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Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling

Ice cliffs are common on debris‐covered glaciers and have relatively high melt rates due to their direct exposure to incoming radiation. Previous studies have shown that their number and relative area can change considerably from year to year, but this variability has not been explored, in part beca...

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Autores principales: Kneib, M., Miles, E. S., Buri, P., Molnar, P., McCarthy, M., Fugger, S., Pellicciotti, F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285626/
https://www.ncbi.nlm.nih.gov/pubmed/35860443
http://dx.doi.org/10.1029/2021JF006179
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author Kneib, M.
Miles, E. S.
Buri, P.
Molnar, P.
McCarthy, M.
Fugger, S.
Pellicciotti, F.
author_facet Kneib, M.
Miles, E. S.
Buri, P.
Molnar, P.
McCarthy, M.
Fugger, S.
Pellicciotti, F.
author_sort Kneib, M.
collection PubMed
description Ice cliffs are common on debris‐covered glaciers and have relatively high melt rates due to their direct exposure to incoming radiation. Previous studies have shown that their number and relative area can change considerably from year to year, but this variability has not been explored, in part because available cliff observations are irregular. Here, we systematically mapped and tracked ice cliffs across four debris‐covered glaciers in High Mountain Asia for every late ablation season from 2009 to 2019 using high‐resolution multi‐spectral satellite imagery. We then quantified the processes occurring at the feature scale to train a stochastic birth‐death model to represent the cliff population dynamics. Our results show that while the cliff relative area can change by up to 20% from year to year, the natural long‐term variability is constrained, thus defining a glacier‐specific cliff carrying capacity. In a subsequent step, the inclusion of external drivers related to climate, glacier dynamics, and hydrology highlights the influence of these variables on the cliff population dynamics, which is usually not a direct one due to the complexity and interdependence of the processes taking place at the glacier surface. In some extreme cases (here, a glacier surge), these external drivers may lead to a reorganization of the cliffs at the glacier surface and a change in the natural variability. These results have implications for the melt of debris‐covered glaciers, in addition to showing the high rate of changes at their surface and highlighting some of the links between cliff population and glacier state.
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spelling pubmed-92856262022-07-18 Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling Kneib, M. Miles, E. S. Buri, P. Molnar, P. McCarthy, M. Fugger, S. Pellicciotti, F. J Geophys Res Earth Surf Research Article Ice cliffs are common on debris‐covered glaciers and have relatively high melt rates due to their direct exposure to incoming radiation. Previous studies have shown that their number and relative area can change considerably from year to year, but this variability has not been explored, in part because available cliff observations are irregular. Here, we systematically mapped and tracked ice cliffs across four debris‐covered glaciers in High Mountain Asia for every late ablation season from 2009 to 2019 using high‐resolution multi‐spectral satellite imagery. We then quantified the processes occurring at the feature scale to train a stochastic birth‐death model to represent the cliff population dynamics. Our results show that while the cliff relative area can change by up to 20% from year to year, the natural long‐term variability is constrained, thus defining a glacier‐specific cliff carrying capacity. In a subsequent step, the inclusion of external drivers related to climate, glacier dynamics, and hydrology highlights the influence of these variables on the cliff population dynamics, which is usually not a direct one due to the complexity and interdependence of the processes taking place at the glacier surface. In some extreme cases (here, a glacier surge), these external drivers may lead to a reorganization of the cliffs at the glacier surface and a change in the natural variability. These results have implications for the melt of debris‐covered glaciers, in addition to showing the high rate of changes at their surface and highlighting some of the links between cliff population and glacier state. John Wiley and Sons Inc. 2021-10-13 2021-10 /pmc/articles/PMC9285626/ /pubmed/35860443 http://dx.doi.org/10.1029/2021JF006179 Text en © 2021. The Authors. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kneib, M.
Miles, E. S.
Buri, P.
Molnar, P.
McCarthy, M.
Fugger, S.
Pellicciotti, F.
Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling
title Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling
title_full Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling
title_fullStr Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling
title_full_unstemmed Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling
title_short Interannual Dynamics of Ice Cliff Populations on Debris‐Covered Glaciers From Remote Sensing Observations and Stochastic Modeling
title_sort interannual dynamics of ice cliff populations on debris‐covered glaciers from remote sensing observations and stochastic modeling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285626/
https://www.ncbi.nlm.nih.gov/pubmed/35860443
http://dx.doi.org/10.1029/2021JF006179
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