Changing regimes of gangotri and surrounding glaciers: A case study of Garhwal Himalaya, India

Kumar, Praveen; Mohan, Kshitij; Mishra, Varun Narayan; Biswari, Mayank; Sharma, Arjun D.; Rai, S.C.

doi:10.1016/j.rsase.2016.02.001

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…The isotopic values of groundwater in this study lie between river water and precipitation (snow‐rain) (Table 2 and Figure 4), indicating that groundwater recharge primarily occurs from these two sources. During the spring season, depleted δ 18 O signatures and lower EC values are observed in groundwater (Table 2), which can be attributed to the influence of snowmelt recharge (Baraer et al, 2015; Rai et al, 2016). In contrast, during the summer season, groundwater in Lidder and Sindh exhibits depleted δ 18 O values and higher EC values (Figure 4).…”

Section: Discussionmentioning

confidence: 99%

Understanding hydrological processes of glacierized catchments in the western Himalayas by a multi‐year tracer‐based hydrograph separation analysis

Dar,

Rai,

Kumar

et al. 2024

Hydrological Processes

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Snow and glacier melt are significant contributors to streamflow in Himalayan catchments, and their increasing contributions serve as key indicators of climate change. Consequently, the quantification of these streamflow components holds significant importance for effective water resource management. In this study, we utilized the spatio‐temporal variability of isotopic signatures in stream water, rainfall, winter fresh snow, snowpack, glaciers, springs, and wells, in conjunction with hydrometeorological observations and Snow Cover Area (SCA) data, to identify water sources and develop a conceptual understanding of streamflow dynamics in three catchments (Lidder, Sindh, and Vishow) within the western Himalayas. The following results were obtained: (a) endmember contributions to the streamflow exhibit significant spatial and seasonal variability across the three catchments during 2018–2020; (b) snowmelt dominates streamflow, with average contributions across the entire catchment varying: 59% ± 9%, 55% ± 4%, 56% ± 6%, and 55% ± 9% in Lidder, 43% ± 6%, 38% ± 6%, 32% ± 4%, and 33% ± 5% in Sindh and 45% ± 8%, 40% ± 6%, 39% ± 6%, and 32% ± 5% in Vishow during spring, summer, autumn, and winter seasons, respectively; (c) glacier melt contributions can reach ~30% to streamflow near the source regions during peak summer; (d) The primary uncertainties in streamflow components are attributed to the spatiotemporal variability of tracer signatures of winter fresh snow/snowpack (±1.9% to ±20%); (e)regarding future streamflow components, if the glacier contribution were to disappear completely, the annual average streamflow in Lidder and Sindh could decrease up to ~20%. The depletion of the cryosphere in the region has led to a rapid increase in runoff (1980–1900), but it has also resulted in a significant streamflow reduction due to glacier mass loss and changes in peak streamflow over the past three decades (1990–2020). The findings highlight the significance of environmental isotope analysis, which provides insights into water resources and offers a critical indication of the streamflow response to glacier loss under a changing climate.

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Section: Discussionmentioning

confidence: 99%

Understanding hydrological processes of glacierized catchments in the western Himalayas by a multi‐year tracer‐based hydrograph separation analysis

Dar,

Rai,

Kumar

et al. 2024

Hydrological Processes

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“…The sum of the glacier accumulation and ablation areas makes a total glacier area Area Accumulation Ratio (AAR) is calculated from accumulation area divided by total glacial area (Kulkarni 1992, Kulkarni et al 2004Rai et al 2009Rai et al , 2013Rai et al , 2016. A remote sensing-based calculated AAR is useful for glaciers mapping where field data are not available (Kulkarni 1992).…”

Section: Methodsmentioning

confidence: 99%

“…Besides, a numbers of studies using satellite data were made by Bhambri et al (2011Bhambri et al ( , 2012, Dozier and Hall (1987), Kulkarni et al ( , 2004Kulkarni et al ( , 2005Kulkarni et al ( , 2007Kulkarni et al ( , 2011, Kordzakhia et al (2015), Pandey et al (2012) and Rai et al (2009Rai et al ( , 2013Rai et al ( , 2016. It was showed that the best method for analysis of glaciers is application of combined approach of satellite remote sensing with terrestrial observations and expert knowledge of separate glaciers.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing-Based Study for Evaluating the Changes in Glacial Area: A Case Study from Himachal Pradesh, India

et al. 2017

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“…The ablation zone of these glaciers is extensively debris-covered, having a potential to affect their albedo and melting patterns. Past observations on these glaciers show that most of them have been losing mass and retreating at varying rates [31]. Considering its religious and social significance, the Gangotri glacier has gained significant media and scientific attention in the recent times.…”

Section: Research Area and Datasetsmentioning

confidence: 99%

On Drivers of Subpixel Classification Accuracy—An Example From Glacier Facies

Yousuf

Shukla

Arora

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Subpixel classification (SPC) extracts meaningful information on land-cover classes from the mixed pixels. However, the major challenges for SPC are to obtain reliable soft reference data (RD), use apt input data, and achieve maximum accuracy. This article addresses these issues and applies the support vector machine (SVM) to retrieve the subpixel estimates of glacier facies (GF) using high radiometric-resolution Advanced Wide Field Sensor (AWiFS) data. Precise quantification of GF has fundamental importance in the glaciological research. Efficacy of the approach was first tested on the synthetic data followed by the input AWiFS and reference MultiSpectral Instrument data, including ancillary data. SPC of synthetic data resulted in overall accuracy (OA) of 95%, proving the merit of SVM. Classification accuracy is inversely related to the glacier's surface heterogeneity. Reducing the number of classes enhanced the OA by ∼18%. Source and timing of RD invariably controls the SPC accuracy. OA improved by ∼5% on addressing the issue of temporal gap between input and RD. ∼11% increase in OA with the inclusion of ancillary data confirmed their positive effect on the accuracy. Input and reference fractional area of GF were strongly correlated (r > 0.9) with each other substantiating the results.

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Changing regimes of gangotri and surrounding glaciers: A case study of Garhwal Himalaya, India

Cited by 7 publications

References 37 publications

Understanding hydrological processes of glacierized catchments in the western Himalayas by a multi‐year tracer‐based hydrograph separation analysis

Understanding hydrological processes of glacierized catchments in the western Himalayas by a multi‐year tracer‐based hydrograph separation analysis

Remote Sensing-Based Study for Evaluating the Changes in Glacial Area: A Case Study from Himachal Pradesh, India

On Drivers of Subpixel Classification Accuracy—An Example From Glacier Facies

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