---
_id: '12574'
abstract:
- lang: eng
  text: Melt from supraglacial ice cliffs is an important contributor to the mass
    loss of debris-covered glaciers. However, ice cliff contribution is difficult
    to quantify as they are highly dynamic features, and the paucity of observations
    of melt rates and their variability leads to large modelling uncertainties. We
    quantify monsoon season melt and 3D evolution of four ice cliffs over two debris-covered
    glaciers in High Mountain Asia (Langtang Glacier, Nepal, and 24K Glacier, China)
    at very high resolution using terrestrial photogrammetry applied to imagery captured
    from time-lapse cameras installed on lateral moraines. We derive weekly flow-corrected
    digital elevation models (DEMs) of the glacier surface with a maximum vertical
    bias of ±0.2 m for Langtang Glacier and ±0.05 m for 24K Glacier and use change
    detection to determine distributed melt rates at the surfaces of the ice cliffs
    throughout the study period. We compare the measured melt patterns with those
    derived from a 3D energy balance model to derive the contribution of the main
    energy fluxes. We find that ice cliff melt varies considerably throughout the
    melt season, with maximum melt rates of 5 to 8 cm d−1, and their average melt
    rates are 11–14 (Langtang) and 4.5 (24K) times higher than the surrounding debris-covered
    ice. Our results highlight the influence of redistributed supraglacial debris
    on cliff melt. At both sites, ice cliff albedo is influenced by the presence of
    thin debris at the ice cliff surface, which is largely controlled on 24K Glacier
    by liquid precipitation events that wash away this debris. Slightly thicker or
    patchy debris reduces melt by 1–3 cm d−1 at all sites. Ultimately, our observations
    show a strong spatio-temporal variability in cliff area at each site, which is
    controlled by supraglacial streams and ponds and englacial cavities that promote
    debris slope destabilisation and the lateral expansion of the cliffs. These findings
    highlight the need to better represent processes of debris redistribution in ice
    cliff models, to in turn improve estimates of ice cliff contribution to glacier
    melt and the long-term geomorphological evolution of debris-covered glacier surfaces.
article_processing_charge: No
article_type: original
author:
- first_name: Marin
  full_name: Kneib, Marin
  last_name: Kneib
- first_name: Evan S.
  full_name: Miles, Evan S.
  last_name: Miles
- first_name: Pascal
  full_name: Buri, Pascal
  last_name: Buri
- first_name: Stefan
  full_name: Fugger, Stefan
  last_name: Fugger
- first_name: Michael
  full_name: McCarthy, Michael
  last_name: McCarthy
- first_name: Thomas E.
  full_name: Shaw, Thomas E.
  last_name: Shaw
- first_name: Zhao
  full_name: Chuanxi, Zhao
  last_name: Chuanxi
- first_name: Martin
  full_name: Truffer, Martin
  last_name: Truffer
- first_name: Matthew J.
  full_name: Westoby, Matthew J.
  last_name: Westoby
- first_name: Wei
  full_name: Yang, Wei
  last_name: Yang
- first_name: Francesca
  full_name: Pellicciotti, Francesca
  id: b28f055a-81ea-11ed-b70c-a9fe7f7b0e70
  last_name: Pellicciotti
citation:
  ama: Kneib M, Miles ES, Buri P, et al. Sub-seasonal variability of supraglacial
    ice cliff melt rates and associated processes from time-lapse photogrammetry.
    <i>The Cryosphere</i>. 2022;16(11):4701-4725. doi:<a href="https://doi.org/10.5194/tc-16-4701-2022">10.5194/tc-16-4701-2022</a>
  apa: Kneib, M., Miles, E. S., Buri, P., Fugger, S., McCarthy, M., Shaw, T. E., …
    Pellicciotti, F. (2022). Sub-seasonal variability of supraglacial ice cliff melt
    rates and associated processes from time-lapse photogrammetry. <i>The Cryosphere</i>.
    Copernicus Publications. <a href="https://doi.org/10.5194/tc-16-4701-2022">https://doi.org/10.5194/tc-16-4701-2022</a>
  chicago: Kneib, Marin, Evan S. Miles, Pascal Buri, Stefan Fugger, Michael McCarthy,
    Thomas E. Shaw, Zhao Chuanxi, et al. “Sub-Seasonal Variability of Supraglacial
    Ice Cliff Melt Rates and Associated Processes from Time-Lapse Photogrammetry.”
    <i>The Cryosphere</i>. Copernicus Publications, 2022. <a href="https://doi.org/10.5194/tc-16-4701-2022">https://doi.org/10.5194/tc-16-4701-2022</a>.
  ieee: M. Kneib <i>et al.</i>, “Sub-seasonal variability of supraglacial ice cliff
    melt rates and associated processes from time-lapse photogrammetry,” <i>The Cryosphere</i>,
    vol. 16, no. 11. Copernicus Publications, pp. 4701–4725, 2022.
  ista: Kneib M, Miles ES, Buri P, Fugger S, McCarthy M, Shaw TE, Chuanxi Z, Truffer
    M, Westoby MJ, Yang W, Pellicciotti F. 2022. Sub-seasonal variability of supraglacial
    ice cliff melt rates and associated processes from time-lapse photogrammetry.
    The Cryosphere. 16(11), 4701–4725.
  mla: Kneib, Marin, et al. “Sub-Seasonal Variability of Supraglacial Ice Cliff Melt
    Rates and Associated Processes from Time-Lapse Photogrammetry.” <i>The Cryosphere</i>,
    vol. 16, no. 11, Copernicus Publications, 2022, pp. 4701–25, doi:<a href="https://doi.org/10.5194/tc-16-4701-2022">10.5194/tc-16-4701-2022</a>.
  short: M. Kneib, E.S. Miles, P. Buri, S. Fugger, M. McCarthy, T.E. Shaw, Z. Chuanxi,
    M. Truffer, M.J. Westoby, W. Yang, F. Pellicciotti, The Cryosphere 16 (2022) 4701–4725.
date_created: 2023-02-20T08:09:42Z
date_published: 2022-11-11T00:00:00Z
date_updated: 2023-02-28T13:59:22Z
day: '11'
doi: 10.5194/tc-16-4701-2022
extern: '1'
intvolume: '        16'
issue: '11'
keyword:
- Earth-Surface Processes
- Water Science and Technology
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.5194/tc-16-4701-2022
month: '11'
oa: 1
oa_version: Published Version
page: 4701-4725
publication: The Cryosphere
publication_identifier:
  issn:
  - 1994-0424
publication_status: published
publisher: Copernicus Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sub-seasonal variability of supraglacial ice cliff melt rates and associated
  processes from time-lapse photogrammetry
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2022'
...
