Modelling ice thickness distribution and volume of Patsio Glacier in Western Himalayas

Kumari, Sangita; Pandit, Ankur; Ramsankaran, Raaj; Soheb, Mohd; Angchuk, Thupstan; Ramanathan, Arvind

doi:10.1007/s12040-021-01643-w

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…The GlabTop2 model was validated on the basis of observations (in-situ GPR-based ice thickness data) available for Chhota Shigri Glacier for year 2009 (Azam et al, 2012), Hamtah Glacier for year 2012 (Swain et al, 2018), and Patsio Glacier for year 2017 (Kumari et al, 2021) After multiple runs of the model using different shape factors (Table S4), the shape factor value of 0.5 was used for CB Basin glacier thickness estimation. This shape factor model bias ranges from -35 to 39 m which is admissible for basin-scale thickness estimation (Table 4).…”

Section: Model Results and Bias Estimationmentioning

confidence: 99%

“…3). Of these, Chhota Shigri and Patsio are almost clean-ice glaciers having 3.4 % and 12 % debris cover of total glacier area (Angchuk et al, 2021;Azam et al, 2016). Ground surveys were carried out for accuracy assessment of the image classification.…”

Section: Accuracy Assessment Of Mlc Methods For Debris Cover Estimationmentioning

confidence: 99%

“…Considering the aforementioned challenges and drawbacks of available inventories for studying a given 70 glacierised basin, the present work aims at developing a multitemporal glacier inventory which overcomes these limitations, and further helps investigate the state of glaciers on a multitemporal scale in Chandra-Bhaga Basin (CB Basin), a large glacierised region located in the western Himalaya. Glaciers in CB Basin are some of the most studied ones in the Himalaya (Angchuk et al, 2021;Garg et al, 2017;Kumar et al, 2016;Mandal et al, 2020;Pandey et al, 2017) given the hydrological significance of CB Basin. Chandra and Bhaga rivers, originating from 75 CB Basin are tributaries of Chenab River which on average, has snow and glacier melt contribution of 49 % in its https://doi.org/10.5194/essd-2022-311 Preprint.…”

Section: Introductionmentioning

confidence: 99%

“…Supraglacial debris cover is known to affect the rate of ablation (Banerjee and Shankar, 2013;Kääb et al, 2012b;Maurer et al, 2016). Angchuk et al, (2021), Mandal et al, (2020), andWagnon et al, (2007), using the pointscale ablation data, reported that ablation in debris-covered area was considerably lower as compared to clean ice for Patsio and Chhota Shigri glaciers in CB Basin. Thus, we further aimed at building a dataset of the debris cover 100 area on each glacier for the CB Basin which will aid other researchers in understanding the impact of debris on glacier dynamics in the region.…”

Section: Introductionmentioning

confidence: 99%

“…Ground Penetrating Radar (GPR) survey conducted on 105 the Chhota Shigri Glacier in 2009 estimated the ice thickness ranging from 124 to 270 m (Azam et al, 2012). For Hamtah and Patsio glaciers, GPR-measured ice thickness ranged from 155 to 247 m (Swain et al, 2018) and 37 to 105 m (Kumari et al, 2021), respectively.…”

Section: Introductionmentioning

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: Model Results and Bias Estimationmentioning

confidence: 99%

Section: Accuracy Assessment Of Mlc Methods For Debris Cover Estimationmentioning

confidence: 99%