High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra

Abstract: " Soil organic carbon (SOC) in Arctic coastal polygonal tundra is vulnerable to climate change, especially in soils with occurrence of large amounts of ground ice. Pan-arctic studies of mapping SOC exist, yet they fail to describe the high spatial variability of SOC storage in permafr...

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Published in:Geoderma
Main Authors: Wagner, Julia, Martin, Victoria, Speetjens, Niek J., A'Campo, Willeke, Durstewitz, Luca, Lodi, Rachele, Fritz, Michael, Tanski, George, Vonk, Jorien E., Richter, Andreas, Bartsch, Annett, Lantuit, Hugues, Hugelius, Gustaf
Format: Article in Journal/Newspaper
Language:English
Published: Zenodo 2023
Subjects:
Ice
Online Access:https://doi.org/10.1016/j.geoderma.2023.116652
id ftzenodo:oai:zenodo.org:8335242
record_format openpolar
spelling ftzenodo:oai:zenodo.org:8335242 2024-09-15T17:58:50+00:00 High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra Wagner, Julia Martin, Victoria Speetjens, Niek J. A'Campo, Willeke Durstewitz, Luca Lodi, Rachele Fritz, Michael Tanski, George Vonk, Jorien E. Richter, Andreas Bartsch, Annett Lantuit, Hugues Hugelius, Gustaf 2023-09-04 https://doi.org/10.1016/j.geoderma.2023.116652 eng eng Zenodo https://zenodo.org/communities/nunataryuk https://doi.org/10.1016/j.geoderma.2023.116652 oai:zenodo.org:8335242 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Geoderma, 438(116652), (2023-09-04) Random forest Machine learning Soil organic carbon Tundra Permafrost info:eu-repo/semantics/article 2023 ftzenodo https://doi.org/10.1016/j.geoderma.2023.116652 2024-07-26T02:08:55Z Abstract: " Soil organic carbon (SOC) in Arctic coastal polygonal tundra is vulnerable to climate change, especially in soils with occurrence of large amounts of ground ice. Pan-arctic studies of mapping SOC exist, yet they fail to describe the high spatial variability of SOC storage in permafrost landscapes. An important factor is the landscape history which determines landform development and consequently the spatial variability of SOC. Our aim was to map SOC stocks, and which environmental variables that determine SOC, in two adjacent coastal areas along Canadian Beaufort Sea coast with different glacial history. We used the machine learning technique random forest and environmental variables to map the spatial distribution of SOC stocks down to 1m depth at a spatial resolution of 2m for depth increments of 0–5, 5–15, 15–30, 30–60 and 60–100cm. The results show that the two study areas had large differences in SOC stocks in the depth 60–100cm due to high amounts of ground ice in one of the study areas. There are also differences in variable importance of the explanatory variables between the two areas. The area low in ground ice content had with 66.6kg C/m −2 more stored SOC than the area rich in ground ice content with 40.0kg C/m −2 . However, this SOC stock could be potentially more vulnerable to climate change if ground ice melts and the ground subsides. The average N stock of the area low in ground ice is 3.77kgm −2 and of the area rich in ground ice is 3.83kgm −2 . These findings support that there is a strong correlation between ground ice and SOC, with less SOC in ice-rich layers on a small scale. In addition to small scale studies of SOC mapping, detailed maps of ground ice content and distribution are needed for a validation of large-scale quantifications of SOC stocks and transferability of models." Article in Journal/Newspaper Beaufort Sea Climate change Ice permafrost Tundra Zenodo Geoderma 438 116652
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic Random forest
Machine learning
Soil organic carbon
Tundra
Permafrost
spellingShingle Random forest
Machine learning
Soil organic carbon
Tundra
Permafrost
Wagner, Julia
Martin, Victoria
Speetjens, Niek J.
A'Campo, Willeke
Durstewitz, Luca
Lodi, Rachele
Fritz, Michael
Tanski, George
Vonk, Jorien E.
Richter, Andreas
Bartsch, Annett
Lantuit, Hugues
Hugelius, Gustaf
High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
topic_facet Random forest
Machine learning
Soil organic carbon
Tundra
Permafrost
description Abstract: " Soil organic carbon (SOC) in Arctic coastal polygonal tundra is vulnerable to climate change, especially in soils with occurrence of large amounts of ground ice. Pan-arctic studies of mapping SOC exist, yet they fail to describe the high spatial variability of SOC storage in permafrost landscapes. An important factor is the landscape history which determines landform development and consequently the spatial variability of SOC. Our aim was to map SOC stocks, and which environmental variables that determine SOC, in two adjacent coastal areas along Canadian Beaufort Sea coast with different glacial history. We used the machine learning technique random forest and environmental variables to map the spatial distribution of SOC stocks down to 1m depth at a spatial resolution of 2m for depth increments of 0–5, 5–15, 15–30, 30–60 and 60–100cm. The results show that the two study areas had large differences in SOC stocks in the depth 60–100cm due to high amounts of ground ice in one of the study areas. There are also differences in variable importance of the explanatory variables between the two areas. The area low in ground ice content had with 66.6kg C/m −2 more stored SOC than the area rich in ground ice content with 40.0kg C/m −2 . However, this SOC stock could be potentially more vulnerable to climate change if ground ice melts and the ground subsides. The average N stock of the area low in ground ice is 3.77kgm −2 and of the area rich in ground ice is 3.83kgm −2 . These findings support that there is a strong correlation between ground ice and SOC, with less SOC in ice-rich layers on a small scale. In addition to small scale studies of SOC mapping, detailed maps of ground ice content and distribution are needed for a validation of large-scale quantifications of SOC stocks and transferability of models."
format Article in Journal/Newspaper
author Wagner, Julia
Martin, Victoria
Speetjens, Niek J.
A'Campo, Willeke
Durstewitz, Luca
Lodi, Rachele
Fritz, Michael
Tanski, George
Vonk, Jorien E.
Richter, Andreas
Bartsch, Annett
Lantuit, Hugues
Hugelius, Gustaf
author_facet Wagner, Julia
Martin, Victoria
Speetjens, Niek J.
A'Campo, Willeke
Durstewitz, Luca
Lodi, Rachele
Fritz, Michael
Tanski, George
Vonk, Jorien E.
Richter, Andreas
Bartsch, Annett
Lantuit, Hugues
Hugelius, Gustaf
author_sort Wagner, Julia
title High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
title_short High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
title_full High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
title_fullStr High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
title_full_unstemmed High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
title_sort high resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in canadian lowland tundra
publisher Zenodo
publishDate 2023
url https://doi.org/10.1016/j.geoderma.2023.116652
genre Beaufort Sea
Climate change
Ice
permafrost
Tundra
genre_facet Beaufort Sea
Climate change
Ice
permafrost
Tundra
op_source Geoderma, 438(116652), (2023-09-04)
op_relation https://zenodo.org/communities/nunataryuk
https://doi.org/10.1016/j.geoderma.2023.116652
oai:zenodo.org:8335242
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.1016/j.geoderma.2023.116652
container_title Geoderma
container_volume 438
container_start_page 116652
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