Regression models for the estimation of carbon exchange in boreal forests
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Based on the measurements of the carbon dioxide fluxes at a few sites of the global FLUXNET network, we developed linear regression models for the estimation of the carbon budget of boreal forests. The model estimates satisfactorily agree with the measurement data obtained in the boreal forests of Canada (Manitoba, Thompson) and Russia (Krasnoyarsk krai, Zotino). The correlation coefficient between the calculations and measurements for a coniferous forests exceeds 0.9, with the annually mean model error in the budget estimate, as compared to the experimental results, not exceeding 50 gC/m2/yr. Using satellite and ground-based meteorological data, we calculated the monthly average carbon budget of the coniferous forests of Krasnoyarsk krai in 2001. Based on the satellite classification of the vegetation cover, we plotted monthly and annually average maps of the carbon budget that reflect the spatiotemporal distribution of the CO2 uptake and emission rates. It is shown that the coniferous forests of Krasnoyarsk krai are mostly a carbon sink, uptaking as much as 300 gC/m2 throughout the growing season.
KeywordsPhotosynthetically Active Radiation Coniferous Forest Boreal Forest Carbon Budget Cloud Fraction
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- 2.E. A. Vaganov, E. F. Vedrova, S. V. Verkhovets, S. P. Efremov, T. T. Efremova, V. B. Kruglov, A. A. Onuchin, A. I. Sukhinin, and O. B. Shibistova, “Forests and Swamps in Siberia in Global Carbon Cycle,” Sib. Ekol. Zh 12(4), 631 (2005).Google Scholar
- 10.TCOS Siberia, 2003: Proposal EVK2-2002-00555, http://www.bgc-jena.mpg.de/public/carboeur/webTCOS/
- 11.A. Belward and T. Loveland, “The DIS 1km Land Cover Data Set, 1996, GLOBAL CHANGE,” The IGBP Newslett. No. 27 (Sep. 1996), http://wwwsurf.larc.nasa.gov/surf/pages/explan.html.
- 14.S. C. Wofsy and A. Dunn, “BOREAS Follow-On FLX-01 NSA-OBS Derived Data — NEE, GEE, and Respiration, 2001,” Data set. (Oak Ridge Nat. Labor. Distributed Active Archive Center, Oak Ridge, Tennessee, USA), http://daac.ornl.gov
- 17.Leemans and Cramer Climatic Database (Inst. of Climate Impact Research, Potsdam, 1996), Personal communication.Google Scholar
- 18.T. B. Zhuravleva and K. M. Firsov, “Algorithms of Calculations of Spectral Fluxes of Solar Radiation in the Cloudy and Clear-sky Atmosphere,” Atmos. Ocean. Opt. 17, 903 (2004).Google Scholar
- 19.T. B. Zhuravleva, A. N. Rublev, T. A. Udalova, and T. Yu. Chesnokova, “On Calculation of Photosynthetically Active Radiation in Estimation of Carbon Balance Parameters of Surface Ecosystems,” Atmos. Ocean. Opt. 19(1), 64 (2006).Google Scholar
- 20.Modis / Terra Atmosphere Monthly Global Product, http://g0dup05u.ecs.nasa.gov/Giovanni//modis.MOD08-M3. shtml
- 21.Russion Soil Temperature Stations, ftp://sidads.colorado.edu/pub/datasets/arcss/data/arcss078/
- 22.V. Stolbovoi and I. Savin, Maps of Soil Characteristics, V. Stolbovoi and I. McCallum, (2002), CD-ROM Land Resources of Russia, Laxenburg, Austria, International Institute for Applied Systems Analysis and the Russian Academy of Science, CD-ROM, Distributed by the National Snow and Ice Data Center / World Data Center for Glaciology, Boulder.Google Scholar
- 23.Global Precipitation Climatology Project (GPCP), http://cics.umd.edu/?yin/GPCP/main.html
- 24.Global Land Cover 2000 (GLC2000) Maps, http://www-gvm.jrc.it/glc2000/input-data.htm