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Quantifying Debris Thickness of Debris‐Covered Glaciers in the Everest Region of Nepal Through Inversion of a Subdebris Melt Model

Authors: David R. Rounce; David R. Rounce; Owen King; Michael McCarthy; Michael McCarthy; David Shean; Franco Salerno;

Quantifying Debris Thickness of Debris‐Covered Glaciers in the Everest Region of Nepal Through Inversion of a Subdebris Melt Model

Abstract

AbstractDebris‐covered glaciers are ubiquitous in the Himalaya, and supraglacial debris significantly alters how glaciers respond to climate forcing. Estimating debris thickness at the glacier scale, however, remains a challenge. This study inverts a subdebris melt model to estimate debris thickness for three glaciers in the Everest region from digital elevation model difference‐derived elevation change. Flux divergences are estimated from ice thickness and surface velocity data. Monte Carlo simulations are used to incorporate the uncertainties associated with debris properties, flux divergence, and elevation change. On Ngozumpa Glacier, surface lowering data from 2010 to 2012 and 2012 to 2014 are used to calibrate and validate the method, respectively. The debris thickness estimates are consistent with existing in situ measurements. The method performs well over both actively flowing and stagnant parts of the glacier and is able to accurately estimate thicker debris (>0.5 m). Uncertainties associated with the thermal conductivity and elevation change contribute the most to uncertainties of the debris thickness estimates. The surface lowering associated with ice cliffs and supraglacial ponds was found to significantly reduce debris thickness, especially for thicker debris. The method is also applied to Khumbu and Imja‐Lhotse Shar Glaciers to highlight its potential for regional application.

Countries
Italy, United Kingdom, Italy
Keywords

690, debris thickness, debris-covered glaciers, debris properties, Everest region, climate change, glacier mass balance, Nepal Himalaya

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    62
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    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
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    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
62
Top 1%
Top 10%
Top 1%
Green
bronze