes in the flow velocity, elevation, and surface characteristics of glaciers [11,31,33,34]. Recent studies by Zhang et al. (2022), Shangguan et al. (2016), and Wendt et al. (2017) explored glacier variations, surge dynamics, and hydrological influences in the Pamir Mountains, emphasizing the importance of remote sensing techniques for monitoring these dynamic environments [35,36,37].
The objectives of our research are the following: (1) to enhance comprehension of Medvezhiy glacier surge dynamics and measure the glacier’s behavior and (2) to investigate the dynamics of degradation from 1968 to 2023. By using satellite images (Corona, Hexagon, and Landsat), remote sensing products (SRTM DEM and ITS_LIVE), unmanned aerial vehicles (UAVs), and meteorological and hydrological data, we revealed some important issues regarding the evolution of surging glaciers and their influencing factors. To better understand the dynamics of surges in the Medvezhiy glacier and the relationship between surges and the formation of glacial lakes, this study identified six surges of the Medvezhiy glacier since 1968. Satellite data from Corona, Hexagon, and Landsat were utilized to analyze glacier movement speeds, changes in elevation, and area. The study analyzed the correlation between surges and GLOFs, examined the relationship between climate and changes in glacier terminus, and established the period and process of surges. The findings of this study are crucial for enhancing our understanding of Medvezhiy glacier surge dynamics, assessing glacier behavior, and investigating the dynamics of degradation from 1968 to 2023, providing valuable information to support informed decision making in the context of glacial changes, climate interactions, and potential GLOF risks.

2. Study Area

The upper stages of the Abdukahor River, a left tributary of the Vanj River, are home to the Medvezhiy glacier (Figure 1C), which is situated in the northwest of the Pamir Mountains on the western slope of the Academy of Sciences Range (Vanj River Basin). The Medvezhiy glacier (38°39′00″N; 72°11′00″E) is one of the thirty-five surging glaciers in Tajikistan, capable of sometimes dramatically increasing its speed of surge [38]. The glacier’s elevation is in range of 3000 m to 5500 m above sea level (a.s.l.), and the accumulation zone encompasses approximately 17.5 km2 of total area. The accumulation and ablation zone are separated from the narrow tongue (5.5 km2) by abrupt icefall of 600 m in height. In total, the glacier blankets an expansive area of about 25 km2 [28,39]. The glacier has a middle moraine, which divides the glacier into two ice streams, one of which flows from the upper part of the glacier and the other from the side (the eastern part of the glacier). Historical records of the glacier, published in [40], indicate a surge that occurred not long before 1916. Locals remember a surge of the glacier in 1937, and in 1951, R.D. Zabirov, who studied the glacier and its environs in the middle of the 20th century, reported that it surged more than 1 km [41].
The climate of the study area is associated with its geographical location and its location on the western periphery of a vast mountain uplift. The climate of the glacial zone is determined to a greater extent by the influence of the free circulation of the atmosphere and vertical zonality. The main climatic conditions in the upper reaches of the Vanj River are characterized by the meteorological station of Humrogi. The hydro-meteorological station of Humrogi is located at an altitude of 1320 m (a.s.l.), and its closeness between two ridges protects it from cold intrusions and weathering. In winter, the average temperature drops to −2 °C, whereas in summer, it rises to +26–27 °C [42].

3. Data and Methods

3.1. Climate Data

According to the Humrogi station (71°23′31.50″E, 38°18′56.13″N), the average annual temperature in the Vanj River Basin is 9.9 °C. Annual precipitation is about 341 mm. The driest month is August, with precipitation of 1 mm. Most of the precipitation falls in March, with an average of 64 mm. The warmest month of the year is July, with an average temperature of 23.0 °C. The average temperature in January is −4.9 °C. This is the lowest average temperature during the year. The difference in the amount of precipitation between the driest and wettest month is 63 mm. The average temperature varies throughout the year by 27.9 °C [42]. Five evaluation statistics, the Pearson correlation coefficient (PCC) [43], coefficient of determination (R2) [44], Nash–Sutcliffe efficiency (NSE) [45], Kling–Gupta efficiency (KGE) [46], and relative error (PBIAS) [47], were examined.
To analyze the mechanism of glacier surge in the Vanj River Basin, we first compared the air temperature and precipitation at the Humrogi weather station from the Vanj Basin with the temperature at the surrounding weather stations since 1955. We obtained data on the air temperature and precipitation at the Humrogi station since 1955 to analyze the mechanism of glacier surge, which indicated that the Humrogi station had the best match with the CHIRPS temperature but did not match well with precipitation (see Table 1).

3.1.1. Multispectral Data

To track the process of the Medvezhiy glacier surge, data from the U.S. Geological Survey (USGS), taken between 1968 and 2023, were utilized (Figure 2). Specifically, the following data sources were used: (1) 25 Landsat TM and ETM+ OLI images with a spatial resolution of 30 m from the USGS, obtained for the periods of 1973, 1977, 1991, 2000, and 2023 to monitor the process of the Medvezhiy glacier surge (see Table 2); (2) Corona KH-4 satellite images with a spatial resolution of 1.82 m (1968, 1971); and (3) Hexagon KH-9 satellite images with a spatial resolution of 6 × 9 m (1973, 1975, and 1980) for evaluating glacier surface runoff. Aerial photography of the glacier was carried out using a DJI Phantom quadcopter, which is useful for aerial photography of small areas in remote mountainous areas of Tajikistan [48]. The USGS Earth Explorer website (https://earthexplorer.usgs.gov/; accessed on 19 January 2022) provided the satellite data for download. Table 2 presents the sensor characteristics. The stereo high-resolution CORONA KH-4B images initially lacked georeferencing, so we utilized ESRI ArcGIS 10.5 to rectify each image, stitching together segments to form complete scenes. Ground control points (GCPs) were gleaned from modern Digital Globe imagery via the ESRI baseman. Selecting GCPs proved challenging due to potential landscape changes over time, as discussed in [49]. Rock outcrops emerged as reliable GCPs, offering stability and easy identification. Initially relying on 2–3 GCPs, we iteratively refined the georeferencing process by identifying more GCPs, thereby reducing error. In order to assess the uncertainty determination of glacier contours, we considered the error associated with each. For glaciers covered with debris, the error-determination contours were set at the level of one pixel, while for clearer contours of pure ice and lakes, the error was as low as half a pixel [50,51,52]. A pixel size determination algorithm for Corona KH-4B, Hexagon KH-9, Landsat TM, “ETM+”, and OLI as well as other datasets was established using the following equation:
U n c e r t a i n t y = e 2 + f 2
In this equation, e and f represent the boundary uncertainty for the two involved images. For the lower part of glaciers covered with a thick layer of debris, e and f are equal to the size of one pixel, while for the glacier boundary, the size of half a pixel was utilized. All Earth remote sensing resources used for the project were located on the UTM projection of the WGS1984 ellipsoid (zone 43N) [53]. For the detection of glacier surges and determination of their start, end, and cycle, we utilized freely available Landsat imagery cloud-free scenes from the end of summer but also considered earlier acquisitions for some regions.

3.1.2. Glacier Surface Velocity Data

We obtained the Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) data from NASA to assess the interannual variations in the velocity of the Medvezhiy glacier. Based on Landsat 5,7,8,9 and Sentinel-1,2 images, this glacier velocity product uses the autonomous Repeat Image Feature Tracking processing approach as outlined by [54] to extract glacier velocity by feature tracking, local normalization, and over-sampling. The data were strong enough, and picture matching could be the source of inaccuracies. However, surge occurrences could last for many months instead of a year, in which case a mean annual figure would significantly underestimate true flow velocities. We used annual glacier velocity data with a spatial resolution of 240 m from 1986 to 2018 in our analysis (https://nsidc.org/apps/itslive/ (accessed on 8 March 2024)). The glacier velocity error was estimated using the ITS_LIVE data. Each component’s velocity was linked to the stable region during the creation of ITS_LIVE data, and to account for co-registration problems, the median of each velocity component (Vx, Vy) was set to zero on the non-glacial zone. Velocity components that differed from the median value of all pixels in the same position by more than three times the quartile range were deemed to be gross outliers and were eliminated. The standard deviation of the component velocity recorded in the stable region was used to determine the uncertainty in each image-pair velocity value. According to [55], the uncertainties in merging velocities are calculated on a pixel basis by propagating the uncertainty of each image-pair velocity. The remaining velocity error, if the correction is successful in eliminating the co-registration flaws that were present during the creation of Sentinel-1 velocity data, is equivalent to 0.1 pixels of tracking precision. Thus, under 6 d and 12 d repetition cycles, the predicted velocity errors obtained for Sentinel-1 data with a pixel size of 3 × 14 m are 0.24 m d−1 and 0.12 m d−1, respectively. According to [56], there is less uncertainty (~0.08 m−1) in the glacier velocity produced from the 12-day Sentinel-1 data.

3.1.3. DEM

To estimate the GLOF threat, we employed the SRTM DEM as the primary remote sensing data to ascertain the geomorphometric properties of the lake environs [57,58,59,60]. More specifically, from 1968 to 2023, SRTM DEM was also utilized as supplemental data for charting glacial lakes. Threshold zones for the distance between lakes and glaciers were defined using glacier polygons. The SRTM data were utilized to define the glacier’s slope, aspect, contour, and hillshade. Subsequently, the ArcGIS 10.5 program was employed to process the SRTM data and generate maps. It was retrieved from the Earth Explorer website of the United States Geological Survey (http://earthexplorer.usgs.gov/; accessed on 12 March 2022). To determine the landscape’s elevation-based characteristics, terrain characterization using DEM analysis is very helpful [61]. Through the characterization of elevation, slope, and aspect obtained from the SRTM, and digital terrain analysis was carried out. Understanding the dynamics of these quickly changing glaciers requires a detailed examination of the glaciers and surface features, which was made possible by the SRTM [62,63,64]. The study by [65] indicated the surface elevation change rates of glaciers and their surrounding areas within a 10 km buffer zone between 2000 and 2019. The elevation changes were analyzed at a horizontal resolution of 100 m × 100 m for the 5-year periods of 2000–2004, 2005–2009, 2010–2014, and 2015–2019 as well as for the full 20-year period of 2000–2019 (http://maps.theia-land.fr/theia-cartographic-layers; accessed on 5 April 2022). The estimation of surface elevation change was derived from fitting Gaussian process regression to time series of elevation observations from multiple digital elevation models (DEMs). These DEMs are primarily generated and corrected from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) stereo imagery (ASTL1A)(https://lpdaac.usgs.gov/products/ast_l1av003/; accessed on 18 October 2022). Additionally, DEMs from the archives of Arctic DEM and of the Reference Elevation Model of Antarctica, which are derived from WorldView and GeoEye imagery, were also utilized in the polar regions. Periods are defined as inclusive calendar years from January 1 to January 1 (e.g., 2000–2004 includes data from 1 January 2000 to 1 January 2005) [65].

3.2. Glacier Parameter Extraction

The glacier boundary was delineated through visual interpretation of Corona, Hexagon, Landsat, and UAV images. To enhance accuracy, we identified the glacier boundaries using data from the first inventory of USSR glaciers [66]. This inventory offers a consistent dataset for each glacier, including manually modified outlines from field surveys conducted in 1978. Since the inventory captures a specific moment in time, uplift-type glaciers might not be individually studied due to varying stages in their uplift cycle. Instead, they may be considered part of a larger main glacier. We kept the area of the upper glacier cluster unchanged, altering only the portion that changed at the glacier’s end to establish boundaries for different years.
The glacier area was directly allocated, and the river basin’s glacier borders were determined based on the glacier boundary using ArcGIS software (version 10.5). We employed an animation approach to visualize all time period images in sequence using ArcMap. According to [53], all Earth remote sensing and UAV materials used in the research were located in the UTM projection (zone 43N) on the WGS1984 ellipsoid. Additionally, glacier tongue borders at intermediate dates between 1968 and 2023 facilitated the analysis of their surges. By creating a buffer zone along the glacier edges with a width equal to twice the spatial resolution of space photos, we calculated errors in estimating glacier areas. Axial lines were established for the Medvezhiy glacier tongue between the highest and lowest recorded frontal locations from 1968 to 2023. Along these lines, observations of front oscillations were made (as shown in Figure 3 and Figure 4).

4. Results

During our study, we discovered that the Medvezhiy glacier had at least six surges. The characteristics of the Medvezhiy surges between 1968–2023 are thoroughly described in Section 4.1. The Medvezhiy glacier’s velocity and height fluctuations are detailed in Section 4.2 and Section 4.3, respectively.

4.1. Glacier Surge from 1968 to 2023

The analysis of the behavior of the Medvezhiy glacier based on the images from Corona KH-4B, Hexagon KH-9, and Landsat MSS, taken from 1968 to 2000, provides valuable information about its changes during this period (Figure 3). In August 1971, the Medvezhiy glacier exhibited significant ice loss, with the terminus position of the glacier decreasing to 0.49 ± 0.005 km2 compared to measurements in 1968. Subsequently, from June 1973, the glacier advanced by about 530 ± 5 m, and its area increased by 0.55 ± 0.005 km2, indicating a period of accelerated surge. By June 1975, the glacier had retreated by about 515 ± 6 m, with an area change of 0.64 ± 0.006 km2. However, by August 1977, there was a noticeable advance of 400 ± 84 m, resulting in the area increasing to 0.38 ± 0.08 km2. According to Hexagon KH-9 data, by 20 August 1980, the Medvezhiy glacier had receded to 77 ± 30 m, with an area change of 0.27 ± 0.06 km2 compared to its measurements in 1977. According to Landsat data, since 14 July 1991, the Medvezhiy glacier has retreated by 1000 ± 30 m, with a decrease in area of 0.58 ± 0.03 km2. From 16 September 2000, the glacier continued to retreat 703 ± 30 m, with a decrease in area by 0.34 ± 0.03 km2 compared to 1991. After processing satellite imagery from 1968 to 2000, it was found that during this period, the Medvezhiy glacier showed four surges (1968, 1973, 1977, and 1989–1990).
Landsat images taken between 2001 and 2010 (Figure 4a,b) showed that after the surge in October 2001, the tongue area of the Medvezhy glacier was at an altitude of 2950 m a.s.l., and the total area of the glacier was 30.65 ± 0.03 km2. Since September 2002, the starboard side of the glacier has moved forward by 136 ± 30 m. From August 2003 to August 2008, the glacier area was 30.58 ± 0.03 km2, and its position decreased by 35 m. In the period from September 2009 to August 2010, the state of the glacier practically did not change. The last notable advance of the glacier was recorded on 22 August 2011, when it extended to 930 m, and the area increased to 0.64 ± 0.03 km2. Subsequently, by September 2012, it advanced another 42 m. This period was characterized by rapid progress in the initial years, followed by gradual advancement, and a subsequent slowdown. The glacier’s area began to steadily decrease, and recent fieldwork using UAVs indicates that it has retreated by 707 ± 30 m since 2011. Between August 2013 and 2014, this figure reached 0.03 km2. Landsat data revealed that the lower part of the Medvezhiy glacier broke off in August 2015, resulting in a gap of 461 ± 30 m. In 2016, the glacier retreated by a further 712 m compared to 2011. Gross changes prevented the glacier from advancing between August 2017 and August 2018. However, by August 2019, the glacier retreated by 882 ± 30 m. A Landsat image from September 2020 demonstrated the beginning of the western edge of the Medvezhiy glacier terminal breaking away from the dam. Subsequently, the last minor surge of the glacier was detected on 20 August 2021, west of the glacier’s end. However, Landsat images taken after September 2022–2023 showed no signs of glacier advance. The area of the Medvezhiy glacier is measured at 30.57 ± 0.03 km2, and recent fieldwork using UAVs indicates that it has retreated by 707 m since 2011. It is noted that since 1968, six ascents have occurred on the Medvezhiy glacier, each lasting two to three years and reaching a maximum advance of 530 ± 16 m. A drone photo taken on 20 August 2023 indicates that the glacier has already begun to retreat, possibly due to higher temperatures in that year. The summer of 2011 is suggested to have been the peak of the glacier’s speed. Based on the analysis of Landsat and UAV images (Figure 2(4)), it was observed that the glacier tongue has reached the endpoint of retreat and is currently situated at an altitude of 2950 m (a.s.l.). A comparative analysis of glacier area data from 2001 to 2023 shows that the area of the glacier started to decline (Figure 4a–d). The obtained data validate the swift melting of the studied glacier, which is moving quickly from its upper to its lower section and may eventually totally melt. The glacier can quickly erode and eventually disappear as a result of this behavior. During this period, the Medvezhiy glacier showed two surges (2001 and 2011).
The results of our study revealed significant changes in the characteristics of the Medvezhiy glacier over the past 55 years (Figure 3 and Figure 4). Our findings indicate that the Medvezhiy glacier experienced a significant retreat of approximately 1630 meters from 1973 to 2023, as illustrated in Figure 5. The glacier experienced an earlier rise before 1990, followed by a period of melting and contour changes. In 1991, the glacier ledge was at an altitude of 2920 m (a.s.l.), and by 2000, it had retreated by 693.60 m. Subsequently, the glacier ledge moved to an altitude of 3000 m (a.s.l.). A comparison of Landsat satellite images from 2001 to 2023 revealed that the last surge of the glacier with an advance of 859 m was observed in August 2011, and the position of the muzzle decreased to an altitude of 2915 m (a.s.l.). From 2013 to 2023, the Medvezhiy glacier returned to its previous state and began to decrease by 700 m, and according to the results of the UAV, it is currently at an altitude of 2960 m (a.s.l.) (Figure 2(4)). It is noteworthy that the Abdukahor River, which was previously blocked by the glacier due to rising water levels, now flows under the glacier.
Observations of the Medvezhiy glacier have revealed significant retreats in recent years. Satellite and field data indicate that after 2000, the glacier began gradually shedding its remaining mass from the accumulation zones, with its last surge occurring in 2011. Currently, the Medvezhiy glacier is in a quiescent state and does not exhibit any surges. These results underscore a period of accelerated melting and contour changes, highlighting the glacier’s vulnerability to climate change.

4.2. Glacier Velocity Changes

The velocity of the Medvezhiy glacier, measured in meters per year (m/year), has been observed from 1988 to 2018 as part of IT’S LIVE (https://nsidc.org/apps/itslive/; accessed on 10 January 2023). The data records the maximum velocities of the glacier for each year during this period, revealing fluctuations in its movement. A significant increase in velocity was recorded in 1988, reaching 138.9 m/year, which is substantially higher than the previous and succeeding years. Notably high velocities were observed in 2001, 2007, and 2013 at 113.8 m/year, 88.1 m/year, and 96.7 m/year, respectively, marking periods of rapid movement (Figure 6 and Figure 7). On the flip side, the slowest speeds were documented in 1993 and 1994, both at 4.13 m/year, followed by 2000 at 6.61 m/year and 2002 at 6.63 m/year, and with 2010 exhibiting notably lower velocities under 3.59 m/year. This variation indicates that the Medvezhiy glacier experienced both accelerated and reduced movement over the observed time frame.
Overall, this dataset reflects the dynamic nature of the Medvezhiy glacier, showcasing fluctuations in its velocity over the course of three decades. These variations in glacier velocity can provide valuable insights into the behavior and response of the glacier to environmental factors such as temperature, precipitation, and other contributing variables.
During the period from 1988 to 1992, the Medvezhiy glacier exhibited a maximum velocity of 138.9 ± 3 m/per. However, from 1993 to 1999, there was a significant decline in its velocity, with a maximum of only 29.6 ± 1 m/per. Subsequently, from 2000 to 2007, there was a slight increase to a maximum velocity of 113.8 ± 2 m/per. Between 2008 and 2010, the velocity decreased again, with a maximum of 59.9 ± 1 m/per. The years 2011 to 2013 saw a stabilization in velocity, maintaining a maximum of 96.7 ± 1 m/per, coinciding with glacier surges observed in 2011. Finally, from 2014 to 2018, the maximum velocity remained consistent at 90.5 ± 1 m/per. These fluctuations in glacier velocity suggest dynamic changes in its behavior over time, reflecting periods of both acceleration and deceleration, potentially influenced by the occurrence of glacier surges in 1989–1990, 2001, and 2011.

4.3. Glacier Elevation Changes

The glacier’s terminus position showed a significant upward trend, while its misposition displayed a considerable downward trend based on the elevation changes between 2000 and 2019. During this period, there was an increase of 7.3 ± 0.5 m in height above the terminus position of the glacier and a decrease of −4.8 ± 0.3 m in height above the middle part (Figure 8, Table 3).
Using publicly available data [65] and the well-known geodetic approach [67], the dynamics of changes in glacier surface dynamics from 2000 to 2019 were evaluated to verify the validity of the study’s findings. Using the geodetic approach, the examined glacier showed a drop in surface elevations in the center portion and an increase in height in the tongue part at the same time. Specifically, it was discovered that the middle portion of the investigated glacier declined by 4.8 m/year during the process of modifying the glacier surface between 2000 and 2019, whereas the tongue part had a maximum growth of 7.3 m. Utilizing the data, we were able to determine that the right part of the Medvezhiy glacier shifted by 887 m following a surge in 2011, resulting in a coverage area of 30.5 km2 compared to 30.8 km2 in 2000. A comparative analysis of glacier area data from 2000 to 2023 reveals a decline in the glacier’s area. The acquired data corroborate the rapid melting of the studied glacier, which is moving swiftly from its upper to its lower section and may ultimately completely melt. These findings indicate that the glacier is progressively losing mass in the ablation area.

5. Discussion

Medvezhiy glacier surges occur when the glacier’s tongue becomes weighed down by a buildup of up to 200 million m3 of snow and ice due to excessive snowfall and inadequate melting in the preceding years [24]. Konovalov and Desinov reported an extensive retreat of the terminus portions of the Medvezhiy glacier [68]. Our results from satellite data (Corona, Hexagon, and Landsat), revealed that the terminus position of the Medvezhiy glacier surged in 1973 by about 530 m, with an average speed of terminus advance of 12.7 m/day, which is considered the most active period with a velocity of about 40 m/day, and the peak speed in time intervals reached 100 m/day. These findings are consistent with previous studies that recorded the same behavior of the Medvezhiy glacier [69]. During the period from 1988 to 1992, the Medvezhiy glacier exhibited an average maximum velocity of 46.1 m/year. However, from 1993 to 1999, there was a significant decline in its velocity, with an average of only 9.6 m/year. Subsequently, from 2000 to 2007, there was a slight increase to an average maximum velocity of 11.9 m/year. Between 2008 and 2010, the velocity decreased again, averaging to 8.9 m/year. During years 2011 to 2013, we observed a stabilization in velocity, maintaining an average of 11.2 m/year. Finally, from 2014 to 2018, the average maximum velocity remained consistent at 11.2 m/year. These fluctuations in glacier velocity suggest dynamic changes in its behavior over time, reflecting periods of both acceleration and deceleration.
The slope of the glacier surface in our dataset ranges from 0 to 29.2 (Figure 9a). In the avalanche zone of the Medvezhiy glacier, a sharp change in slope was observed, where contour lines of elevation are shown, and a rapid increase in height can be observed, with a difference of 250 m (Figure 9c). Our results showed that the maximum elevation (the elevation of the highest point of the glacier) for the Medvezhiy glacier using DEM and glacier outline varies approximately within the range of 5906 m (a.s.l.), respectively (Figure 9c).
The values for hillshade, generated in ArcGIS, typically fall within the range of 0 to 180, where 0 represents the darkest shadows, and 180 represents the brightest areas. The hillshade result of the glacier is shown in Figure 9b, where lower values (0–26) represent areas in deep shadow, where the relief blocks direct sunlight; moderate values (74–117) represent areas that are partially shaded, where the relief partially blocks direct sunlight; and higher values (154–180) represent areas that are fully exposed to the impact of direct sunlight, where the terrain does not obstruct the incoming solar radiation. Our newly obtained results revealed the Medvezhiy glacier retreated approximately about 1600 m between 1968 and 2023.

5.1. Unveiling Glacier Surge Dynamics

The Medvezhy glacier’s surges occurred at regular intervals, typically every 10–16 years, with documented events in 1916, 1937, 1951, 1963, 1973, 1989, 2001, and 2011 [70]. While an unobserved surge possibly took place in the 1920s, the 1916 surge was identified by an ice dam and the 1937 surge by local reports [69]. Researchers recorded the 1951 surge, noting the glacier’s rapid kilometer-long advancement over a few days. The periods between the 1963 and 1973 surges experienced unusually cold summers, with 1972 being the coldest in 80 years [24,71]. Temperature assessments showed a decrease in average temperature during the warmer half of the year for Medvezhy glacier, declining from 7.2 °C (averaged between 1954–1973) to 6.2 °C in 1972, coinciding with a period where approximately 65% of the surging glaciers in the Pamir region underwent surges during the 1970s [24]. Moreover, the Medvezhy glacier’s surge aligned with the sole recorded surge of the Abramov glacier in the Alai Range [72], indicating a synchronization of glacier dynamics. The satellite image animation revealed that glacier surges typically initiate from the middle part of glacier, a common occurrence where a mass of ice from the accumulation zone descends due to the force of gravity [73].
The glacier’s length was 13.3 km in 1968, and its area changed to 31.71 km2, as revealed by Corona KH-4B observations. The eastern side of the glacier advanced by 528 m, and its size increased by 0.55 km2, both of which were the highest splash during the initial period of climb in 1973. To estimate the second spike, data from ITS_LIVE and Landsat TM were utilized. The primary reason for this conclusion is the cessation of uplift due to climate warming. Some researchers believe that the speed of the glacier’s surge decreased when the final recession occurred in 2011, causing the uplift to cease, which is consistent with our result, which clearly illustrates the glacier’s annual retreat (Figure 4). However, we observed a continued forward surge of the midpoint of the glacier from 2014 to 2022. When combined with the sharp decrease in glacier velocity after the August 2011 recession, we hypothesize that the glacier’s uplift ceased following the recession in July 2011. At least 49 surge-type glaciers, categorized into three groups, are located in the Vanj Basin, according to Osipova [8]. The mechanism that causes the rise of the Medvezhiy glacier is relatively complex and can be influenced by three factors: hydrological, thermal, and geomorphological. From 1968 to 2023, six GLOFs occurred on the Medvezhiy glacier, resulting in catastrophic damage to residents downstream of the Vanj River. These findings are consistent with Osipova (2015), who previously described similar events in the Medvezhiy glacier. These events took place following outbreaks in 1968, 1973, 1975, 1977, and 1989–1990 and the last GLOF event during the dormant phase in 2011. Concurrently, the speed of the glacier’s surge increased from 0.1–0.15 m/day to 50 m/day [74].

5.2. Interplay of Climate Change

Additionally, an analysis of temperature and precipitation changes based on data from the Humrogi station revealed an increase in air temperature and a decrease in precipitation after 2000 (refer to Figure 10b,c). This indicates an insufficient amount of snow in the glacier accumulation zone, resulting in an inadequate increase in mass due to climate change. Therefore, monitoring the full surge cycle is beneficial for further understanding the mechanism of glacier surges. We anticipate that future research will enhance the monitoring of recurring glacier surges. The observed changes in climatic data found that in this area, the decrease in annual precipitation was 32.9 mm or 14% from the annual precipitation amount. Following a thorough analysis of the average annual air temperature and annual precipitation data from the Humrogi station, it became apparent that post 2000, there was a notable increase in air temperature alongside a decrease in precipitation in the study area. These trends contribute to an accelerated process of glacier degradation. When comparing the air temperature to the glacier movement, the picture more vividly displays their correlation. In order to determine the dependence of the surge of the Medvezhiy glacier’s terminus position on meteorological conditions (atmospheric precipitation and air temperatures), graphs of glacier surge and changes in meteorological conditions were constructed for the observation periods.
Figure 10c illustrates the precipitation changes of a parabolic pattern to a negative value after 2000, and the Medvezhiy glacier terminus position’s retreat accelerated at the same time (Figure 10a). Thus, it can be hypothesized that there is a functional connection between these phenomena. During the observation period (1968 to 2023), the air temperature displayed a tendency of significant increase, and the glacier gradually moved away from its previous position (Figure 10b). Our integrated analysis (satellite and ground data) demonstrated a significant correlation between the Medvezhiy glacier’s retreat and the temperature increase, validating the relationship between glacier behaviors and rising temperatures. These findings are consistent with other studies that have also observed the influence of rising temperatures on glacier retreat in the Himalayas and Pamir [75,76,77,78]. Overall, the 55 years of observations indicate that the glacier tongue has moved 1.6 ± 0.03 km away from its previous position and reached its highest point in 2023.

5.3. Perils of Glacier Surges

The Abdukahor subglacial lake serves as a primary source of devastating floods and has received special research attention. Monitoring of the lake’s accumulation and discharge has been ongoing since 1963 (Table 4). Detailed surveys of the lake basin following drainage have provided insights into its volumes and the patterns of outbursts. Studies indicate that factors influencing the lake’s formation, volume, timing, intensity, and outburst trajectories extend beyond the morphology and size of the ice dam. They also encompass the intricacies of ice movement during surges and the resultant glaciotectonic formations within the glacier. Notably, the path of the lake’s discharge across the Medvezhiy glacier was influenced by the presence of a deep crevasse. During surges, the upper ice layer traversed this heavily fractured dead ice layer, as illustrated in Figure 11. It is shown that the Medvezhiy glacier obstructs the Abdukahor River following surge, forming a glacial lake during the first burst in the Corona 4B image on 18 August 1968 (Figure 11a). Nevertheless, the August 1968 photo did not show this glacial lake. On the other hand, the presence of signs of a glacial lake breach in the 1971 Corona 4B photograph suggests that this event happened at this time (Figure 11b). The 1973 Landsat satellite image indicates that the new terminal glacial lake covered 0.36 km2. A new glacial lake (0.34 km2) was created in 1977 (Figure 11d). The area of the next GLOF reached its maximum during the surge phase of 1989–1990 (Figure 3), at which point a breach happened (Figure 11e). There is evidence of the formation of a small terminal glacial lake from a Landsat image from 1991. As a result, the glacial lake’s collapse put an end to the glacier’s ascent. The last glacial lake formed in 2011 during the last phase of uplift (Figure 11f). Relying on the glacier’s surge speeds and intervals, we believe that the next Medvezhiy glacier surge, which would happen in the future, will block the Abdukahor River and may create a GLOF.

6. Conclusions

The comprehensive analysis of the Medvezhiy glacier surge from the time from of 1968 to 2023, which incorporated Corona KH-4B, Hexagon KH-9, Landsat 5–8, UAVs, and station climate records, offers valuable insights into the dynamic behavior of glaciers in response to changing environmental conditions. This study emphasizes the surging tendencies of the Medvezhiy glacier in the Vanj River Basin and the associated risks of flooding and GLOFs to the local populace. Changes in summer air temperature and precipitation since the 21st century have led to reduced ice influx, increased melting, and fewer glacier surges, with the last surge occurring in May 2011. Thus, its dormancy since then raises concerns about potential future surges due to accumulated mass. Based on our obtained date from above-mentioned methods, we concluded the following: (1) Based on our observation data, we revealed that the since the 1968 to 2023 period, the Medvezhiy glacier changed its terminus position by about 1600 m. (2) According to the analysis of climatic data, since the 2000s, an increase in temperature and a decrease in precipitation have been observed in the study area, which led to the melting of the final position of the glacier, and this provoked the dumping of mass from the middle part to the ablation area in 2001. (3) The Medvezhiy glacier is the most observed glacier in Tajikistan, and it may represent potential danger (GLOFs) for the downstream habitants. Thus, it is crucial to observe any changes of the glacier, as the consequences of not doing so may lead to catastrophe. The detailed study and observations may prevent loss of life.
The accumulated masses/or zone may provide a potential mechanism for future surges because of gravity flow due to its location on the slope.

Author Contributions

M.M. and L.L. contributed to the main idea, work design, data collection, formal analysis, and original draft preparation; M.M., M.S., K.K. and M.L. were involved in statistical analysis, data collection, experiment conduction, data analysis, and paper writing; Y.Q. edited and proofread the manuscript with discussion on the results; the experiments in Section 4.3 were performed by A.M.; the main ideas behind the experiments were conceived by K.K. and M.L., with many helpful suggestions from L.L.; L.L. and A.G. provided supervision and resources for the study. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Projects of National Natural Science Foundation of China (42230708), the Bureau of International Cooperation, Chinese Academy of Sciences (072GJHZ2023086MI), the National Natural Science Foundation of China (42071245), the provincial talents project (E3370302), the K. C. Wong Education Foundation (GJTD-2020-14), and Xinjiang Jiaotou’s unveiling and commanding program in 2021 (ZKXFWCG 2022060004).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

Murodkhudzha Murodov would like to express his sincere gratitude to the Research Center for Ecology and Environment in Central Asia (Dushanbe) and the State Scientific Institution “Center for Research of Glaciers of the National Academy of Sciences of Tajikistan”. Special thanks to Chen Xi from the Xinjiang Institute of Ecology and Geography (XIEG) for his financial support in publishing this paper. Corona, Hexagon, Landsat, and Sentinel data were collected from the U.S. Geological Survey and the Copernicus Open Access Hub (https://landsat.usgs.gov/; https://scihub.copernicus.eu/dhus (accessed on 8 March 2024)). The permanent glaciers information was obtained from the USSR Glacier Inventory (Osipova, 1978). Observation station data were collected from the Agency for Hydrometeorology of the Committee for Environmental Protection under the Government of the Republic of Tajikistan, and the DEM was downloaded from the Shuttle Radar Topography Mission (SRTM) website (https://srtm.csi.cgiar.org/ (accessed on 8 March 2024)). For financial support throughout his doctoral studies, the first author is especially appreciative of the University of Chinese Academy of Sciences (UCAS) Scholarship program (http://english.iue.cas.cn/et/scs/UCAS/ (accessed on 8 March 2024)).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (A,B) The location of Pamir region in the map of Tajikistan (solid black line in figure (A)), (C) Vanj River Basin with Medvezhiy glacier (inset), (D) 3D Medvezhiy glacier model, and (E) glacier outlines and water inflow (manually digitized from Landsat image, 1973) and DEM.
Figure 1. (A,B) The location of Pamir region in the map of Tajikistan (solid black line in figure (A)), (C) Vanj River Basin with Medvezhiy glacier (inset), (D) 3D Medvezhiy glacier model, and (E) glacier outlines and water inflow (manually digitized from Landsat image, 1973) and DEM.
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Figure 2. River flow overlaps and breakthroughs, with specific references as follows: (1,2) Hexagon KH-9 images from 12 June 1973 and 20 August 1980 (blue and yellow masks indicate the zones where GLOFs have formed); (3) Landsat-5 image from 14 June 1991; (4) UAV image from 20 August 2023.
Figure 2. River flow overlaps and breakthroughs, with specific references as follows: (1,2) Hexagon KH-9 images from 12 June 1973 and 20 August 1980 (blue and yellow masks indicate the zones where GLOFs have formed); (3) Landsat-5 image from 14 June 1991; (4) UAV image from 20 August 2023.
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Figure 3. Comparing the progression and regression of Medvezhiy glacier before and after, using (a) Corona, (b–d) Hexagon, and (e,f) Landsat data: The lines depict the initial position of the glacier terminus in early images and its position in the latest images.
Figure 3. Comparing the progression and regression of Medvezhiy glacier before and after, using (a) Corona, (b–d) Hexagon, and (e,f) Landsat data: The lines depict the initial position of the glacier terminus in early images and its position in the latest images.
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Figure 4. Comparing the progression and regression of Medvezhiy glacier before and after, utilizing Landsat data: Subfigures present the glacier’s terminus positions in four intervals: 2001 (a), 2002–2010 (b), 2011–2019 (c), and 2020–2023 (d). The lines illustrate the initial position of the glacier terminus in early images and its position in the latest images.
Figure 4. Comparing the progression and regression of Medvezhiy glacier before and after, utilizing Landsat data: Subfigures present the glacier’s terminus positions in four intervals: 2001 (a), 2002–2010 (b), 2011–2019 (c), and 2020–2023 (d). The lines illustrate the initial position of the glacier terminus in early images and its position in the latest images.
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Figure 5. The location of the Medvezhiy glacier is as follows: (1) Osipova took this photo in June 1973; (2) Murodov M. took this photo on 20 August 2023. The red and yellow lines denote significant changes in the Medvezhiy glacier’s position over time.
Figure 5. The location of the Medvezhiy glacier is as follows: (1) Osipova took this photo in June 1973; (2) Murodov M. took this photo on 20 August 2023. The red and yellow lines denote significant changes in the Medvezhiy glacier’s position over time.
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Figure 6. Annual velocity profiles along the central branches of the Medvezhiy glacier from 1988 to 2018. The center line of the branch is formed by points (Data source: ITS_LIVE).
Figure 6. Annual velocity profiles along the central branches of the Medvezhiy glacier from 1988 to 2018. The center line of the branch is formed by points (Data source: ITS_LIVE).
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Figure 7. Curves of the average and maximum speed of glacier movement over time. Red vertical lines indicate maximum velocity.
Figure 7. Curves of the average and maximum speed of glacier movement over time. Red vertical lines indicate maximum velocity.
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Figure 8. Surface elevation change of Medvezhiy glacier from 2000–2019 m/year−1. Subfigure (A) provides an overview of the glacier’s elevation change throughout the entire period. Subfigures (B–E) zoom in on specific time intervals: 2000–2004, 2004–2009, 2010–2014, and 2015–2019, respectively. Cool colors indicate positive mass balance, whereas warm colors indicate negative mass balance.
Figure 8. Surface elevation change of Medvezhiy glacier from 2000–2019 m/year−1. Subfigure (A) provides an overview of the glacier’s elevation change throughout the entire period. Subfigures (B–E) zoom in on specific time intervals: 2000–2004, 2004–2009, 2010–2014, and 2015–2019, respectively. Cool colors indicate positive mass balance, whereas warm colors indicate negative mass balance.
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Figure 9. An illustration of the Medvezhiy glacier using: (a) slope, (b) hillshade, (c) topography, and (d) aspect.
Figure 9. An illustration of the Medvezhiy glacier using: (a) slope, (b) hillshade, (c) topography, and (d) aspect.
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Figure 10. (a) Terminus position change of Medvezhiy glacier from 1968 to 2023, (b) average annual temperature, and (c) annual precipitation at Humrogi station.
Figure 10. (a) Terminus position change of Medvezhiy glacier from 1968 to 2023, (b) average annual temperature, and (c) annual precipitation at Humrogi station.
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Figure 11. GLOF formation on the left bank of the Medvezhiy glacier. Subfigures (a–f) present satellite images captured at different times: (a) Corona KH-4B (18 August 1968), (b) Landsat-5 (12 July 1973), (c) Hexagon KH-9 (13 July 1975), (d,e) Landsat-5 (6 August 1977) and (14 July 1991), and (f) Landsat-8 (22 August 2011). The yellow arrows illustrate the zone where GLOFs form after a glacier surge.
Figure 11. GLOF formation on the left bank of the Medvezhiy glacier. Subfigures (a–f) present satellite images captured at different times: (a) Corona KH-4B (18 August 1968), (b) Landsat-5 (12 July 1973), (c) Hexagon KH-9 (13 July 1975), (d,e) Landsat-5 (6 August 1977) and (14 July 1991), and (f) Landsat-8 (22 August 2011). The yellow arrows illustrate the zone where GLOFs form after a glacier surge.
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Table 1. Statistics of the observed and simulated monthly average temperature and annual precipitation between Humrogi station and CHIRPS.
Table 1. Statistics of the observed and simulated monthly average temperature and annual precipitation between Humrogi station and CHIRPS.
NamePCCR2NSEKGEPBIAS (%)
Average Temperature, °C0.960.93−0.41−11.502026.05
Annual Precipitation, mm0.680.460.04−10.4940.52
Table 2. Main characteristics of the satellite scenes used.
Table 2. Main characteristics of the satellite scenes used.
Date of AcquisitionImageResolution (m)PATHROWPurposeSource
18 August 1968
15 September 1971		Corona KH-4BStereo High ~1.82DS1104-2169DA104b, 105b, 106bMap glacial surging during the period 1968–2023USGS Earth Explorer
12 July 1973Hexagon KH-9~4.72DZB1206-500007L016001
13 July 1975Hexagon KH-9~4.72DZB1210-500134L006001Observations on the rise of the Medvezhiy glacier and the emergence of a glacial lake
12 July 1973
6 August 1977		Landsat-5Thematic Mapper 60 × 60
(NASA, United States of America (U.S.A), California)		163033
20 August 1980Hexagon KH-9~4.72DZB1216-500273L008001
30 May 1989
30 March 1990
14 July 1991		Landsat-5Thematic Mapper 30 × 30
(NASA, U.S.A, California)
16 September 2000Landsat-7 “ETM+”Enhanced Thematic Mapper Plus 30 × 30
(Lockheed Martin Space Systems, U.S.A, California)		163033Identification of hazardous areas
26 July 2001
5 August 2002
18 September 2003
4 September 2004Change in the area of glaciers
10 September 2006
13 September 2007
13 August 2008Landsat-5Thematic Mapper 30 × 30
15 July 2009
4 September 2010
22 August 2011
27 August 2013Landsat-8Operational Land Imager (OLI)
30 × 30
(NASA and USGS, U.S.A, California)		Observations of the latest surge of the Medvezhiy glacier
30 August 2014
17 August 2015
3 August 2016
22 August 2017
25 August 2018
28 August 2019
30 August 2020
20 August 2023DJI Phantom 40.15Glacier terminus position
12 February 2000SRTM30Elevation change
1989–2018ITS_LIVE240Annual glacier velocity
Table 3. Changes in the surface elevation of the glacier.
Table 3. Changes in the surface elevation of the glacier.
YearsMaximum Decrease in the Middle Part m/YearMaximum Increase in Tongue Part m/Year
2000–20046.1−4.8
2005–20095.1−2.7
2010–20147.3−3.08
2015–20195.7−4.8
Table 4. The 55-year study of the Medvezhiy glacier’s surges (1968–2023).
Table 4. The 55-year study of the Medvezhiy glacier’s surges (1968–2023).
YearTerminal Advance, mGlacier Area, km2The Volume of the Dammed LakeObservation Type
19631750-14.5 million m3, with catastrophic breaks across the glacierGround reconnaissance [28]
1973192532.2616.4 million m3, with catastrophic breaks across the glacierGround observations, phototheodolite surveys [28]
1975126031.62-Satellite data (Landsat TM)
1977125032-Satellite data (Hexagon KH-9)
1988–1989110031.10The volume is insignificant, the descent along the left edge of the glacierAero-topographic monitoring, and ground measurements of Tajikhydromet [28]
200145030.69The lake did not formAgency for Hydrometeorology of the Committee for Environmental protection under the Government of the Republic of Tajikistan [28]
2011More than 800 m31.22
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Murodov, M.; Li, L.; Safarov, M.; Lv, M.; Murodov, A.; Gulakhmadov, A.; Khusrav, K.; Qiu, Y. A Comprehensive Examination of the Medvezhiy Glacier’s Surges in West Pamir (1968–2023). Remote Sens. 2024, 16, 1730. https://doi.org/10.3390/rs16101730

AMA Style

Murodov M, Li L, Safarov M, Lv M, Murodov A, Gulakhmadov A, Khusrav K, Qiu Y. A Comprehensive Examination of the Medvezhiy Glacier’s Surges in West Pamir (1968–2023). Remote Sensing. 2024; 16(10):1730. https://doi.org/10.3390/rs16101730

Chicago/Turabian Style

Murodov, Murodkhudzha, Lanhai Li, Mustafo Safarov, Mingyang Lv, Amirkhamza Murodov, Aminjon Gulakhmadov, Kabutov Khusrav, and Yubao Qiu. 2024. "A Comprehensive Examination of the Medvezhiy Glacier’s Surges in West Pamir (1968–2023)" Remote Sensing 16, no. 10: 1730. https://doi.org/10.3390/rs16101730

APA Style

Murodov, M., Li, L., Safarov, M., Lv, M., Murodov, A., Gulakhmadov, A., Khusrav, K., & Qiu, Y. (2024). A Comprehensive Examination of the Medvezhiy Glacier’s Surges in West Pamir (1968–2023). Remote Sensing, 16(10), 1730. https://doi.org/10.3390/rs16101730

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