Glacier mass budget and climate reanalysis data indicate a climatic shift around 2000 in Lahaul-Spiti, western Himalaya

Mukherjee, Kriti; Bhattacharya, Atanu; Pieczonka, Tino; Ghosh, S.; Bolch, Tobias

doi:10.1007/s10584-018-2185-3

Cited by 60 publications

(20 citation statements)

References 51 publications

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“…While this difference in total mass loss rate may be related to differences in methodology, it could also be influenced by the inclusion of an additional 2+ years (2016)(2017)(2018) of DEMs later in the 2000 to 2018 study period. Based on observed long-term glacier mass balance trends for HMA (e.g., Mukherjee et al, 2018;Zhou et al, 2018;Maurer et al, 2019) and glaciers worldwide, we might expect greater mass loss toward the end of the record as opposed to earlier in the record. Despite a more negative total mass balance, we find a smaller sea level rise contribution from exhoreic basins (13.3 ± 2.3 Gt yr −1 ) compared to the 14.6 ± 3.1 Gt yr −1 estimate by Brun et al (2017).…”

Section: Full Hma Glacier Mass Balancementioning

confidence: 99%

A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance

et al. 2020

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High-mountain Asia (HMA) constitutes the largest glacierized region outside of the Earth's polar regions. Although available observations are limited, long-term records indicate sustained HMA glacier mass loss since ∼1850, with accelerated loss in recent decades. Recent satellite data capture the spatial variability of this mass loss, but spatial resolution is coarse and some estimates for regional and HMA-wide mass loss disagree. To address these issues, we generated 5,797 high-resolution digital elevation models (DEMs) from available sub-meter commercial stereo imagery (DigitalGlobe WorldView-1/2/3 and GeoEye-1) acquired over HMA glaciers from 2007 to 2018 (primarily 2013-2017). We also reprocessed 28,278 ASTER DEMs over HMA from 2000 to 2018. We combined these observations to generate robust elevation change trend maps and geodetic mass balance estimates for 99% of HMA glaciers between 2000 and 2018. We estimate total HMA glacier mass change of −19.0 ± 2.5 Gt yr −1 (−0.19 ± 0.03 m w.e. yr −1 ). We document the spatial pattern of HMA glacier mass change with unprecedented detail, and present aggregated estimates for HMA glacierized sub-regions and hydrologic basins. Our results offer improved estimates for the HMA contribution to global sea level rise in recent decades with total cumulative sea-level rise contribution of ∼0.7 mm from exorheic basins between 2000 and 2018. We estimate that the range of excess glacier meltwater runoff due to negative glacier mass balance in each basin constitutes ∼12-53% of the total basin-specific glacier meltwater runoff. These results can be used for calibration and validation of glacier mass balance models, satellite gravimetry observations, and hydrologic models needed for present and future water resource management.

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Section: Full Hma Glacier Mass Balancementioning

confidence: 99%

A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance

et al. 2020

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“…Due to such rapid recession of glaciers, associated catastrophic events such as glacial lake outburst flood (Bajracharya, Mool, and Shrestha 2007), avalanche, rockfall and water scarcity in the upper Himalaya villages are commonly reported (Kulkarni et al 2002;Kulkarni 2007). Although Mass balance studies across the Himalayan glaciers are limited (Mukherjee et al 2018;Brun et al 2017;Kääb et al 2012), most of these studies have primarily focused on the fluctuation of the glacier area and terminus (Table 1). In order to understand the regional climate, mitigate glacial hazards, water resource planning and predicting the future availability of water, it is indispensable, therefore, to study and monitor the Himalayan glaciers precisely.…”

Section: Himalaya and Climate Changementioning

confidence: 99%

Development of glacier mapping in Indian Himalaya: a review of approaches

Kaushik

Joshi

Singh

2019

International Journal of Remote Sensing

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The paper reviews the status of glacier mapping with special reference to the Indian Himalaya. The review provides information on various satellite remote sensing data interpretation methods used with special emphasis laid on recent semi-automated algorithms used for glacier and debris-cover mapping, along with their limitations and challenges. Further, the pragmatic solutions on offer are discussed, and the emerging areas of glacier mapping research are highlighted. The review also touchescontribution of Survey of India (SOI) and Geological Survey of India (GSI) in the glacier mapping. Finally, it discusses the wider range of spatial and spectral domains in which remote sensing data helps to inventories glaciers. The review also identifies gaps in using the latest techniques like Unmanned Aerial Vehicles (UAVs) and machine learning algorithms to improvise on the ongoing efforts. At last, the review provides an exhaustive list of references on glacier mapping from the Indian Himalaya as benefit to readers.

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“…Glacier. Mukherjee et al (2018) estimated glacier area and length change of Samudra Tapu Glacier from 1971 to 2015 and correlated this with mass balance in the same period. Vijay and Braun (2016) analysed mass balance of all glaciers along with Samudra Tapu Glacier in Chandra Basin during 2000-2013.…”

Section: Researchers Have Carried Out Various Studies On Samudra Tapumentioning

confidence: 99%

Surface Velocity Dynamics of Samudra Tapu Glacier, India From 2013 to 2017 Using Landsat-8 Data

Sahu

Gupta

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In glacier dynamics, surface velocity of glacier is an important parameter to understand the behaviour of glacier in absence of mass balance and long-term glacier area change information. In present study, surface velocity of Samudra Tapu Glacier, India is estimated using freely available Landsat-8 OLI (PAN) images during 2013–2017. To estimate surface velocity, open source COSI-Corr tool is used which is based on cross-correlation algorithm. Maximum annual surface velocity estimated is 55.68 ± 4.01 m/year during 2015–2016 while the minimum surface velocity being 44.99 ± 4.67 m/year in 2016–2017. The average annual velocity during 2013–2017 was 50.51 ± 4.49 m/year which is higher than other glaciers in Chandra basin. The variation in annual surface velocity is analysed which not only depends on mass loss but also on temperature, pressure and internal drainage. Further, as one moves opposite to glacier terminus, the surface velocity increases with the increase in glacier elevation and slope. The higher surface velocity can be attributed to the fact that Samudra Tapu is a top-heavy glacier based on HI index analysis having larger accumulation area along with high glacier ice-thickness.

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Glacier mass budget and climate reanalysis data indicate a climatic shift around 2000 in Lahaul-Spiti, western Himalaya

Cited by 60 publications

References 51 publications

A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance

A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance

Development of glacier mapping in Indian Himalaya: a review of approaches

Surface Velocity Dynamics of Samudra Tapu Glacier, India From 2013 to 2017 Using Landsat-8 Data

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