Glacier-Surface Velocity Derived Ice Volume and Retreat Assessment in the Dhauliganga Basin, Central Himalaya – A Remote Sensing and Modeling Based Approach

Sattar, Ashim; Goswami, Ajanta; Kulkarni, Anil V.; Das, Pritam

doi:10.3389/feart.2019.00105

Cited by 50 publications

(16 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Satter et al . 33 report a mass budget of −3.3 ± 0.5 Gt considering 15 glaciers under the region of study whereas, our mass balance estimates (−5.04 ± 0.36 Gt) considering the entire glaciated terrain of Dhauliganga. Moreover, Kääb et al .…”

Section: Resultsmentioning

confidence: 59%

Spatial distribution of decadal ice-thickness change and glacier stored water loss in the Upper Ganga basin, India during 2000–2014

Bandyopadhyay

Singh

Kulkarni

2019

Sci Rep

Self Cite

View full text Add to dashboard Cite

Himalayan glaciers have long been the focus of glaciologists across the world while trying to understand the contrasting patterns of elevation and mass changes. However, with limited number of ground observations, a comprehensive assessment of mass balance on a regional scale still remains elusive. Using the synoptic coverage of remote sensing data, we estimate a detailed spatial variation of glacier ice thickness change in the Central Himalaya of Uttarakhand using geodetic method, on a catchment scale. High resolution TerraSAR-X/TanDEM-X (12 m) and SRTM (30 m) digital elevation models (DEMs) have been utilized. The mean elevation change in the catchments is found to be −9.56 ± 0.2 m (mean annual elevation change rate is −0.68 ± 0.01 m a−1). To highlight the water potential of this region, the total ice mass loss has been estimated to be 16.0 ± 1.2 Gigatonne (Gt) from 2000–2014 from eight identified catchments namely Yamunotri, Bhagirathi, Mandakini, Alaknanda, Dhauliganga, Pindar, Goriganga and Kali/Sarda. The estimated mass balance has been validated using reported observations on five selective glaciers and the coefficient of determination is 0.93. This spatial variation of ice thickness estimated in the eight catchments is critical, as the melt-water from these glaciers contribute to the upper Ganga basin.

show abstract

Section: Resultsmentioning

confidence: 59%

Spatial distribution of decadal ice-thickness change and glacier stored water loss in the Upper Ganga basin, India during 2000–2014

Bandyopadhyay

Singh

Kulkarni

2019

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The lake boundaries were delineated manually using the rectified Corona images. Corona images had previously been used successfully for mapping the glaciated terrain in Himalaya [11,25,26]. Since the present study aimed to evaluate the GLOF hazard of Safed Lake at a present scenario, we considered the latest extent (2018) of the lake for volume estimation and flood routing.…”

Section: Remotely Sensed Data and The Growth Assessment Of Safed Lakementioning

confidence: 99%

Lake Evolution, Hydrodynamic Outburst Flood Modeling and Sensitivity Analysis in the Central Himalaya: A Case Study

Sattar

Goswami

Kulkarni

et al. 2020

Water

Self Cite

View full text Add to dashboard Cite

Climate change has led to the formation of numerous high-altitude lakes of glacial origin in the Himalaya. Safed Lake is one of the largest glacial lakes, located at an elevation 4882 m a.s.l. in the state of Uttarakhand, central Himalaya, India. A temporal analysis of the lake surface using satellite imagery shows that the lake has grown more than double its size from 0.10 km2 to 0.23 km2 over the past 50 years. In this study, we performed a hazard assessment of the lake using 1D and 2D hydrodynamic modeling. We identified the potential glacial lake outburst flood (GLOF) triggering factors and evaluated the impact of a moraine breach event of the lake on the nearest village located 16.2 km downstream of the lake. A series of dynamic simulations were performed for different scenario-models based on varied breach depths, breach widths and time of moraine failure. In a worst-case GLOF scenario where breach depth reached up to 60 m, hydrodynamic routing of the breach hydrograph along the given channel revealed inundation depth up to 5 m and flow velocities up to 3.2 m s−1 at Milam village. Considering the flat geometry of the frontal moraine, hazard assessment of the lake was performed by for different breach incision depths (30 and 15 m). In addition, the study incorporated a series of hydrodynamic routing to understand the sensitivity of GLOF to different model input parameters and terrain conditions. The sensitivity of the initial GLOF hydrograph to breach formation time (Tf) was evaluated by considering different hypothetical breach scenarios with a varied time of failure. Increases of 11.5% and 22% in the peak flooding were recorded when the moraine failure time was decreased by 15 and 30 min respectively. The two-dimensional sensitivity revealed flow velocity (m s−1) to be more sensitive to change in Manning’s N when compared to the inundation depth (m). Changes of 10.7% and 0.5% in the mean flow velocity (in m s−1) and flow depth (in m) were recorded when dN was 0.01. The flow velocity was more sensitive to the slope and the top-width of the channel when compared to the inundation depths. A regression of flow velocity versus slope gives a correlation coefficient of 0.76. GLOF flow hydraulics are sensitive to changes in terrain elevation, where flow depth and velocity vary in a similar manner.

show abstract

“…However, the Kunlun and Karakoram region glaciers show anomalous behaviour, where glaciers are either stable or advancing (Farinotti et al, 2020; Gardelle et al, 2012). Information on glacier velocity is imperative in order to understand glacier mass (Dehecq et al, 2019), ice volume (Sattar et al, 2019), surge events of the glacier (Quincey et al, 2011a), topography (Sam et al, 2018) and response to climate change (Garg et al, 2019; Zhao et al, 2020). Additionally, glacier flow bears significance in terms of concomitant supraglacial and proglacial lake formation and expansion (Thakuri et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Long‐term spatiotemporal variability in the surface velocity of Eastern Himalayan glaciers, India

Kaushik

Singh

Bhardwaj

et al. 2022

Earth Surf Processes Landf

View full text Add to dashboard Cite

Investigation of spatiotemporal variation in glacier velocity is imperative to comprehend glacier mass and volume loss as a function of their sensitivity to climate change. The long‐term glacier velocity record for the Eastern Himalayan region is of utmost importance owing to its data scarcity and climate sensitivity. Here, we present a long‐term dataset spanning more than two decades (1994–2020) of glacier surface velocity for the entire Sikkim Himalaya by applying image correlation methods on the multi‐temporal Landsat images. Our results demonstrate an average glacier surface velocity decline from 15.7 ± 5.69 (1994/96) to 12.88 ± 2.09 m yr−1 (2018/2020), that is a decline of ~15% during the period of investigation. Trend analysis shows a decreasing trend in median velocity (32.2%) at a rate of 0.25 m yr−1. Despite the general decline in average glacier velocity, the rate of slowdown of individual glaciers is extremely heterogeneous (3.6–20 m yr−1). Our study shows that up to 32% of the observed heterogeneity in velocity variation can be explained by the variation in glacier size. The present study highlights that large glaciers with thick ice cover move faster compared to small glaciers (even those situated on steep slopes). The findings are significant and have direct implications for assessing future water availability scenarios and modelling glacio‐hydrology in the region.

show abstract

Glacier-Surface Velocity Derived Ice Volume and Retreat Assessment in the Dhauliganga Basin, Central Himalaya – A Remote Sensing and Modeling Based Approach

Cited by 50 publications

References 43 publications

Spatial distribution of decadal ice-thickness change and glacier stored water loss in the Upper Ganga basin, India during 2000–2014

Spatial distribution of decadal ice-thickness change and glacier stored water loss in the Upper Ganga basin, India during 2000–2014

Lake Evolution, Hydrodynamic Outburst Flood Modeling and Sensitivity Analysis in the Central Himalaya: A Case Study

Long‐term spatiotemporal variability in the surface velocity of Eastern Himalayan glaciers, India

Contact Info

Product

Resources

About