High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
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. A...
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ftdoajarticles:oai:doaj.org/article:45eb3ff63d6a45c8a4c2af15abe4f548 2023-10-09T21:49:00+02:00 High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra Julia Wagner Victoria Martin Niek J. Speetjens Willeke A'Campo Luca Durstewitz Rachele Lodi Michael Fritz George Tanski Jorien E. Vonk Andreas Richter Annett Bartsch Hugues Lantuit Gustaf Hugelius 2023-10-01T00:00:00Z https://doi.org/10.1016/j.geoderma.2023.116652 https://doaj.org/article/45eb3ff63d6a45c8a4c2af15abe4f548 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S0016706123003294 https://doaj.org/toc/1872-6259 1872-6259 doi:10.1016/j.geoderma.2023.116652 https://doaj.org/article/45eb3ff63d6a45c8a4c2af15abe4f548 Geoderma, Vol 438, Iss , Pp 116652- (2023) Random forest Machine learning Soil organic carbon Tundra Permafrost Science Q article 2023 ftdoajarticles https://doi.org/10.1016/j.geoderma.2023.116652 2023-09-10T00:34:17Z 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 1 m depth at a spatial resolution of 2 m for depth increments of 0–5, 5–15, 15–30, 30–60 and 60–100 cm.The results show that the two study areas had large differences in SOC stocks in the depth 60–100 cm 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 66.6 kg C/m−2 more stored SOC than the area rich in ground ice content with 40.0 kg 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 3.77 kg m−2 and of the area rich in ground ice is 3.83 kg m−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 Arctic Beaufort Sea Climate change Ice permafrost Tundra Directory of Open Access Journals: DOAJ Articles Arctic Geoderma 438 116652 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Random forest Machine learning Soil organic carbon Tundra Permafrost Science Q |
spellingShingle |
Random forest Machine learning Soil organic carbon Tundra Permafrost Science Q Julia Wagner Victoria Martin Niek J. Speetjens Willeke A'Campo Luca Durstewitz Rachele Lodi Michael Fritz George Tanski Jorien E. Vonk Andreas Richter Annett Bartsch Hugues Lantuit Gustaf Hugelius 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 Science Q |
description |
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 1 m depth at a spatial resolution of 2 m for depth increments of 0–5, 5–15, 15–30, 30–60 and 60–100 cm.The results show that the two study areas had large differences in SOC stocks in the depth 60–100 cm 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 66.6 kg C/m−2 more stored SOC than the area rich in ground ice content with 40.0 kg 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 3.77 kg m−2 and of the area rich in ground ice is 3.83 kg m−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 |
Julia Wagner Victoria Martin Niek J. Speetjens Willeke A'Campo Luca Durstewitz Rachele Lodi Michael Fritz George Tanski Jorien E. Vonk Andreas Richter Annett Bartsch Hugues Lantuit Gustaf Hugelius |
author_facet |
Julia Wagner Victoria Martin Niek J. Speetjens Willeke A'Campo Luca Durstewitz Rachele Lodi Michael Fritz George Tanski Jorien E. Vonk Andreas Richter Annett Bartsch Hugues Lantuit Gustaf Hugelius |
author_sort |
Julia Wagner |
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 |
Elsevier |
publishDate |
2023 |
url |
https://doi.org/10.1016/j.geoderma.2023.116652 https://doaj.org/article/45eb3ff63d6a45c8a4c2af15abe4f548 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Beaufort Sea Climate change Ice permafrost Tundra |
genre_facet |
Arctic Beaufort Sea Climate change Ice permafrost Tundra |
op_source |
Geoderma, Vol 438, Iss , Pp 116652- (2023) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S0016706123003294 https://doaj.org/toc/1872-6259 1872-6259 doi:10.1016/j.geoderma.2023.116652 https://doaj.org/article/45eb3ff63d6a45c8a4c2af15abe4f548 |
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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1779312044614352896 |