Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization

Permafrost has become increasingly unstable as a result of surface warming; therefore it is crucial to improve our understanding of permafrost spatiotemporal dynamics to assess the impact of active layer thickening on future hydrogeological processes. However, direct determinations of permafrost act...

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Published in:Geophysical Research Letters
Main Authors: de Bruin, Jelte G.H., Bense, Victor F., van der Ploeg, Martine J.
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Online Access:https://research.wur.nl/en/publications/inferring-permafrost-active-layer-thermal-properties-from-numeric
https://doi.org/10.1029/2021GL093306
id ftunivwagenin:oai:library.wur.nl:wurpubs/586974
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spelling ftunivwagenin:oai:library.wur.nl:wurpubs/586974 2024-04-28T07:53:14+00:00 Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization de Bruin, Jelte G.H. Bense, Victor F. van der Ploeg, Martine J. 2021 application/pdf https://research.wur.nl/en/publications/inferring-permafrost-active-layer-thermal-properties-from-numeric https://doi.org/10.1029/2021GL093306 en eng https://edepot.wur.nl/553838 https://research.wur.nl/en/publications/inferring-permafrost-active-layer-thermal-properties-from-numeric doi:10.1029/2021GL093306 https://creativecommons.org/licenses/by-nc/4.0/ Wageningen University & Research Geophysical Research Letters 48 (2021) 16 ISSN: 0094-8276 active layer permafrost thermal properties Article/Letter to editor 2021 ftunivwagenin https://doi.org/10.1029/2021GL093306 2024-04-03T15:01:56Z Permafrost has become increasingly unstable as a result of surface warming; therefore it is crucial to improve our understanding of permafrost spatiotemporal dynamics to assess the impact of active layer thickening on future hydrogeological processes. However, direct determinations of permafrost active-layer thermal properties are few, resulting in large uncertainty in forecasts of active layer thickness. To assess how to reduce the uncertainty without expanding monitoring efforts, a total of 1,728 numerical 1D models were compared using three error measures against observed active layer temperature data from the Qinghai-Tibetan Plateau. Resulting optimized parameter values varied depending on the error measure used, but agree with reported ones: bulk volumetric heat capacity is 1.82–1.94 (Formula presented.) K, bulk thermal conductivity 1.0–1.2 W/m K and porosity 0.25–0.45 (Formula presented.). The active layer thickening rate varied significantly for the three error measures, as demonstrated by a (Formula presented.) years thawing time-lag between the error measures over a 100 years modeling period. Article in Journal/Newspaper Active layer temperature Active layer thickness permafrost Wageningen UR (University & Research Centre): Digital Library Geophysical Research Letters 48 16
institution Open Polar
collection Wageningen UR (University & Research Centre): Digital Library
op_collection_id ftunivwagenin
language English
topic active layer
permafrost
thermal properties
spellingShingle active layer
permafrost
thermal properties
de Bruin, Jelte G.H.
Bense, Victor F.
van der Ploeg, Martine J.
Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization
topic_facet active layer
permafrost
thermal properties
description Permafrost has become increasingly unstable as a result of surface warming; therefore it is crucial to improve our understanding of permafrost spatiotemporal dynamics to assess the impact of active layer thickening on future hydrogeological processes. However, direct determinations of permafrost active-layer thermal properties are few, resulting in large uncertainty in forecasts of active layer thickness. To assess how to reduce the uncertainty without expanding monitoring efforts, a total of 1,728 numerical 1D models were compared using three error measures against observed active layer temperature data from the Qinghai-Tibetan Plateau. Resulting optimized parameter values varied depending on the error measure used, but agree with reported ones: bulk volumetric heat capacity is 1.82–1.94 (Formula presented.) K, bulk thermal conductivity 1.0–1.2 W/m K and porosity 0.25–0.45 (Formula presented.). The active layer thickening rate varied significantly for the three error measures, as demonstrated by a (Formula presented.) years thawing time-lag between the error measures over a 100 years modeling period.
format Article in Journal/Newspaper
author de Bruin, Jelte G.H.
Bense, Victor F.
van der Ploeg, Martine J.
author_facet de Bruin, Jelte G.H.
Bense, Victor F.
van der Ploeg, Martine J.
author_sort de Bruin, Jelte G.H.
title Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization
title_short Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization
title_full Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization
title_fullStr Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization
title_full_unstemmed Inferring Permafrost Active Layer Thermal Properties From Numerical Model Optimization
title_sort inferring permafrost active layer thermal properties from numerical model optimization
publishDate 2021
url https://research.wur.nl/en/publications/inferring-permafrost-active-layer-thermal-properties-from-numeric
https://doi.org/10.1029/2021GL093306
genre Active layer temperature
Active layer thickness
permafrost
genre_facet Active layer temperature
Active layer thickness
permafrost
op_source Geophysical Research Letters 48 (2021) 16
ISSN: 0094-8276
op_relation https://edepot.wur.nl/553838
https://research.wur.nl/en/publications/inferring-permafrost-active-layer-thermal-properties-from-numeric
doi:10.1029/2021GL093306
op_rights https://creativecommons.org/licenses/by-nc/4.0/
Wageningen University & Research
op_doi https://doi.org/10.1029/2021GL093306
container_title Geophysical Research Letters
container_volume 48
container_issue 16
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