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来源类型Article
规范类型其他
DOI10.5194/bg-9-5323-2012
An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods.
Dolman AJ; Shvidenko A; Schepaschenko D; Ciais P; Tchebakova N; Chen T; van der Molen MK; Belelli Marchesini L
发表日期2012
出处Biogeosciences 9 (12): 5323-5340
出版年2012
语种英语
摘要We determine the carbon balance of Russia, including Ukraine, Belarus and Kazakhstan using inventory based, eddy covariance, Dynamic Global Vegetation Models (DGVM), and inversion methods. Our current best estimate of the net biosphere to atmosphere flux is -0.66 Pg C yr-1. This sink is primarily caused by forests that using two independent methods are estimated to take up -0.69 Pg C yr-1. Using inverse models yields an average net biosphere to atmosphere flux of the same value with a interannual variability of 35%. The total estimated biosphere to atmosphere flux from eddy covariance observations over a limited number of sites amounts to -1 Pg C yr-1. Fires emit 137 to 121 Tg C yr-1 using two different methods. The interannual variability of fire emissions is large, up to a factor 0.5 to 3. Smaller fluxes to the ocean and inland lakes, trade are also accounted for. Our best estimate for the Russian net biosphere to atmosphere flux then amounts to -659 Tg C yr-1 as the average of the inverse models of -653 Tg C yr-1, bottom up -563 Tg C yr-1 and the independent landscape approach of -761 Tg C yr-1. These three methods agree well within their error bounds, so there is good consistency between bottom up and top down methods. The best estimate of the net land to atmosphere flux, including the fossil fuel emissions is -145 to -73 Tg C yr-1.
主题Ecosystems Services and Management (ESM)
URLhttp://pure.iiasa.ac.at/id/eprint/9940/
来源智库International Institute for Applied Systems Analysis (Austria)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/129381
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Dolman AJ,Shvidenko A,Schepaschenko D,et al. An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods.. 2012.
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