Acceleration of ice loss across the Himalayas over the past 40 years

Maurer, Joshua A.; Schaefer, Joerg M.; Rupper, S.; Corley, A.

doi:10.1126/sciadv.aav7266

Cited by 407 publications

(297 citation statements)

References 73 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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“…Owing to these reasons, quantifying the mass loss over different Himalayan regions in the past 74 years, ascertaining present status of the cryosphere and how these changes are likely to affect the freshwater 75 accessibility in the region are at the forefront of contemporary cryospheric research (Brun et al 2017; Sakai and 76Fujita, 2017). This aptly triggered several regional (Kaab et al, 2012; Gardelle et al, 2013; Brun et al 2017; 77 Zhou et al, 2018;Maurer et al, 2019) , local (Bhushan et al, 2018 Vijay and Braun, 2018) and glacier specific 78 studies (Dobhal et al, 2013;Bhattacharya et al, 2016;Azam et al, 2018) in the region. These studies at varying 79 https://doi.scales contribute towards solving the jigsaw puzzle of the Himalayan cryosphere.…”

mentioning

confidence: 99%

“…Fujita, 2017). This aptly triggered several regional (Kaab et al, 2012; Gardelle et al, 2013; Brun et al 2017; 77 Zhou et al, 2018;Maurer et al, 2019) , local (Bhushan et al, 2018 Vijay and Braun, 2018) and glacier specific 78 studies (Dobhal et al, 2013;Bhattacharya et al, 2016;Azam et al, 2018) in the region. These studies at varying 79 https://doi.scales contribute towards solving the jigsaw puzzle of the Himalayan cryosphere.…”

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confidence: 99%

Temporal inventory of glaciers in the Suru sub-basin, western Himalaya: Impacts of the regional climate variability

Shukla¹,

Garg²,

Mehta³

et al. 2019

Preprint

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Abstract. Updated knowledge about the glacier extent and characteristics in the Himalaya cannot be overemphasised. Availability of precise glacier inventories in the latitudinally diverse western Himalayan region is particularly crucial. In this study we have created an inventory of the Suru sub-basin, western Himalaya for year 2017 using Landsat OLI data. Changes in glacier parameters have also been monitored from 1971 to 2017 using temporal satellite remote sensing data and limited field observations. Inventory data shows that the sub-basin has 252 glaciers covering 11 % of the basin, having an average slope of 25 ± 6° and dominantly north orientation. The average snow line altitude (SLA) of the basin is 5011 ± 54 masl with smaller (47 %) and cleaner (43 %) glaciers occupying the bulk area. Longterm climate data (1901–2017) shows an increase in the mean annual temperature (Tmin & Tmax) by 0.77 ºC (0.25 & 1.3 ºC) in the sub-basin, driving the overall glacier variability in the region. Temporal analysis reveals a glacier shrinkage of ~ 6 ± 0.02 %, an average retreat rate of 4.3 ± 1.02 ma−1, debris increase of 62 % and 22 ± 60 m SLA rise in past 46 years. This confirms their transitional response between the Karakoram and the Greater Himalayan Range (GHR) glaciers. Besides, glaciers in the sub-basin occupy two major ranges, i.e., GHR and Ladakh range (LR) and experience local climate variability, with the GHR glaciers exhibiting a warmer and wetter climate as compared to the LR glaciers. This variability manifestes itself in the varied response of GHR and LR glaciers. While the GHR glaciers exhibit an overall rise in SLA (GHR: 49 ± 69 m; LR: decrease by 18 ± 50 m), the LR glaciers have deglaciated more (LR: 7 %; GHR: 6 %) with an enhanced accumulation of debris cover (LR: 73 %; GHR: 59 %). Inferences from this study reveal prevalence of glacier disintegration and overall degeneration, transition of clean ice to partially debris covered glaciers, local climate variability and non-climatic (topographic and morphometric) factor induced heterogeinty in glacier response as the major processes operatives in this region. The dataset (Shukla et al., 2019) is accessible at https://doi.pangaea.de/10.1594/PANGAEA.904131.

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“…Given the lack of statistically significant information to support retreat of glaciers in Hunza and Mangla, we have assumed a fixed area for the glaciers. This would not be an appropriate assumption for glaciers in the region more broadly; for example, Maurer et al () report in the Himalayas, to the east of the study area, shrinking of glaciers over the past 40 years, and the increasing rate of that shrinkage.…”

Section: Methodsmentioning

confidence: 97%

Amplification of hydrological model uncertainties in projected climate simulations of the Upper Indus Basin: Does it matter where the water is coming from?

Dolk

Penton

Ahmad

2020

Hydrological Processes

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There is a growing appreciation of the uncertainties in the estimation of snow-melt and glacier-melt as a result of climate change in high elevation catchments. Through a detailed examination of three hydrological models in two catchments, and interpretation of results from previous studies, we observed that many variations in estimated streamflow could be explained by the selection of a best parameter set from the possible good model parameters. The importance of understanding changing glacial dynamics is critically important for our study areas in the Upper Indus Basin where Pakistan's policymakers are planning infrastructure to meet the future energy and water needs of hundreds of millions of people downstream. Yet, the effect of climate on glacial runoff and climate on snowmelt runoff is poorly understood. With the HBV model, for example, we estimated glacial melt as between 56% and 89% for the Hunza catchment. When rainfall was a scaled parameter, the models estimated glacial melt as between 20% and 100% of streamflow. These parameter sets produced wildly different projections of future climate for RCP8.5 scenarios in 2046-2075 compared to 1976-2005. Assuming no glacial shrinkage, for one climate projection, we found that the choice among good parameter sets resulted in projected values of future streamflow across a range from +54% to +125%. Parameter selection was the most significant source of uncertainty in the glaciated catchment and amplified climate model uncertainty, whereas climate model choice was more important in the rainfall dominated catchment. Although the study focuses on Pakistan, the overall conclusions are instructive for other similar regions in the world.We suggest that modellers of glaciated catchments should present results from at least the book-ends: models with low sensitivity to ice-melt and models with high sensitivity to ice-melt. This would reduce confusion among decision makers when they are faced with similar contrasting results.

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Acceleration of ice loss across the Himalayas over the past 40 years

Cited by 407 publications

References 73 publications

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

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

Temporal inventory of glaciers in the Suru sub-basin, western Himalaya: Impacts of the regional climate variability

Amplification of hydrological model uncertainties in projected climate simulations of the Upper Indus Basin: Does it matter where the water is coming from?

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