An empirical model of carbon ¯uxes in Russian tundra
This study presents an empirical model based on a GIS approach, which was constructed to estimate the large-scale carbon ¯uxes over the entire Russian tundra zone. The model has four main blocks: (i) the computer map of tundra landscapes; (ii) data base of long-term weather records; (iii) the submod...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.514.607 http://www.sevin.ru/fundecology/authors/zamolpub/empirical_model_of_carbon_fluxes.pdf |
| Summary: | This study presents an empirical model based on a GIS approach, which was constructed to estimate the large-scale carbon ¯uxes over the entire Russian tundra zone. The model has four main blocks: (i) the computer map of tundra landscapes; (ii) data base of long-term weather records; (iii) the submodel of phytomass seasonal dynamics; and (iv) the submodel of carbon ¯uxes. The model uses exclusively original in situ diurnal CO2 ¯ux chamber measurements (423 sample plots) conducted during six ®eld seasons (1993±98). The research sites represent the main tundra biome landscapes (arctic, typical, south shrub and mountain tundras) in the latitudinal diapason of 65±74°N and longitudinal pro®le of 63°E±172°W. The greatest possible diversity of major ecosystem types within the different landscapes was investigated. The majority of the phytomass data used was obtained from the same sample plots. The submodel of carbon ¯uxes has two dependent [GPP, Gross Respiration (GR)] and several input variables (air temperature, PAR, aboveground phytomass components). The model demonstrates a good correspondence with other independent regional and biome estimates and carbon ¯ux seasonal patterns. The annual GPP of Russian tundra zone for the area of 235 ¥ 106 ha was estimated as ±485.8 K 34.6 ¥ 106 tC, GR as +474.2 K 35.0 ¥ 106 tC, and NF as ±11.6 K 40.8 ¥ 106 tC, which possibly corresponds to an equilibrium state of carbon balance during the climatic period studied (the ®rst half of the 20th century). The results advocate that simple regression-based models are useful for extrapolating carbon ¯uxes from small to large spatial scales. |
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