Towards Bedmap Himalayas: a new airborne glacier thickness survey in Khumbu Himal, Nepal [in review]

Pritchard, Hamish ORCID: https://orcid.org/0000-0003-2936-1734; King, Edward C. ORCID: https://orcid.org/0000-0003-3793-3915; Goodger, David J.; Boyle, Douglas; Goldberg, Daniel N. ORCID: https://orcid.org/0000-0001-9130-4461; Recinos, Beatriz ORCID: https://orcid.org/0000-0002-0647-8131; Orr, Andrew ORCID: https://orcid.org/0000-0001-5111-8402; Regmi, Dhananjay. 2025 Towards Bedmap Himalayas: a new airborne glacier thickness survey in Khumbu Himal, Nepal [in review]. Earth System Science Data: Discussions. 10.5194/essd-2025-519

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Abstract/Summary

Mountain glaciers provide an important service in sustaining river flows for large populations downstream of High Mountain Asia (HMA) but these glaciers are retreating and the future of this water resource is highly uncertain. Glacier thickness measurements are vital for accurate mapping of the remaining ice reserve and for predicting where and how fast it will decline under climate change, but such measurements are severely lacking in this region due to the difficulties of surveying in remote, high-altitude settings. We report on a uniquely extensive new thickness dataset for eleven glaciers in the Khumbu Himal around Mount Everest that we collected in late 2019 using a novel, low-frequency helicopter-borne radar. To aid in interpreting the survey radargrams we developed a terrain clutter model, and we succeeded in mapping ice thickness with a precision of around ±7 % and horizontal spacing of around 40 m, for thicknesses of up to 445 m and spanning a total of 119 line-km, approximately doubling the length of previous thickness surveys in HMA. To demonstrate the utility of our new measurements, we compare them to existing modelled thickness products and find that the models struggle to reproduce the distribution of ice in these complex, steep, rapidly slowing, thinning and stagnating glaciers, with widespread systematic thin and thick biases equivalent to around half of the measured ice thickness or more. This new dataset (https://doi.org/10.5285/e39647f5-fb72-4d16-acbd-9784ed2167b8) permits for the first time a detailed analysis of model performance on Himalayan glaciers, a key step in improving model skill and hence the accuracy of modelled thickness distributions and future ice loss on the mountain-range scale.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.5194/essd-2025-519
ISSN:	18663508
NORA Subject Terms:	Glaciology
Date made live:	01 Oct 2025 14:34 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/540237

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