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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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 |
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Open Polar |
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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 |
_version_ |
1797586896595976192 |