Glacier mapping and change analysis in Chandra basin, Western Himalaya, India during 1971–2016

Sahu, Rakesh; Gupta, R.

doi:10.1080/01431161.2020.1752412

Cited by 31 publications

(15 citation statements)

References 65 publications

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“…South facing glaciers, even in complex local topography are more likely to receive more solar heat available for glacier melting thereby accelerating retreat (Azam et al, 2014;Fujita and Ageta, 2000;Oliphant et al, 2003). Moreover, present glacier inventory (in agreement with Maurer et al, 2016;Sahu and Gupta, 2020) highlights that the Himalayan glaciers with a lake at their snouts are losing mass 635 and shrinking more rapidly than those without (King et al, 2018(King et al, , 2019, irrespective of debris cover. Calving is an important component of mass loss for a glacier terminating into peri-glacial lake (Maurer et al, 2016;Sakai et al, 2009).…”

Section: Comparison With Previous Studies In the Regionmentioning

confidence: 58%

“…Previous studies in the region have also observed a decline in glacier area. Garg et al, (2017) and Sahu and Gupta, (2020) 610 can be attributed to the exclusion of flanks on either side of the glacier which contribute to the glacier flux and have been included in several studies (Chand et al, 2017;Yellala et al, 2019;Nela et al, 2020). The distinctions in glacier boundary defined by different studies contributes further to the challenge of statistical intercomparison and necessitates field surveys and visual inspection in order to ensure accuracy.…”

Section: Comparison With Previous Studies In the Regionmentioning

confidence: 99%

“…Manual digitisation carried out in the present study reduces uncertainty as compared to other studies that have opted for a semi-automated approach (viz. Sahu and Gupta, 2020).…”

Section: Comparison With Previous Studies In the Regionmentioning

confidence: 99%

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A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India

Vatsal

Bhardwaj

Azam

et al. 2022

Preprint

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Abstract. Delineation of Glaciers is a challenging task in the Himalaya due to its complex topography, cloud cover, seasonal snow cover, hillshade, debris cover. Glacio-hydrological studies including mass balance, run-off, and dynamic modelling rely on the availability of consistent and reliable glacier inventory datasets. This article on data set presents a homogenous, multidecadal inventory of glaciers in the Chandra-Bhaga Basin (CB Basin), western Himalaya, for 1993, 2000, 2010, and 2019. Landsat Thematic Mapper (TM), Enhanced Thematic mapper (ETM+), and Operational Land Imager (OLI) imageries, with minimum snow and cloud cover have been used for enhanced accuracy and consistency. Uncertainty assessment for the generated glacier inventory was performed, following various approaches such as buffer method, standard error estimation, and manual digitisation error and the maximum uncertainty has been quantified. We have identified and manually mapped a total of 251 glaciers with an area > 0.5 km2, and in order to minimise the uncertainty, field surveys were carried out on 6 glaciers in the basin. Out of these 251 glaciers, 217 are clean ice and 35 were debris-covered glaciers. The estimated total glacier area was 996 ± 62 km2 in 1993 that decreased to 973 ± 70 km2 in 2019. Apart from quantifying temporal changes in glacier area, this inventory further allows the estimation of supraglacial debris cover and glacier volume. The supraglacial debris cover area has increased by 14.1 ± 2.54 km2 (15.2 %) during 1993–2019. Accuracy of the debris cover dataset estimated using ground surveys is 82 % with a kappa coefficient of 0.87. Moreover, a glacier ice volume dataset was also generated by incorporating the inventory into Glacier Bed Topography Version 2 (GlabTop2) model and shows a total of 112.5 ± 41 km3 of ice volume stored in the CB Basin glaciers. For accuracy assessment of the DEMs generated using ASTER Stereopairs images, DGPS surveys were carried out on (28 GCPs) and off (6 GCPs) the glaciers. Glacier volume uncertainty with respect to the generated DEMs and model bias is 5.3 km3 and 35.5 km3 respectively. Overestimation of glacier volume due to over deepening is estimated to be 1.2 km3. The impact of climate change on the Himalayan glaciers is a matter of serious concern and such holistic multitemporal inventory datasets can help quantify that impact with improved certainty.

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Section: Comparison With Previous Studies In the Regionmentioning

confidence: 58%

Section: Comparison With Previous Studies In the Regionmentioning

confidence: 99%

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A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India

Vatsal

Bhardwaj

Azam

et al. 2022

Preprint

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show abstract

“…These tests are the Mann-Kendall [45][46], and the Spearman's rho (SR). More detailed information about the test can be found in [3] and [47].…”

Section: Trend Testsmentioning

confidence: 99%

Influence of Climate Change on the Flood Disasters in Bursa, Turkey

Balov¹

2021

International Journal of Engineering Technologies IJET

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The recent increase in the frequency and intensity of the floods resulted from huge precipitation events in Bursa Province, Turkey, is the main motivation of this study. For this purpose, the daily precipitation values from the historical data recorded between 1971 and 2000 together with the projected data of the NorESM1-M general circulation model under RCP4.5 and RCP8.5 scenarios between 2031 and 2100 were used to calculate eight extreme precipitation events. The Spearman's rho and the Mann-Kendall trend tests were used to detect the trend in the indices. The results of the trend tests showed that any change is not expected in the total amount of the yearly precipitation amount, whereas the occurrence of the intense and short duration precipitation events is expected to be more probable by the end of the current century, compared to the last 30 years of the previous century. The positive significant trends were detected in some indices. However, the magnitude of the change in the climatic parameters and the increase in the number of extreme precipitation events cannot solely explain the increase in the number of destroying flood events in the province. Other parameters such as the dramatic increase in urbanization as a result of rapid population growth during the past years (since 1960) may have a more prominent effect on the increase of the flood risk in the province which is in need of comprehensive modeling and investigation.

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“…The first and second blue bands of Landsat-8 OLI image data have a high degree of recognition for the glaciers in the shadow area [62], which are the dominant band, and this helps distinguish glacier information in the shadow area. Snow-ice has high spectral reflectance in the visible spectrum (VIS) but low reflectance in the short-wave infrared (SWIR) band [63,64]. Based on the significant difference between the glacial spectral reflectance in the VIS and SWIR bands, snow and ice are identified by thresholding NDSI features.…”

Section: Analysis Featuresmentioning

confidence: 99%

Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir

Zhang

Huang

2020

Water

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Debris-covered glaciers are common features on the eastern Pamir and serve as important indicators of climate change promptly. However, mapping of debris-covered glaciers in alpine regions is still challenging due to many factors including the spectral similarity between debris and the adjacent bedrock, shadows cast from mountains and clouds, and seasonal snow cover. Considering that few studies have added movement velocity features when extracting glacier boundaries, we innovatively developed an automatic algorithm consisting of rule-based image segmentation and Random Forest to extract information about debris-covered glaciers with Landsat-8 OLI/TIRS data for spectral, texture and temperature features, multi-digital elevation models (DEMs) for elevation and topographic features, and the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) for movement velocity features, and accuracy evaluation was performed to determine the optimal feature combination extraction of debris-covered glaciers. The study found that the overall accuracy of extracting debris-covered glaciers using combined movement velocity features is 97.60%, and the Kappa coefficient is 0.9624, which is better than the extraction results using other schemes. The high classification accuracy obtained using our method overcomes most of the above-mentioned challenges and can detect debris-covered glaciers, illustrating that this method can be executed efficiently, which will further help water resources management.

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Glacier mapping and change analysis in Chandra basin, Western Himalaya, India during 1971–2016

Cited by 31 publications

References 65 publications

A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India

A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India

Influence of Climate Change on the Flood Disasters in Bursa, Turkey

Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir

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