1. Introduction
Glaciers in the High Mountains of Asia (HMA) are significant freshwater resources nourishing billions of people downstream [1,2], and their rapid retreat under the global warming trend in recent decades has inevitably aggravated the instability of runoffs [3,4]. This raises concerns not only of disasters such as floods but also of water-scarcity issues [5,6]. It is of immense importance to estimate the past ice loss properly so that confidence and reliability can be found in the projects and policies facing future regional development.
The ice loss of glaciers originates from surface thinning and snout retreat, and both can commonly be estimated through comparing surface elevation change based on airborne and satellite remote sensing images [3,7,8,9]. Accompanied by models and meteorological data, glacier decadal or annual mass loss dynamics can be revealed [10,11]. However, the ice loss of lake-terminating glaciers is different from that of other glaciers. Since the regions of their snout are occupied by lake water after retreat [12], neither optical nor microwave remote sensing methods can penetrate deep lakes and receive ice change information in water-covered regions. Only by a sophisticated field study of lake depth can the lake volume be calculated [13,14], and ice loss at water-covered areas can be adequately revealed. Hence, although glacial lakes influence glacier movement and partly exacerbate glacier mass loss [15,16,17], due to the complexity and difficulty of data collection, the subaqueous ice loss of lake-terminating glaciers has been neglected in previous studies [9,18].
Recently, along with the development of measurement and communication technology, sonar technology and unmanned surface vessels with bathymetric survey systems have gradually been utilized in glacial lake depth investigations [19,20]. Their convenience and reliability have made it much easier to acquire subaqueous terrain data. Subsequently, not only was the ice loss of lake-terminating glaciers in a case study evaluated by separating the glacier into different parts according to their spatial location and ablation discrepancies, such as surface lowing area and lake-related snout retreat area [21], but using sound lake bathymetry data and an updated Area–Volume formula of glacial lakes, an underestimated ice loss result of 6.5% was brought forward in the greater Himalayas, owing to the neglect of subaqueous ice loss [22]. Due to their remote location and harsh environment, there was still a data gap for large glacial lakes, especially lakes bigger than 2 km2 [22]. The diversity of glacial lakes makes it hard not to question whether these giant lakes perform as predicted. It emphasizes that more lake bathymetry data should be collected in the gap. At the same time, the analysis of ice–lake interaction and its influence on the acceleration of glacier retreat can be achieved by further dividing ice loss’s contribution into both underwater and above water, which has been scarcely carried out in the HMA recently. With reliable evidence and an evaluation of ice loss status, a more accurate estimation of water resources in the HMA and effective reduction in regional disaster risks can be achieved.
The present study aims to provide a case study of the Jiongpu glacier, one of the biggest lake-terminating glaciers in the HMA. Through a long time series of remote sensing images, area changes in the Jiongpu glacier and Jiongpu Co were extracted over the past few decades. Along with a bathymetric survey, the reconstruction of a typical result of ice loss of the lake-terminating glacier after the rapid retreat was made to clarify the contribution of ice loss character and different loss rates. The target glacier was divided into three parts, above lake, under lake, and glacier region. It is expected that the present study’s results can be a reference for regional glacier ice loss estimation for the past few decades and local sustainability plans in the near future.
2. Materials and Methods
2.1. Study Region
Jiongpu Co [94.50°E, 30.65°N, 3882 m above sea level (a.s.l.)] is a proglacial lake that has shown evident expansion over the last few decades. Consequently, the lake is currently the largest proglacial lake in southeast TP. The Jiongpu Co is located at the northeast headwaters of the Yigong Zangbo River, the central region of the Nyainqentanglha Mountain, as shown in Figure 1A. The Yigong Zangbu River is a tributary of the Yarlung Zangbu Rive. The annual average temperature and total annual precipitation of the Jiongpu Co valley from 2020 to 2021 were 4.2 °C and 774 mm, respectively, according to the weather station located at the end-moraine dam of Jiongpu Co. The distance to the nearest downstream township, the Jinling township, is around 12 km.
2.2. Delineation of the Glacier and Glacial Lake
Satellite images of the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Sentinel 2 MultiSpectral Instrument (MSI) were utilized in Jiongpu Co lake to outline the delineation process. The change in the area of Jiongpu Co from 1976 to 2021 was obtained through artificial visual interpretation (Table S1).
The uncertainty of the glacial lake area was estimated as an error of ±0.5 pixels on either side of the delineated lake boundary, which was ±3% of the lake area in 2021.
2.3. Bathymetry of the Glacial Lake
An unmanned surface vessel (USV) equipped with a sonar system was used in Jiongpu Co Lake to conduct a volume survey [19]. The system consisted of four main components including a data-acquisition module, a data transmission module, a positioning and navigation control module, and a power module. A single-beam sonar with a frequency of 200 KHz was used to generate depth data with an error of ± (1 cm + 0.1% H), where H is the depth value obtained.
Three field surveys (August 2020, May 2021, and July 2021) recorded the lake bathymetry of Jiongpu Co. The Lake bathymetry of Jiongpu Co was calculated through the universal kriging spatial interpolation of over 13,000 lake depth data sample points, as elaborated in Figure S1.
The reliability of the bathymetry results was assessed by measuring the depth at seven points using a measuring rope, with the results shown in Table S2. In comparison with the measuring rope results, the spatial interpolation of depth had an average error of 5 m, less than 4%.
The level of Jiongpu lake surface did not change much since its stable outlet in recent decades, and in fact, its seasonal dynamics between maximum and minimum water levels were less than 2 m, recorded by a water level camera. Hence, in most situations of the present study, the lake level of Jiongpu glacial lake was set as a constant value, 3882 m a.s.l.
2.4. Dataset of Digital Elevation Models
Digital elevation models (DEMs) are the main data sources for evaluating glacier surface elevation change and obtaining ice loss information. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) provides global surface elevation with a spatial resolution of 30 m. The ASTER GDEM data acquisition date of the Jiongpu glacier was 2000, providing the data basis for estimating ice loss.
The mission of TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) generated a global DEM product acquired between 2010 and 2015, and its released version named Edited DEM (EDEM) with a spatial resolution of 30 m was used in acquiring the glacier surface elevation of the Jiongpu glacier. In 2017, the consequent mission of TanDEM was decided to acquire an additional global coverage to generate a new independent, more up-to-date global DEM and also to provide the TanDEM-X 30 m DEM Change Maps (DCM) [23]. Two newly released versions of DCMs, separately named FIRST and LAST, will be released according to their acquired date. The LAST version acquired in the Jiongpu Glacier was in 2020, which, accompanied with the EDEM before, can provide a DEM dataset on the Jiongpu glacier in 2020.
The unmanned aerial vehicle (UAV) photogrammetric technique can generate a detailed and high-precision digital surface model (DSM) in small regions [24]. In fact, data misses and an obvious smoothing process exist in the TanDEM dataset, especially in the conjunction region between the glacier and the glacial lake. Thus, in this study, a UAV was used to collect a DSM in the lake–ice conjunction region to supplement and rectify the DEM products in case of deficiency. The survey date is 27 July 2021.
Owing to the complicated landform and large drop in high mountain regions, and although the accuracy of DEM products was analyzed and showed acceptable results, it was necessary to reanalyze the uncertainty in the present study region and rectify the products as much as possible. Several control points at the end-moraine of the Jiongpu glacier were established through the real-time kinematics technique in the present study (Table S3). For ASTER GDEM, the data were adjusted using lake surface elevation (3882 m a.s.l.) first, assuming the lake surface is absolutely flat, and the control point comparison illustrated an average error of 6.33 m for the revised ASTER GDEM; for the TanDEM series data, the average error was 2.39 m compared with control points; for the UAV DSM data, since they were rectified based on TanDEM EDEM data, the error should be same as the TanDEM series data.
2.5. Ice Loss Pattern and Spatial Classification
Various parts of the lake-termination glacier faced several factors and followed different patterns during the glacier retreating process, especially in the lake–glacier conjunction region. It is important to divide the glacier into several regions oriented by spatial differences in the ice loss reconstruction process so that ice losses of different forms and attributions can be easily categorized and analyzed. Since the proglacial lake nourished in front of the lake-terminating glacier, Jiongpu Co followed the lake-expanded mode after the glacier retreat [12]. During the process, underwater glacier ice loss was mostly influenced by water–ice heat exchange. Glacier ice loss above water has a direct connection with ice loss underwater, such as caving and collapse owing to bottom melt, as well as different characteristics, for example, surface thinning influenced by moraine coverage. Meanwhile, the upper area of the glacier, which had no direct contact with the glacial lake, was controlled by radiation and air temperature. So, it is reasonable to separate these different regions in ice loss estimations to obtain enough components for the analysis of glacier dynamics in the past.
Figure 2A,B elaborates on an ideal retreat pattern of the lake-terminating glacier in a simple profile. In this ideal pattern, the ice loss of lake-terminating glaciers would consist of three parts: ice loss above water, ice loss underwater, and ice loss of glacier surface. It is noteworthy that during the evaluation process of ice loss in the Lake region, the buoyancy-induced snout floating was also considered based on the ice–lake contact model in previous research [25] (* area in Figure 2A). On the plane, the first two parts happened in the snout retreat region, and the last part is currently on the glacier surface. Hence, from a bird’s-eye view, two main regions, the Glacier and the Lake regions, were also used for categorization in this study, as depicted in Figure 2C.
In practice, ice loss in the Glacier region not only contained surface thinning but also lateral shrinkage induced by snout retreat. Figure 2D shows the spatial location of the shrink area in a zoomed picture. Besides the shrinking area, the UAV investigation area used for supplement and verification was also highlighted in the picture. Thus, ice loss, in general, was estimated and reconstructed through the following equation.
Mtotal = Maw + Muw − Mcav + Msurf + Mshr,(1)
In the equation, Mtotal means the total ice loss of the glacier. Maw and Muw are ice loss above water and underwater, and Mcav is ice loss that is overestimated owing to the cavity caused by snout floating. Msurf and Mshr are ice loss on the glacier surface and in the shrinkage area. A constant value of 900 kg·m−3 was used, as recommended by Ref. [26].
2.6. Ice Loss Calculation in Different Parts
Here, according to the spatial characteristics of ice loss of the Jiongpu glacier, we used the Lake region and Glacier region as fundamental categorizations. The calculation workflow of ice loss in each part was designed using lake bathymetry, DEMs, and area change data, as described in Figure 3.
For the ice loss in the Lake region, which contained ice loss above water (① in Figure 2B) and ice loss underwater (② in Figure 2B), satellite imageries were used to extract the glacier retreat region from 2000 to 2020. Then, ice loss above water (①) was obtained through ASTER GDEM data minus the lake surface elevation (3882 m a.s.l.). Ice loss underwater (②) in that region was calculated utilizing the corresponding lake volume data generated from the lake bathymetry data. The potential floating area (* in Figure 2A) of the Jiongpu glacier in the year 2000 was evaluated based on density differences between ice and water, as ice volume floating above water and submerged underwater should be 1:9. Hence, it is assumed that the cavity existed from the glacier terminus to where the ice height above water was continuously greater than 1/9 of the lake depth. Figure 2E discloses the potential floating ice area at the terminus of the Jiongpu glacier in 2000.
For the ice loss in the Glacier region, which mainly consists of ice loss of the glacier surface (③ in Figure 2B), the ice body loss was calculated from the surface elevation change data simply using DEMs from 2000 (ASTER GDEM) to 2020 (TanDEM EDEM and DCM). In the process, the DSM based on the UAV photogrammetry method was employed to verify and fix the DEM of the TanDEM series data. In the Shrink area, as explained in Figure 2D, where the glacier retreated while bed rocks and moraines emerged, the ASTER GDEM and TanDEM verified by DSM were also used to evaluate the ice loss induced by lateral shrinkage of snout retreat. Still, a difference remains in that, because of partial data hollows in TanDEM products, spatial interpolation was conducted based on the elevation data at glacier outlines in 2000 from ASTER GDEM and in 2020 from DSM. The comparison of the ASTER GDEM with the spatial interpolation results led to the ice loss results in the Shrink area.
3. Results
3.1. Area Dynamics and Volume Investigation of Jiongpu Co
The proglacial lake Jiongpu Co expanded from 1.19 ± 0.09 km2 to 5.34 ± 0.07 km2 from 1976 to 2021, exceeding the previous size by a factor of nearly 3.5 times, as elaborated in Figure 4A. The lake experienced slight expansion before 2006. After 2007, the lake grew rapidly and reached its maximum area in 2014 and 2015, with an average yearly increment of up to 0.5 km2. After 2015, the lake area gradually stabilized, as depicted in Figure 4B. The expansion of Jiongpu Co could be directly attributed to the space generated by the retreat of its host glacier. The host glacier of Jiongpu Co retreated to a higher and steeper bedrock slope. Therefore, the stable dynamics of the lake area could be foreseen, considering the stable outlet channel of the lake over decades.
The bathymetry results show that the lake reached an average depth of 124.54 m, with the deepest depth recorded at 245 m. The volume of the lake was 0.66 ± 0.03 Gt during the summer of 2021, calculated using universal kriging spatial interpolation based on depth sampling points. The morphology of the lake basin was consistent with the typical geomorphic characteristics of glacial erosion. From downstream to upstream, the lake bathymetry formed a giant U-shape valley, with an open and flat bottom and a steep bank, as shown in Figure 5.
3.2. Total Ice Loss of the Jiongpu Glacier during 2000 to 2020
The total ice loss of the Jiongpu glacier was 1.52 ± 0.37 Gt from 2000 to 2020. Less than 2/3 of the ice loss came from the Glacier region, and about 1/3 of the ice loss happened in the Lake region, as presented in Figure 6.
In the Lake region, the ice loss underwater was 0.32 ± 0.01 Gt, contributing around 21% of the total ice loss, while the ice loss above water was 0.19 ± 0.02 Gt, occupying 12% of the total ice loss. Nearly 2/3 of the ice loss in the Lake region was derived from the ice loss underwater. Hence, the ice loss underwater was the dominating part in the Lake region. It should be noted that the result depicted in Figure 6 shows the ice loss after the deduction of floating snout influence, which means that ice loss resulting from the cavity under glacier termini was subtracted, and its quantity was 0.04 Gt.
In the Glacier region, the surface thinning-induced ice loss contributed the majority, which was 0.91 ± 0.34 Gt and took 90% in this region. Meanwhile, ice loss in the Shrink area induced by glacier shrinkage was the minority, and was only 0.1 ± 0.003 Gt and took around 10% in the Glacier region.
From the perspective of ice loss reason, i.e., retreat and surface thinning, glacier retreat led to more than 40% of the ice loss (ice loss in the Lake region and lateral shrinkage in the Shrink area), while glacier surface thinning brought about the remaining 60% of ice loss (ice loss in Glacier region without lateral shrinkage at Shrink area).
4. Discussion
4.1. New Volume Record with Underestimation Result of Glacial Lake
Jiongpu Co is currently the largest proglacial lake in the southeast TP, with a width and length of 1 km and 7 km, respectively (7th in the HMA), and is only smaller than the biggest nonglacial lake, the Basomtso Lake, according to the glacial lake inventory of Ref. [27]. The bathymetry of Jiongpu Co also revealed an astonishing volume of 0.66 ± 0.03 Gt. The present study found that it is even equivalent to that of a large-scale reservoir in comparison with other glacial lakes in the vicinity of the TP, with a recorded average depth of 124.54 m.
Although the glacial lake depth was gradually surveyed using modern mapping methods in recent years [20,28], limited by the remote location of glacial lakes, few proglacial lake volume detection projects were carried out in the entire TP in fact. To properly evaluate the status of glacial lake water resources or the potential disaster risk of outburst flood, Area–Volume (A-V) empirical equations were developed and employed in previous studies [29]. The present study used A-V methods which were mainly employed in related study regions to predict the volume of Jiongpu Co. The results disclose that the large volume of Jiongpu Co did not fit the Area–Volume (A-V) empirical equations used in previous studies, as shown in Figure 7A, and the simulation uncertainty ranged from 16% to 65% (Table S4). All predictions by the A-V method underestimated the water storage of Jiongpu Co. The underestimation of proglacial lake volume not only implied that there was room for accuracy growth in the A-V methods but suggested there might be more over-deep glacial lakes forming in the future or whether some glacial lakes with large water storage beyond our imagination already existed while not recognized. Ref. [22] collected 211 volume survey records of all kinds of glacial lakes with areas ranging from 3390 m2 to 6 km2, as illustrated in Figure 7B. The data collection also revealed a severe shortage of volume survey for big lakes larger than 2 km2. In fact, according to the glacial lake inventory of Ref. [27], there were about 77 glacial lakes larger than 2 km2 in the TP. Thus, to better understand the increasing volume of proglacial lakes and reduce the risks brought on by under-evaluated water storage and consequent flood impacts, a broad and thorough investigation of the whole TP should be carried out.
4.2. Ice Loss Underestimation of the Jiongpu Glacier
Ice loss estimation was directly influenced by the underestimation of the glacial lake of Jiongpu Co. The rapid expansion of Jiongpu Co has already drawn attention in previous research. However, short of field survey data, the ice loss underwater could only be analyzed by the predicted lake volume through the A-V method. The results of previous research showed an underwater ice loss of 0.26 Gt in Jiongpu Co [22], and the underestimation of ice loss was 0.06 Gt, nearly 19% lower than in the present study.
Although it was shown using bathymetry data that considerable subaqueous ice loss underestimation is associated with lake expansion, the ice loss ratio of the Jiongpu glacier in different parts did not change obviously and, compared with Ref. [22]’s result, the ratio of subaqueous ice loss in the total ice loss only slightly adjusted from 20% to 21%. The reason was mainly because of the different considerations in the evaluation process for underwater ice loss. In fact, ice loss overestimation caused by water buoyancy and its influence could be large (0.04 Gt, 10% in underwater ice loss), while not evaluated in the previous studies. If such ice loss were not considered, the present study would show an underestimation result of 0.1 Gt, and the ratio of ice loss underwater would rise to 23% among the total ice loss. Otherwise, the different timeframes employed in previous studies also played a significant role in the ice loss results. In fact, without ice loss under water, Ref. [18] provided an ice loss result of 1.05 ± 0.39 Gt for the Jiongpu glacier from 2000 to 2019, while this was 1.20 ± 0.36 Gt from 2000 to 2020 in the present study. The underestimation of ice loss also might come from different DEM data resources. It cannot be denied that data deficiency objectively existed in all the DEM products, owing to terrain layover and thick cloud, etc., especially in the complex context in the HMA. Still, a feasible result would be foreseen with detailed investigation and systematical verification. Accordingly, the underestimation concluded in the present study is convincible.
The ice loss underestimation of the Jiongpu glacier exacerbated the influence of lake-terminating glaciers in the HMA. Due to its giant mass, regional misunderstandings of subaqueous ice loss could raise more important impacts on the research of water resources and hazards. The ice loss underestimation of the Jiongpu glacier induced by the astonishing volume of the glacial lake also created a reality in which, consistent with the potential existence of over-deep glacial lakes, ice loss underestimation could be more severe than in imagination. Hence, the rapid expansion of glacial lakes should draw more attention, not only to disaster prevention research.
The present ice loss result of the Jiongpu glacier caused by rapid lake expansion is also further proof of the influence of proglacial lakes on the acceleration retreat of lake-terminating glaciers. Although lake-terminating glaciers only account for a small portion of the glaciers in the HMA, their unique geometric and dynamic evolution, especially in the retreat and velocity induced by the development of proglacial lakes [17,35], have growing impacts on complicating and intensifying regional climate change and water cycles.
4.3. Limitations and Prospects
One limitation of the present study was that the reasons for ice loss in the Lake region were not diagnosed and categorized. The separation of ice loss based on spatial location could not reveal the contribution of each factor in the ice melt process, especially in the Lake region. Glacial caving and surface melting both accelerated the ice loss above water. In contrast, short of data support, the yearly dynamics of surface change in such regions were not obtained, and nor were the forms and quantity of glacial lake contrition to ice loss. To figure out the contribution of the glacial lake in glacier retreat acceleration, more meteorological data are needed, and both energy-balance models [36] and ice flow models [37] should be employed.
The brave assumption and calculation of snout lifting-induced overestimation in ice loss would be another limitation of this study. It was clear that absolute vertical glacier snout did not exist in nature; meanwhile, a protrusion on the top front resulting from flotation on the glacial lake, like ice caps or Alaska glaciers which flowed into the seas [25,38,39], or a protuberance on the bottom because of dead ice left after retreat [40] are more proper models that are closer to reality. In this study, the termini of glaciers were assumed to be ideally floated on the water and, thus, the influenced ice loss difference was calculated by considering the potential room to generate enough buoyancy to lift the volume of the glacier front. Although there were no observation data, it was still meaningful to try and evaluate the ice loss quantity under the floating front and illustrate that about 10% overestimation might exist in lake-terminating glaciers. More surveys and monitoring should be conducted to clarify the real shape of the glacier termini in the future.
5. Conclusions
Aiming to analyze the ice loss status of lake-terminating glaciers, the present study took the Jiongpu glacier as a typical case, which owns one of the largest proglacial lakes, Jiongpu Co. Based on a USV lake bathymetry system, UAV photogrammetric technique, time-series DEMs, and satellite imageries, a thorough investigation of the Jiongpu glacier was carried out, and datasets including lake expansion and ice loss in recent years were established. The results indicate that Jiongpu Co expanded from 1.19 ± 0.09 km2 to 5.34 ± 0.07 km2, becoming the biggest proglacial lake in southeast TP from 1976 to 2021. Besides this, the volume survey indicated that the volume of Jiongpu Co had reached 0.66 ± 0.03 Gt, along with a recent glacial lake expansion trend. The total ice loss of the Jiongpu glacier was 1.52 ± 0.37 Gt from 2000 to 2020, and more than 1/3 ice loss, 0.50 ± 0.03 Gt, was related to lake expansion (0.32 ± 0.01 Gt underwater, 0.19 ± 0.02 Gt above water). Retreat and surface thinning occupied 40% and 60% of ice loss, respectively. From the over deep of Jiongpu Co, an obvious underestimation of the ice loss amount, 0.06 Gt, nearly 19% compared with a previous study, happened in the covered area after glacier retreat; therefore, the existence of the proglacial lake Jiongpu Co led to at least 21% ice loss in the Jiongpu glacier in 2000 to 2020. This case study not only improves the understanding of ice loss in the HMA but also highlights the potential underestimation of lake-terminating glaciers.
Conceptualization, investigation, methodology, project, administration, supervision, formal analysis, visualization, and writing (original draft), D.L.; Funding acquisition, conceptualization and writing (review and editing), D.S., T.H., B.C., and B.P.; Investigation and writing (review and editing), M.W., Y.L., R.W., and A.Q.B. All authors have read and agreed to the published version of the manuscript.
The data presented in this study are available on request from the corresponding author.
The authors express their gratitude to Jingdong Zhao for his assistance and insightful suggestions in conducting fieldwork. The authors would also like to thank the USGS for providing Landsat data and Sentinel 2 (
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. The location of the Jiongpu glacier in the Yigong Zangpo river basin (A) and its basic terrain characteristics (B). The demonstration of the retreat of the Jiongpu glacier from 2000 to 2020 based on satellite images ((C): Sentinel 2, date: 10 October 2020, tile: T46RFU; (D): Landsat 5 TM, date: 22 July 2000, Path/Row:136/039).
Enlarge this image.Figure 2. Ice loss pattern of lake-terminating glacier before (A) and after (B) retreat in profile (① ice loss above water; ② ice loss underwater; ③ ice loss of glacier surface; * ice loss overestimated owing to cavity caused by snout floating). Spatial characteristics of Glacier region and Lake region of the Jiongpu glacier in ice loss estimation process (C), including further separated three parts, UAV investigation area and shrink area (D) (satellite image: Sentinel 2, date: 20 September 2021, tile: T46RFU). Terminus with potential floating ice of the Jiongpu glacier in 2000 (E) (satellite image: Landsat 5 TM, date: 22 July 2000, Path/Row: 136/039).
Enlarge this image.Figure 4. Area change (A) and increment (B) statistics of glacial lake Jiongpu Co.
Enlarge this image.Figure 5. Lake bathymetry of Jiongpu Co, Tibetan Plateau in 2021 (A). Horizontal profile (from downstream to upstream) and vertical profile (from right lakeshore to left) are shown in (B,C) separately.
Enlarge this image.Figure 6. Ice loss volume and ratio in different parts of the Jiongpu glacier from 2000 to 2020.
![View Image - Figure 7. Prediction volumes of Jiongpu Co by previous studies using the Area–Volume (A-V) relationship (A) [13,22,30,31,32,33,34]. Data collection of volume survey records from Ref. [22] (B). Details of data and equations used are shown in the Supplementary Files, Table S4.](https://media.proquest.com/media/hms/THUM/14/MAbfZ?_a=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%3D%3D&_s=Xjr9ujfCFc6KH0DU979O%2BMjV6K0%3D "View Image - Figure 7. Prediction volumes of Jiongpu Co by previous studies using the Area–Volume (A-V) relationship (A) [13,22,30,31,32,33,34]. Data collection of volume survey records from Ref. [22] (B). Details of data and equations used are shown in the Supplementary Files, Table S4.")
Enlarge this image.Figure 7. Prediction volumes of Jiongpu Co by previous studies using the Area–Volume (A-V) relationship (A) [13,22,30,31,32,33,34]. Data collection of volume survey records from Ref. [22] (B). Details of data and equations used are shown in the Supplementary Files, Table S4.
Supplementary Materials
The following supporting information can be downloaded at:
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; Shangguan, Donghui 2
; Han, Tianding 2 ; Asim Qayyum Butt 3
; Pan, Baotian 4 ; Cao, Bo 4 ; Wang, Meixia 1 ; Wang, Rongjun 1
; Li, Yaojun 1 1 State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;
2 State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;
3 State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;
4 College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;