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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Geoderma |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706123003294 |
| _version_ | 1797689557669380096 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T01:47:16Z |
| format | Article |
| id | doaj.art-45eb3ff63d6a45c8a4c2af15abe4f548 |
| institution | Directory Open Access Journal |
| issn | 1872-6259 |
| language | English |
| last_indexed | 2024-03-12T01:47:16Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoderma |
| spelling | doaj.art-45eb3ff63d6a45c8a4c2af15abe4f5482023-09-09T04:54:19ZengElsevierGeoderma1872-62592023-10-01438116652High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundraJulia Wagner0Victoria Martin1Niek J. Speetjens2Willeke A'Campo3Luca Durstewitz4Rachele Lodi5Michael Fritz6George Tanski7Jorien E. Vonk8Andreas Richter9Annett Bartsch10Hugues Lantuit11Gustaf Hugelius12Department of Physical Geography, Stockholm University, Stockholm, Sweden; Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden; Corresponding author at: Stockholm University Department of Physical Geography, Svante Arrhenius väg 8, SE-106 91 Stockholm, Sweden.University of Vienna Centre for Microbiology and Environmental System Science, Vienna, AustriaVrije Universiteit Amsterdam, Department of Earth Sciences, Amsterdam, NetherlandsDepartment of Physical Geography, Stockholm University, Stockholm, SwedenDepartment of Physical Geography, Stockholm University, Stockholm, SwedenCa' Foscari University, Department of Environmental Sciences, Informatics and Statistics, and CNR Polar Sciences Institute, Venice, ItalyAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, GermanyVrije Universiteit Amsterdam, Department of Earth Sciences, Amsterdam, Netherlands; Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, GermanyVrije Universiteit Amsterdam, Department of Earth Sciences, Amsterdam, NetherlandsUniversity of Vienna Centre for Microbiology and Environmental System Science, Vienna, Austriab.geos GmbH, Korneuburg, AustriaAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, GermanyDepartment of Physical Geography, Stockholm University, Stockholm, Sweden; Bolin Centre for Climate Research, Stockholm University, Stockholm, SwedenSoil 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.http://www.sciencedirect.com/science/article/pii/S0016706123003294Random forestMachine learningSoil organic carbonTundraPermafrost |
| spellingShingle | 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 Geoderma Random forest Machine learning Soil organic carbon Tundra Permafrost |
| title | 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_short | 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 |
| topic | Random forest Machine learning Soil organic carbon Tundra Permafrost |
| url | http://www.sciencedirect.com/science/article/pii/S0016706123003294 |
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