Estimating soil carbon turnover using radiocarbon data: a case study for European Russia

Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km2 area of European Russia were estimated based on 14C data. The rates are corrected for bomb radiocarbon which strongly affects the topsoil 14C balance. The approach is applied for carbon stored in the organic and mineral la...

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Published in:Ecological Modelling
Main Authors: Brovkin, V., Cherkinsky, A., Gorachkin, S.
Format: Article in Journal/Newspaper
Language:English
Published: 2008
Subjects:
Online Access:http://hdl.handle.net/11858/00-001M-0000-0011-F98E-F
http://hdl.handle.net/11858/00-001M-0000-0011-F98D-2
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spelling ftpubman:oai:pure.mpg.de:item_994145 2023-08-27T04:12:19+02:00 Estimating soil carbon turnover using radiocarbon data: a case study for European Russia Brovkin, V. Cherkinsky, A. Gorachkin, S. 2008-08 application/pdf http://hdl.handle.net/11858/00-001M-0000-0011-F98E-F http://hdl.handle.net/11858/00-001M-0000-0011-F98D-2 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2008.03.018 http://hdl.handle.net/11858/00-001M-0000-0011-F98E-F http://hdl.handle.net/11858/00-001M-0000-0011-F98D-2 Ecological Modelling info:eu-repo/semantics/article 2008 ftpubman https://doi.org/10.1016/j.ecolmodel.2008.03.018 2023-08-02T01:34:38Z Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km2 area of European Russia were estimated based on 14C data. The rates are corrected for bomb radiocarbon which strongly affects the topsoil 14C balance. The approach is applied for carbon stored in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year−1), intermediate for tundra soils (0.25% year−1), and low for grassland soils (0.02–0.08% year−1) with the exception of southern Chernozems (0.32% year−1). In the soil layer of 20–100 cm depth, the turnover rates were much lower for all soil types (0.01–0.06% year−1) except for peat bog soils of the southern taiga (0.14% year−1). Combined with a map of soil type distribution and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m2 year)), intermediate for tundra soils (33 gC/(m2 year)), and lowest for grassland soils (1–26 gC/(m2 year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from 14C measurements that most soil organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO2 in 21st century and beyond. Article in Journal/Newspaper taiga Tundra Max Planck Society: MPG.PuRe Ecological Modelling 216 2 178 187
institution Open Polar
collection Max Planck Society: MPG.PuRe
op_collection_id ftpubman
language English
description Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km2 area of European Russia were estimated based on 14C data. The rates are corrected for bomb radiocarbon which strongly affects the topsoil 14C balance. The approach is applied for carbon stored in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year−1), intermediate for tundra soils (0.25% year−1), and low for grassland soils (0.02–0.08% year−1) with the exception of southern Chernozems (0.32% year−1). In the soil layer of 20–100 cm depth, the turnover rates were much lower for all soil types (0.01–0.06% year−1) except for peat bog soils of the southern taiga (0.14% year−1). Combined with a map of soil type distribution and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m2 year)), intermediate for tundra soils (33 gC/(m2 year)), and lowest for grassland soils (1–26 gC/(m2 year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from 14C measurements that most soil organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO2 in 21st century and beyond.
format Article in Journal/Newspaper
author Brovkin, V.
Cherkinsky, A.
Gorachkin, S.
spellingShingle Brovkin, V.
Cherkinsky, A.
Gorachkin, S.
Estimating soil carbon turnover using radiocarbon data: a case study for European Russia
author_facet Brovkin, V.
Cherkinsky, A.
Gorachkin, S.
author_sort Brovkin, V.
title Estimating soil carbon turnover using radiocarbon data: a case study for European Russia
title_short Estimating soil carbon turnover using radiocarbon data: a case study for European Russia
title_full Estimating soil carbon turnover using radiocarbon data: a case study for European Russia
title_fullStr Estimating soil carbon turnover using radiocarbon data: a case study for European Russia
title_full_unstemmed Estimating soil carbon turnover using radiocarbon data: a case study for European Russia
title_sort estimating soil carbon turnover using radiocarbon data: a case study for european russia
publishDate 2008
url http://hdl.handle.net/11858/00-001M-0000-0011-F98E-F
http://hdl.handle.net/11858/00-001M-0000-0011-F98D-2
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op_source Ecological Modelling
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http://hdl.handle.net/11858/00-001M-0000-0011-F98D-2
op_doi https://doi.org/10.1016/j.ecolmodel.2008.03.018
container_title Ecological Modelling
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