期刊
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
卷 127, 期 7, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021JG006750
关键词
Arctic; permafrost; alluvial fan; polygon; tundra
资金
- Sentinel North postdoctoral fellowship of the Universite Laval (Canada First Research Excellence Fund)
- NSERC
- French Polar Institute (IPEV)
- Polar Continental Shelf Program
The distribution of soil carbon stocks varies among different geomorphological terrain units in glacial valleys of the Arctic, with the highest soil carbon and nitrogen content found in humid polygons. These findings are crucial for refining estimates of soil carbon and nitrogen stocks in the pan-Arctic region and improving climate models.
Soils of circumpolar regions store large amounts of carbon (C) and are a crucial part of the global C cycle. Yet, little is known about the distribution of soil C stocks among geomorphological terrain units of glacial valleys in the Arctic. Soil C and nitrogen (N) content for the top 100 cm of the dominant vegetated geomorphological terrain units (i.e., alluvial fans, humid polygons, mesic polygons) at Qarlikturvik Valley, Bylot Island, Canada have been analyzed. Soil C content was greatest in humid low-center ice-wedge polygons (82 kg m(-2)), followed by mesic flat-center ice-wedge polygons (40 kg m(-2)), and alluvial fan area (16 kg m(-2)), due to prevailing geomorphological processes, differences in vegetation and soil characteristics, as well as permafrost processes. Soil N content was greatest in humid polygons (4 kg m(-2)), followed by mesic polygons (2 kg m(-2)), and alluvial fan area (1 kg m(-2)). Vertically, C and N decreased with increasing depth except for a peak in C at depth in humid polygons, a likely result of past changes in vegetation cover. At Qarlikturvik Valley, which has a size of 121.7 km(2), alluvial fans store 0.226 Tg organic C and humid and mesic polygons store 1.643 and 0.218 Tg organic C, respectively in the top 100 cm of soil. Findings like these are important to further constrain pan-Arctic soil C and N stock estimates and thus climate models.
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