This study is perhaps the first attempt to use satellite data (1990–2018) to analyze spatiotemporal changes in glacial lakes over the Kashmir Himalayas supplemented by field studies. Landsat images were used to delineate the spatial extent of glacial lakes at four time points, i.e., 1990, 2000, 2010 and 2018. The total count of lakes as well as their spatial extent showed a discernible increase. The number increased from 253 in 1990 to 322 in 2018, with a growth rate of 21.4%. The area has increased from 18.84 Km2 in 1990 to 22.11 Km2 in 2018 with a growth rate of 14.7 percent. The newly formed glacial lakes, including supra glacial lakes, were greater in number than the lakes that disappeared over the study period. All glacial lakes are situated at elevations of 2700 m asl and 4500 m asl. More than 78% of lake expansion in the study region is largely due to the growth of existing glacial lakes. Through area change analysis, our findings reveal that certain lakes show rapid expansion needing immediate monitoring and observation. The analysis of the meteorological variables reveals that minimum and maximum temperatures in the Jhelum basin have shown an increasing trend. Tmax showed an increase of 1.1°C, whereas Tmin increased to 0.7°C from 1990 to 2018. On the other hand, precipitation has shown a decreasing trend, which can be attributed to one of the major causes of glacier recession and the expansion of glacial lakes in the Upper Jhelum basin. Consequently, this study could play a significant role in devising a comprehensive risk assessment plan for potential GLOFs and developing a mechanism for continuous monitoring and management of lakes in the study region.
Research Article
Spatio-temporal dynamics of glacial lakes (1990-2018) in the Kashmir Himalayas, India using remote sensing and GIS
https://doi.org/10.21203/rs.3.rs-778167/v2
This work is licensed under a CC BY 4.0 License
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Climate change
Glacial lake expansion
Remote sensing
Landsat imageries
GLOFs
Kashmir Himalaya
India
Glacial lakes are bodies of water strongly influenced by the presence of glaciers [23] and/or retreating processes of a glacier [17]. Globally, climate variability has a significant influence on the downwasting of glaciers [11, 54] as a result of forming new glacial lakes or increasing the spatial extent of existing lakes [71, 74, 10, 47]. With shrinking Himalayan glaciers, the high-altitude lakes in the region are continuously evolving and growing in number and size [77, 16, 45, 6, 2]. Himalayan glacier recession is largely attributed to the impact of changing global climate [12, 7, 57, 36, 34, 65; 42,13] and topographic regimes [57]. The continuous shrinkage of glaciers, as reported in the Central Himalayas, could trigger glacial lake expansion in the future [34]. The accelerated rate of glacial lake expansion increases the risk of outbursts of glacial lakes [76, 63, 68]. Furthermore, the development of dangerous glacial lakes and the risk of outburst flooding in mountainous regions are unarguably critical concerns for South Asian countries (e.g., Nepal, China, Pakistan, Bhutan, and India) [52]. GLOF events pose a serious threat to nearby communities and built infrastructure [54, 50, 31, 75]. This was evident in the case of the 2014 Gaya GLOF event that caused destruction in terms of agricultural losses, damage to immediate infrastructure and channel defences, the major reason being the lake’s potential to outburst and subsequent sudden downstream draining [38].
There have been several instances of GLOF events in the past striking the Himalayan region [31, 27, 69, 73, 4, 18]. The well documentation of such multiple extreme weather events acts as historic evidence to identify Himalayas as GLOF high-risk regions, e.g., Nare (Nepal) in 1977 [28]; Nagma Pokhari (Nepal) in 1980 [8, 53]; Zhangzangbo (Tibet) in 1981 [35, 72]; Dig Tsho in 1985 [64]; and Lugge Tsho (Bhutan) in 1994 [44, 69]. The recent event was an outburst flood that struck a village (Gaya) in Ladakh, India in 2014 [38]. Remote sensing has been largely a potent method for constant monitoring and timely detection of GLOFs over inaccessible mountain bound regions, as it guides us to investigate a large area [51, 29].
To carry out an updated inventory and understanding the spatiotemporal dynamism of glacial lakes is the first important step for monitoring and assessing GLOFs in a region [24, 47, 67].T hese multitemporal inventories serve as a baseline for studying the evolution, disappearance, and identification of potentially dangerous glacial lakes [41] as well as modeling GLOFs using various hydrodynamic models. In the Himalayan region, numerous inventories have been carried out by different authors according to their purpose of study. For example, [13] prepared an inventory of 958 glacial lakes (size > 500 m2) for Himachal Pradesh (areal coverage 9.6 ± 0.3 Km2) using the LISS IV data. [47] carried out a multitemporal inventory of glacial lakes in the central Himalayas using Landsat series of data. [62] mapped 2168 glacial lakes covering a total area of 127.61 Km2 in the Koshi basin of central Himalayas. [34] prepared and developed a detailed inventory of 1541 glacial lakes in the Nepal Himalayas with areal coverage of approximately 80.95 ± 15.25 Km2. Various studies that have focused on the inventories of glacial lakes in the Himalayan region are summarized in Table 1. However, to date, no exhaustive study has focused on the glacial lakes of the Jhelum Basin in particular, except for a few noteworthy studies [78, 79, 31] that have mapped some of the glacial lakes in the upper Jhelum Basin of the Kashmir Himalayas. Thus, there has been a gap in knowledge over the most recent decades. With the geographic importance of the Jhelum Basin, an updated glacial lake inventory and an analysis of spatiotemporal dynamism are needed.
To address this need, in the current study, spatiotemporal changes in glacial lakes were observed through time series Landsat imageries of the Kashmir Himalaya from 1990 to 2018. Consequently, the results were linked to the changing climatic regimes and glacier recession to understand the influence of climate change and glacier fluctuations on the expansion of glacial lakes. This is a step towards better and continuous monitoring of lake expansion in the study area, which is critical for GLOF hazard mitigation.
| S. No | Study | Minimum size | Satellite/Sensor/ resolution | No of lakes | Area of lakes Km2 |
|---|---|---|---|---|---|
| 1. | Bhambri et al., (2015) | 0.0005 | Resourcesat-2 LISS IV/5 | 1266 | 7.59 |
| 2. | Shrestha et al., (2017) | 0.003 | Landsat TM/ETM+/30 | 2168 | 127.61 |
| 3. | Khadka et al., (2018) | 0.003 | Landsat TM/OLI | 1541 | 80.95 |
| 4. | Bhambri et al., (2018) | 0.01 | Resourcesat-2 LISS IV/5 | 147 | 5.12 |
| 5. | Begam et al., (2019) | 0.15 | Landsat TM/ETM+/OLI/30 | -- | 37.9 |
| 6. | Wang et al., 2020 | 0.005 | Landsat TM/ETM+/OLI/30 | 30121 | 2080.12 |
| 7. | Chen et al., 2021 | 0.008 | Landsat TM/ETM+/OLI/30 | 15348 | 1395.733 |
| 8. | Present Study | 0.001 | Landsat TM/ETM+/OLI/30 | 322 | 22.11 |
Kashmir Valley is oval-shaped and has a dramatic landscape with several glaciers and glacial lakes spread over lofty mountains. Flanked by the Pir Panjal Mountains in the southwest and the Greater Himalayan range in the northeast, the study region (between 32°20´-34°50´N & 73°55´- 75°35´E) is in the Union Territory of Jammu and Kashmir, India[1],[2][39, Figure 1). Kashmir Valley has an area of approximately 15,948 square kilometers[3]. [21, 3] report that the Pir Panjal range obstructs southwestern monsoons from entering the valley, shaping the climate of the Kashmir region to a more arid-windy type in comparison to the tropical type in other parts of India. Kashmir Valley experiences four seasons: spring, summer, autumn and winter) [5] with annual average precipitation and temperature, i.e. 710 mm, 13.5 °C, while receiving enormous precipitation during winter months (December-February) due to western disturbances [21, 15]. Lakes formed during the time of deglaciation preceded by cold conditions in the Kashmir Himalayas [48]. There are large reserves of glaciers and glacial lakes in North Kashmir that also contribute to the Jhelum River. Jhelum is one of the main tributaries of the Indus basin and acts as a lifeline to the Kashmir valley [57]. The altitude of the study area ranges from 1065 m asl to 5441 m asl, in which the majority of glacial lakes (e.g., proglacial type) are situated at 3800 m asl to 4300 m asl.
3.1 Datasets
Table 2 Satellite data used
|
Date of Pass |
Satellite & Sensor |
Bands & Wavelength (μm) |
Path/Row |
Spatial Resolution (m) |
Repeat Cycle (Days) |
Cloud Cover |
|
14 Oct. 1990 |
Landsat TM |
6 |
149/36 |
30 |
16 |
No |
|
09 Oct. 2000 |
Landsat TM |
6 |
149/36 |
30 |
16 |
|
|
18 Oct. 2010 |
Landsat ETM+ |
8 |
149/36 |
30 |
16 |
|
|
04 Oct. 2018 |
Landsat L8 (OLI) |
9 |
149/36 |
30 |
16 |
Thematic Mapper TM. Enhanced Thematic Mapper ETM. Operational Land imager OLI.
Landsat data have been extensively utilized for the purpose of delineation and mapping glacial lakes due to their free availability, 30 m resolution and wide area coverage [37, 14,59, 55]. Landsat images of 30 m resolution and the Advanced Space-borne Thermal Emission & Reflection Radiometer – Digital Elevation Model (ASTER DEM, 30 m) downloaded from the web portal[5] were used in the present study (Table 2). The Landsat images hosted by the Global Land Survey (GLS) data system of the United States Geological Survey (USGS) were downloaded from their web portal[6] and used for the current study. Glacial lakes show minor changes in the post-monsoon season with limited cloud and snow cover [60]. Landsat scenes used in the study were from the post-monsoon season with less cloud coverage (<10%) or were cloud-free. The Landsat images from 1990 to 2018 were used to prepare glacial lake inventories for area change analysis. Validation of glacial lake boundaries for 1990 was performed using the Survey of India (SOI) toposheet 1979, Scale 1:50,000. The SOI toposheets alone are less reliable for change detection analysis [41]; hence, the Landsat TM (1990) scene has been used as a base image for glacial lake area change.
To validate the glacial lake outlines, Google Earth imagery of high resolution was used because of the comparatively coarser resolution of the Landsat data. The time series of meteorological data (e.g., temperature and precipitation) from 1980 to 2018 of the Pehalgam observatory was analysed to understand climate as a possible influencing factor for glacial lake changes in the study area.
3.2 Glacial lake mapping
A number of methods (processing satellite imagery) have been used for mapping glacial lakes over time. The two commonly used methods are automated and semiautomated methods, e.g., the normalized difference water index (NDWI) [1, 37, 25, 26, 70]. In the present study, we used the normalized difference water index NDWI – first proposed in 1996 by McFeeters – using a semiautomated method similar to [29] for delineation of glacial lake outlines (Figure 2; Equation 1).
*BNIR and Bblue are reflectance in the near infrared and blue bands, respectively
Pixel identification pertaining to the lakes was performed based on NDWI values in the range of -0.60 to -0.85 [24]. A few mountain-shadowed areas were mistaken as lakes due to the same spectral reflectance and topographic effects [62, 34]. Misclassification could occur due to near similarities between glacial lakes from frozen lakes and snow cover because of peculiar surface conditions. The pixels identified as mountain shadows were removed from ASTER DEM-derived values for slope, aspect and hill shade in NDWI images to overcome misclassification of lakes because of topographic effects (Figure 3).
Further correction was performed through a visual interpretation technique using ArcGIS 10.2 [34, 58, 46, 76, 60, 67]. Through this process, the first glacial lake layer in 2018 was prepared. The glacial lake outlines were overlaid with Google Earth imagery for validation and later crosschecked with the toposheet glacial lake inventory generated from SOI toposheet 1979 at a scale of 1:50,000. Corrections, if any, were taken on priority. Subsequently, glacial lake inventories for 2010, 2000 and 1990 were prepared to obtain the final database to observe glacial lake expansion in the study area.
3.3 Uncertainty analysis
In the present study, glacial lakes were identified, delineated, and mapped to observe changes in spatial extent using multidate/multisensor remote sensing data. Uncertainties in glacial lake mapping occur mainly due to image coregistration, area delineation and editing using manual interpretation. As a result, thorough consideration of errors is required to determine the accuracy and relevance of the findings. High-resolution satellite imagery would be the most precise way to assess the errors related to glacial lake outlines [41]. However, high-resolution satellite data were not available for the present research work. Therefore, we used Landsat satellite data in conjunction with high-resolution Google Earth imagery to maintain the accuracy of glacial lake boundaries. The primary errors of co-registration and lake outlines that could have resulted in various levels of accuracy were considered in this study. Most of the Landsat scenes have similar resolutions. The glacial lakes are clear on almost all the scenes utilized in the study with less snow and cloud cover, and the manually delineated lake boundaries were checked two times simultaneously by a single operator.
Initially, Landsat ETM+ images were merged with Pan images with high resolution to create a high-resolution pansharpened image by employing the methodology suggested by [41, 42]. All other images were coregistered with the pansharpened ETM+ image within 7.5 m for TM and OLI images, using it as the base map. Consequently, after the image coregistration, geometrical rectification of all images was carried out with the same projected coordinate system of WGS 1984 UTM Zone 43. The remote sensing uncertainty formula [75, 42] was used to estimate the terminus change uncertainty (U).
where a and b are the resolutions of the image and is the coregistration error of the images to the base image in equation (2). We estimated a terminus accuracy of 47.3 m for Landsat TM and ETM+ and 49.6 m for OLI images.
The uncertainties related to lake area have also been estimated through equation (3), as suggested by Yao et al., 2006.
Uarea=2UV (3)
where U and V are the glacier area uncertainty and pixel resolution, respectively.
In this way, the area uncertainties of the glacial lakes were found to be 0.003 km2 (0.3%) for TM and ETM+ and 0.0025 Km2 (0.25%) for OLI images. Thus, the overall uncertainty was estimated to be 0.005 km2 (0.55%), which are well or below the previously reported acceptable ranges (42,41, 82].
4.1 Glacial lakes 2018
Using Landsat (OLI) imagery from 2018 with 30 spatial resolution, a total of 322 glacial lakes were identified in the UJB, with a total calculated area of approximately 22.11 km2. The area of the glacial lakes varied from 0.001 km2 to 1.65 Km2 with an average size of 0.06Km2. Glacial lakes in the region were classified as (1) proglacial lakes connected to glaciers, (2) proglacial lakes not connected to glaciers and (3) supraglacial lakes based on their hydrologic connection to the glacial watershed. A total of 208 glacial lakes were classified as not connected to glaciers, with an overall surface area of approximately 11. 84 km2), which constitute approximately 64.59% and 53.55% of the total number and area, respectively. Nonglaciers fed (not connected to glaciers) accounted for 93 (28.88%) of the total lake area in the region and 9.95 km2 (45%) of the total lake area. Supra-glacial lakes have been observed to be fewer in number and in area, i.e., 21 and 0.32 km2, respectively (Figure 4). Glacial lakes in the region are not evenly distributed, and the majority of glacial lakes are located in the Sindh and Lidder catchments of the basin, accounting for almost 68% of the total lakes.
The majority of the glacial lakes are small in size, i.e., less than 0.1 km2 contributes 269 of the total number and 5.67 km2 of the total area, which are 83.54% and 25.64%, respectively. Lakes with sizes greater than 0.1 km2 constitute only 54 in number but contribute 16.54 km2 of area, which is approximately 75% of the total surface area of the glacial lakes in the study region. Lakes with an area >0.1 km2 are considered dangerous because they possess an enormous volume of water to cause a flash flood in the downstream region [80, 81]. The details of the glacial lakes greater than 0.1 km2 in the study region are mentioned in Table 6. All glacial lakes in the study area are located at an elevation range between 2700-4500 m asl. Glacial lakes below 2900 m asl and above 4500 m asl share a minimum number and area of lakes, whereas the majority of the glacial lake area, i.e., 68%, is concentrated between the elevation zones of 3650-4150 m.
Table 6 Characteristics of glacial lakes with sizes >0.1 km2.
|
S. No |
Local name |
latitude |
Longitude |
GLIMS_Lake_ID |
Watershed |
Area Km2 |
Elevation (m) |
Type |
Length |
Width |
|
1 |
Dudh Nag |
75.320 |
34.192 |
G034192E75320N |
Lidder |
0.102 |
3704 |
NC |
512.130 |
253.290 |
|
2 |
Raman Sar |
75.143 |
34.361 |
G034361E75143N |
Sind |
0.102 |
3925 |
C |
497.790 |
271.630 |
|
3 |
|
75.177 |
34.646 |
G034646E75177N |
Sind |
0.105 |
4208 |
NC |
506.000 |
217.960 |
|
4 |
Chiti chhan Sar |
74.913 |
34.470 |
G034470E74913N |
Madhumati |
0.112 |
3807 |
NC |
561.190 |
271.270 |
|
5 |
Yamhar Sar |
75.151 |
34.208 |
G034208E75151N |
Sind |
0.113 |
3763 |
C |
498.040 |
301.820 |
|
6 |
Chohar Nag |
75.469 |
33.754 |
G033754E75469N |
Arapal |
0.115 |
3894 |
NC |
399.760 |
461.640 |
|
7 |
|
74.561 |
33.528 |
G033528E74561N |
Rambiahra |
0.123 |
4053 |
NC |
430.250 |
386.070 |
|
8 |
Chiamar Sar |
74.876 |
34.380 |
G034380E74876N |
Erin |
0.126 |
3711 |
C |
592.890 |
388.240 |
|
9 |
Chand Sar |
75.117 |
34.156 |
G034156E75117N |
Lidder |
0.126 |
3917 |
NC |
368.750 |
391.710 |
|
10 |
Sursyar |
74.523 |
33.735 |
G033735E74523N |
Dodhganga |
0.127 |
4024 |
C |
526.340 |
328.950 |
|
11 |
|
74.593 |
33.505 |
G033505E74593N |
Rambiahra |
0.131 |
4041 |
NC |
557.600 |
274.310 |
|
12 |
Kotori Sar |
74.605 |
33.522 |
G033522E74605N |
Rambiahra |
0.131 |
3803 |
NC |
603.760 |
249.650 |
|
13 |
|
74.906 |
34.489 |
G034489E74906N |
Madhumati |
0.133 |
4061 |
C |
554.030 |
378.790 |
|
14 |
Andaun Sar |
74.932 |
34.452 |
G034452E74932N |
Sind |
0.135 |
3854 |
NC |
640.450 |
304.600 |
|
15 |
Tson |
75.216 |
34.063 |
G034063E75216N |
Lidder |
0.137 |
3685 |
NC |
594.910 |
278.260 |
|
16 |
Kaul Sar |
74.944 |
34.385 |
G034385E74944N |
Sind |
0.139 |
3685 |
NC |
682.280 |
317.440 |
|
17 |
Barani Sar |
74.574 |
33.530 |
G033530E74574N |
Rambiahra |
0.151 |
3913 |
NC |
550.450 |
375.730 |
|
18 |
|
74.435 |
33.829 |
G033829E74435N |
Ferozpur |
0.152 |
4006 |
C |
750.450 |
289.510 |
|
19 |
Daman Sar |
74.416 |
33.866 |
G033866E74416N |
Ferozpur |
0.153 |
3912 |
NC |
467.960 |
424.220 |
|
20 |
Bramsar |
74.850 |
33.502 |
G033502E74850N |
Vaishav |
0.155 |
3574 |
C |
519.250 |
385.250 |
|
21 |
Watal Sar |
74.986 |
34.457 |
G034457E74986N |
Sind |
0.158 |
3715 |
C |
972.620 |
290.210 |
|
22 |
Logul Sar |
74.908 |
34.448 |
G034448E74908N |
Sind |
0.172 |
3941 |
NC |
569.140 |
363.570 |
|
23 |
Chandan Sar |
74.542 |
33.550 |
G033550E74542N |
Rambiahra |
0.173 |
3872 |
NC |
523.620 |
350.000 |
|
24 |
Handil Sar |
75.205 |
34.202 |
G034202E75205N |
Lidder |
0.183 |
3668 |
NC |
783.760 |
268.000 |
|
25 |
Chhumhai Sar |
75.160 |
34.093 |
G034093E75160N |
Lidder |
0.188 |
3886 |
NC |
770.840 |
402.200 |
|
26 |
|
75.372 |
34.184 |
G034184E75372N |
Sind |
0.188 |
4253 |
C |
557.360 |
297.840 |
|
27 |
Sona Sar |
75.275 |
34.234 |
G034234E75275N |
Lidder |
0.191 |
3799 |
NC |
909.800 |
312.450 |
|
28 |
Sona Sar |
75.475 |
34.067 |
G034067E75475N |
Lidder |
0.191 |
3697 |
C |
961.490 |
248.280 |
|
29 |
Sorus Nag |
75.378 |
33.953 |
G033953E75378N |
Lidder |
0.202 |
3617 |
NC |
469.750 |
490.000 |
|
30 |
Charl Nag |
75.389 |
33.929 |
G033929E75389N |
Lidder |
0.210 |
4012 |
C |
1001.160 |
300.910 |
|
31 |
Goli Sar |
74.565 |
33.540 |
G033540E74565N |
Rambiahra |
0.224 |
3923 |
NC |
571.680 |
528.340 |
|
32 |
Nandan Sar |
74.526 |
33.559 |
G033559E74526N |
Rambiahra |
0.271 |
3816 |
NC |
912.740 |
401.440 |
|
33 |
Pam Sar |
74.451 |
33.820 |
G033820E74451N |
Ferozpur |
0.285 |
3962 |
C |
1008.150 |
339.790 |
|
34 |
Sarbal Sar |
74.873 |
34.392 |
G034392E74873N |
Erin |
0.288 |
3522 |
NC |
1044.620 |
438.960 |
|
35 |
Nabler Sar |
74.819 |
34.548 |
G034548E74819N |
Madhumati |
0.316 |
3843 |
NC |
1214.650 |
346.060 |
|
36 |
Krishan Sar |
75.103 |
34.397 |
G034397E75103N |
Sind |
0.321 |
3783 |
NC |
922.840 |
479.100 |
|
37 |
Patalwan Sar |
74.827 |
34.537 |
G034537E74827N |
Madhumati |
0.324 |
3891 |
NC |
973.260 |
545.690 |
|
38 |
Laksukh Sar |
74.554 |
33.537 |
G033537E74554N |
Rambiahra |
0.324 |
3971 |
NC |
1007.250 |
323.060 |
|
39 |
Har Nag |
75.377 |
34.139 |
G034139E75377N |
Sind |
0.333 |
3675 |
NC |
1098.600 |
410.750 |
|
40 |
Salnai Sar |
74.892 |
34.444 |
G034444E74892N |
Madhumati |
0.356 |
3814 |
C |
992.650 |
440.620 |
|
41 |
Dhaklar Sar |
74.624 |
33.509 |
G033509E74624N |
Rambiahra |
0.362 |
3905 |
C |
932.130 |
575.800 |
|
42 |
Nund Kol |
74.935 |
34.418 |
G034418E74935N |
Sind |
0.389 |
3459 |
C |
1314.800 |
338.810 |
|
43 |
Gadsar |
75.058 |
34.422 |
G034422E75058N |
Sind |
0.403 |
3722 |
C |
870.830 |
626.450 |
|
44 |
Madmatti Sar |
74.921 |
34.493 |
G034493E74921N |
Madhumati |
0.409 |
3841 |
C |
1316.640 |
504.590 |
|
45 |
Mar Sar |
75.114 |
34.144 |
G034144E75114N |
Lidder |
0.451 |
3788 |
NC |
1180.800 |
489.120 |
|
46 |
Bodh Sar |
74.428 |
33.841 |
G033841E74428N |
Ferozpur |
0.472 |
3919 |
C |
1555.440 |
574.640 |
|
47 |
Vishan Sar |
75.119 |
34.388 |
G034388E75119N |
Sind |
0.480 |
3632 |
C |
1083.540 |
712.350 |
|
48 |
Shesh Nag |
75.497 |
34.093 |
G034093E75497N |
Lidder |
0.553 |
3546 |
NC |
1118.000 |
745.030 |
|
49 |
Bhag Sar |
74.583 |
33.519 |
G033519E74583N |
Rambiahra |
0.730 |
3893 |
NC |
1507.630 |
659.330 |
|
50 |
Tar Sar |
75.151 |
34.140 |
G034140E75151N |
Lidder |
0.872 |
3800 |
NC |
1733.570 |
601.200 |
|
51 |
Konsar Nag |
74.769 |
33.512 |
G033512E74769N |
Vaishav |
1.376 |
3463 |
C |
2912.750 |
645.230 |
|
52 |
Gangabal Lake |
74.924 |
34.432 |
G034432E74924N |
Sind |
1.654 |
3534 |
C |
2754.890 |
840.150 |
C=Lakes connected to glacier
NC=Lakes not connected to glacier
4.2 Glacial lake expansion (1990-2018).
The glacial lakes in the Kashmir Himalayas evolved and increased in number and size over time, particularly from 1990 to 2018, with 253 (18.84±0.02 Km2), 267 (19.31±0.02), 310 (21.03±0.02), and 322 (22.11±0.002 km2) glacial lakes identified and mapped in 1990, 2000, 2010, and 2018, respectively. The total lake area has increased by 3.27±0.013 Km2 (14.8%) in last three decades. The total area has increased by 0.10 Km2± 0.013 for glacial lakes with size ≤ 0.01, 1.22 Km2±0.013 with size >0.10 Km2 and ≤ 1.0 Km2 and 0.27±0.013 Km2 with size >1.0 Km2 from 1900-2018 (Table 3, Figure 5). Hence, the small glacial lakes have contributed less to the total area change, i.e., expanded more slowly than the large ones, similar to what [47] reported in the Central Himalayan region. The smaller lakes are so dynamic that they appear and disappear over time (Table 3, Figure 5), as confirmed by [62].
Table 3 Glacial lake count and area in 1990, 2000, 2010 and 2018
|
Area Range (Km2) |
1990 |
2000 |
2010 |
2018 |
1990-2018 Change |
|||||
|
|
Count |
Km2 |
Count |
Km2 |
Count |
Km2 |
Count |
Km2 |
Count |
Km2 |
|
≤0.01 |
103 |
0.49 |
112 |
0.57 |
110 |
0.60 |
121 |
0.59 |
18 |
0.10 |
|
0.01-0.05 |
77 |
1.70 |
82 |
1.92 |
124 |
2.94 |
115 |
2.68 |
38 |
0.98 |
|
0.05-.10 |
25 |
1.72 |
25 |
1.84 |
27 |
2.01 |
34 |
2.42 |
09 |
0.70 |
|
0.10-1.0 |
46 |
12.20 |
46 |
12.23 |
47 |
12.52 |
50 |
13.42 |
04 |
1.22 |
|
>1.0 |
02 |
2.73 |
02 |
2.75 |
02 |
2.96 |
02 |
3.00 |
0 |
0.27 |
|
Total |
253 |
18.84 |
267 |
19.31 |
310 |
21.03 |
322 |
22.11 |
69 |
3.27 |
More than 78% of existing glacial lakes show an obvious increase in the spatial extent over the 28-year study period. The processes of glacial lake area change were intricate, consisting of expansion in areas of larger glacial lakes as well as the appearance and disappearance of small glacial lakes. Newly developed glacial lakes were found three times more in number than glacial lakes that were extinct, i.e., 55>14 over time. The newly formed glacial lakes, therefore, have a small contribution to lake expansion in the Kashmir Himalaya. (Table 4), similar to the Central Himalayas reported by [47].
Table 4 Newly formed glacial lakes and extinct lakes from 1990 to 2018
|
Year
|
Total Number |
Total Change Area (km2) |
Newly Formed |
Disappeared |
Existing |
||||
|
Number |
Km2 |
Number |
Km2 |
Number |
Change Km2 |
Contribution of the area change (%) |
|||
|
1990 |
253 |
-- |
-- |
-- |
-- |
-- |
-- |
-- |
-- |
|
2000 |
267 |
+0.47 |
10 |
0.15 |
4 |
0.08 |
253 |
0.40 |
85.10 |
|
2010 |
310 |
+1.72 |
36 |
0.47 |
7 |
0.10 |
260 |
1.36 |
79.06 |
|
2018 |
322 |
+1.08 |
9 |
0.13 |
3 |
0.06 |
307 |
1.01 |
93.51 |
The study reveals that the proglacial lakes connected to glaciers showed greater expansion in comparison to supraglacial and proglacial lakes not connected to glaciers, the latter being dynamic in nature. The growth in terms of area as relates to proglacial lakes connected to glaciers increased by 2.70 km2 (82.56%) from 1990 to 2018 (Table 5). The proglacial lakes not connected to glaciers show lower expansion rates, i.e., 6.42% of an overall increase in lake area.
Table 5 Total increase in the area of major glacial lake types from 1990-2018
|
Type |
Increasing Area (Km2) |
Percentage (%) |
|
Proglacial lakes connected to glacier |
2.70 |
82.56 |
|
Supraglacial lakes |
0.36 |
11.01 |
|
Proglacial lakes not connected to glacier |
0.21 |
6.42 |
|
Total |
3.27 |
100.00 |
4.3 Altitudinal differences
Altitude is an important controlling factor for the change in surface area of glacial lakes (Tartari et al., 2008). All the identified glacial lakes in the study region were normally distributed between 2700 m asl and 4500 m asl. The majority of them, particularly proglacial lakes not connected to glaciers and supraglacial lakes, were concentrated in the elevation range of 3800 m asl - 4300 m asl. This is largely due to an enabling environment and peculiar geomorphological setting for glacier lake development in the study region. Hence, maximum glacial lake changes (≥ 91%) occurred at elevations between 3800 m asl and 4300 m asl from 1990 to 2018. The area of glacial lakes has also shown variation in terms of different altitudinal ranges. Glacial lakes below 2900 m asl and above 4500 m asl share a minimum number and area of lakes, whereas the majority of the glacial lake area, i.e., 68%, is concentrated between the elevation zones of 3650-4150 m asl.
4.4 Field investigations
Field measurements of high-altitude lakes are difficult to accurately ascertain in steeply edged mountainous remote areas [26]. Field study experiences, however, still confirm the location, shape and size of such lakes. To supplement our study, we conducted a five (05) day field visit to glacial lakes in the study area from 7-11 October 2019.
During our visit, we could only identify a sizable number of glacial lakes due to inaccessibility issues and hostile conditions in the study region. We identified lakes broadly classified as supra glacial, cirque, moraine and ice dammed glacial lakes (Figure 7a-f) and crosschecked them with the first glacial lake inventory prepared from Landsat images for 2018 to make corrections in lake outlines and obtain the final glacial lake inventory for 2018. Furthermore, various glacial lake parameters, such as latitude, longitude, and elevation, were taken using a differential global positioning system (DGPS). A laser distance meter (LDM) was used to calculate the length and width of the lakes. Various geomorphological parameters of lakes identified during the field investigation are presented in Figure 6a-f. The main purpose of the field investigation of selected lakes was to validate the inventoried glacial lakes on the ground. The coordinates taken during the field visit of selected lakes showed a minor error in terms of location. The elevation on the ground and elevation derived from the DEM also showed a minor error of approximately 4-10 m. For example, Gangabal Lake, which is located in the Sind catchment, has an elevation of 3534 m derived from the DEM, whereas DGPS on the ground has shown an elevation of 3537 m. Similarly, Sheshnag in the Lidder catchment has a DEM-generated elevation of 3564 m, whereas it has shown 3573 m on the ground.
4.5 Climate change in the region
The meltwater of glaciers makes a significant contribution to glacial lake growth in the Himalayan region [47], in which proglacial lakes show an obvious increase in expansion with increasing meltwater [16, 20, 19, 26]. The climatic variables (e.g., temperature and precipitation) of the Kashmir valley show substantial changes from 1980-2014 [61], with an increase in mean maximum and minimum temperatures and annual precipitation showing a downward trend from 1980 to 2018 [58]. The observed extreme warm temperatures in the past have resulted in the remarkable retreat of glaciers in the Western Himalayas [49, 22, 15, 32). Understanding that assessing climate change impact on glacial lakes is a tedious process and cannot singly be a reason for glacial lake expansion [26], the melting of glaciers in the Central Himalayas is exacerbated by the presence of increased light trapping particles (such as black carbon and dust) that may also contribute to the development of glacial lakes [33].
Our study used historical meteorological data from Pehalgam station from 1990 to 2018 to analyze the temperature and precipitation trends in the Kashmir valley. The study revealed that minimum and maximum temperatures in the Jhelum Basin have shown an increasing trend. Tmax showed an increase of 1.1°C, whereas Tmin increased to 0.7°C from 1990 to 2018 (Figure 7a, b). On the other hand, precipitation has shown a decreasing trend, which can be attributed to one of the major causes of glacier recession and the expansion of glacial lakes in the Upper Jhelum basin. Although there can be various underlying causes for such changes, climatic variability appears to be a steeling force for glacial lake changes in the study region.
The Himalayan cryosphere acts as a major source of water for downstream regions and has important interconnections with ecosystems and socioeconomic benefits [31, 2]. Glaciers and glacial lakes in the Jhelum Basin of the Kashmir Himalayas are vital sources to the headwaters of the Indus River basin and support hydropower generation, irrigation, domestic uses and tourism in downstream areas [21]. With the increasing temperatures in the Himalayan region, glaciers are melting, thus resulting in the negative mass balance of glaciers. Thus, this led to the formation and expansion of various types of glacial lakes. An extreme warming trend that has developed in the western Himalayas has resulted in significant glacier retreat [15, 22, 61]. Glacial lakes in the study area have shown a significant increasing trend over the study period. The total count of lakes as well as their spatial extent showed a discernible increase. The number increased from 253 in 1990 to 322 in 2018, with a growth rate of 21.4%. The area has increased from 18.84 Km2 in 1990 to 22.11 Km2 in 2018 with a growth rate of 14.7 percent. The newly formed glacial lakes, including supra glacial lakes, were greater in number than the lakes that disappeared over the study period. Similar to other parts of the Himalayan region, the analysis of the climatic variables (temperature and precipitation) reveals that glacial lake expansion in the study region is also the outcome of the increasing temperatures and decreasing precipitation trends. If the same trend continues in the future, glaciers will shrink more, which may result in the further expansion of glacial lakes. Therefore, the number of potentially dangerous glacial lakes in the region is increasing.
Field investigation is not possible for all glacial lakes because of the tough terrain and harsh weather conditions. In this backdrop, remote sensing-based studies in conjunction with GIS and GPS play an important role in monitoring such glacial lakes in a regular time interval. In the present study, we managed to carry out field surveys on a limited number of lakes with limited investigation due to a lack of instrumentation. In addition, due to the inaccessibility and paucity of hydrometeorological data networks, the hydrological and meteorological phenomena in the Kashmir Himalayan region have been poorly investigated [56]. Therefore, we suggest an in-depth study of rapidly expanding glacial lakes and the analysis of meteorological parameters to understand the influence of the changing climate on the rapid expansion of glacial lakes in the region.
The selected study sites in the Kashmir Himalayas have a complex and rugged topography in addition to the limited network of observational sites (e.g., meteorological and glaciological). This proves to be a major obstacle in conducting extensive field investigations and understanding the occurrences of dynamic environmental processes in the region.
This study systematically observes the spatiotemporal changes in glacial lakes across the Kashmir Himalaya from 1990–2018 using Landsat images supplemented by ground observations. The Landsat images with 30 m resolution used for four time points (1990, 2000, 2010, and 2018) make this entire process of glacial lake change detection more reliable by reducing any uncertainties.
This analysis further revealed that the overall glacial lakes pertaining to the present study showed a substantial increase in count and area through 1990–2018. Area has increased from 18.84 Km2 in 1990 to 22.11 Km2 in 2018 with a growth rate of 14.7%. The number increased from 253 in 1990 to 322 in 2018, with a growth rate of 21.4%. The already existing lakes have contributed more to area expansion than newly developed glacial lakes, with maximum changes occurring in the elevation range of 3800 m asl and 4300 m asl
Glacial lakes that are expanding at higher rates should be taken as case studies for potential GLOF events in the future. Consequently, continuous monitoring and observation specifically focused on area change, lake volumes and outburst scenarios/simulation studies. Furthermore, the development of integrated socio technology-driven early warning systems (EWSs) and the generation of public awareness may help us to reduce the risk of GLOF hazards in the region. The supplementary data related to this research article are freely available at https://doi.org/10.5281/zenodo.5016511.
Data availability
All data generated or analysed during this study are included in this published article.
Acknowledgements
The authors are thankful to the United States Geological Survey (USGS) for freely providing the satellite data used in this study. The first author acknowledges the support of the University Grants Commission (UGC) in the form of grants made available under the MANF-JRF scheme for pursuing Ph.D. The corresponding author acknowledges the support of MOES & NCPOR under HiCom Initititaive.
Declaration of competing interest
The authors have no conflicts of interest to declare that they are relevant to the content of this article.
Funding
No funding was received to carry out this study.
Ethical Approval
All the ethical standards of research publishing were taken care of during this study.
Conflicts of Interest
The authors declare no conflicts of interest.
Authors’ Contributions
Every author has contributed to the successful compilation of this study.
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No competing interests reported.