2009
DOI: 10.5194/bg-6-2879-2009
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Environmental influences on carbon dioxide fluxes over three grassland ecosystems in China

Abstract: Abstract. This study compared carbon dioxide (CO 2 ) fluxes over three grassland ecosystems in China, including a temperate semiarid steppe in Inner Mongolia (NMG), an alpine shrub-meadow in Qinghai (HB), and an alpine meadowsteppe in Tibet (DX). Measurements were made in 2004 and 2005 using the eddy covariance technique. Objectives were to document the seasonality of the net ecosystem exchange of CO 2 (NEE) and its components, gross ecosystem photosynthesis (GEP), and ecosystem respiration (R eco ), and to ex… Show more

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Cited by 125 publications
(93 citation statements)
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“…ChinaFLUX community modified several process-based models and remote sensing models, such as CEVSA (Cao et al, 2005;Gu et al, 2010), In-TEC , EALCO (Mi et al, 2007;Mi et al, 2009), and BEPS Ju et al, 2010), and VPM Wu et al, 2008). Meanwhile, many new models have been developed including AVIM2 (Ji et al, 2008;, CEVSA2 Gu et al, 2015), the evapo-transpiration model (Hu et al, 2013;Ren et al, 2005), a MODIS-based Photosynthetic Capacity Model , a remote sensing model for respiration ReRSM , and the statistical model GSM (Zheng et al, 2009;Zhu et al, 2014a). In addition, a Model-Data Fusion System (MDFS) has been developed based on a series of algorithms including the Markov-Chain Monte Carlo method, the Monte Carlo Simulated Annealing method, and the sobol' method Ren et al, 2012) to quantify and minimize the model uncertainty.…”
Section: The Development Of Ecosystem Carbon Nitrogen and Water Modementioning
confidence: 99%
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“…ChinaFLUX community modified several process-based models and remote sensing models, such as CEVSA (Cao et al, 2005;Gu et al, 2010), In-TEC , EALCO (Mi et al, 2007;Mi et al, 2009), and BEPS Ju et al, 2010), and VPM Wu et al, 2008). Meanwhile, many new models have been developed including AVIM2 (Ji et al, 2008;, CEVSA2 Gu et al, 2015), the evapo-transpiration model (Hu et al, 2013;Ren et al, 2005), a MODIS-based Photosynthetic Capacity Model , a remote sensing model for respiration ReRSM , and the statistical model GSM (Zheng et al, 2009;Zhu et al, 2014a). In addition, a Model-Data Fusion System (MDFS) has been developed based on a series of algorithms including the Markov-Chain Monte Carlo method, the Monte Carlo Simulated Annealing method, and the sobol' method Ren et al, 2012) to quantify and minimize the model uncertainty.…”
Section: The Development Of Ecosystem Carbon Nitrogen and Water Modementioning
confidence: 99%
“…Temperate steppes and alpine meadows served as weak carbon sink (24±83, 45±59 g C m -2 yr -1 ). The grasslands in northern China probably served as a weak carbon source after disturbances, which differed substantially among ecosystems and years (Shi et al, 2006;Li et al, 2006;Fu et al, 2006Fu et al, , 2009Yu et al, 2013b).…”
Section: Evaluation Of Ecosystem Carbon Sink/source Based On Flux Meamentioning
confidence: 99%
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“…Figure 10. Relationships between the annual GPP and precipitation (PPT) for this Lijiang site, the Haibei site (Kato et al, 2006;Fu et al, 2009), the Damxung site (Fu et al, 2009) and the semi-arid grassland sites (Fu et al, 2009;Du and Liu, 2013;Yang and Zhou, 2013).…”
Section: Control Of the Interannual Variation In The Co 2 Exchangementioning
confidence: 99%
“…Several studies of CO 2 exchange have been carried out on the Qinghai-Tibetan plateau, where the mean annual air temperature (T a ) is approximately 0 • C (Gu et al, 2003;Kato et al, 2006;Shi et al, 2006;Zhao et al, 2006). The daily CO 2 fluxes of the alpine meadow steppe in Damxung, Tibet were shown to be jointly affected by T a and soil moisture (Fu et al, 2009), while the daily CO 2 fluxes of an alpine shrubland at Haibei, Qinghai were found to be sensitive to T a . On an annual scale, the measurements at the Haibei alpine meadow revealed that the annual CO 2 uptake was increased by the earlier onset of the growing season, which was caused by higher T a (Kato et al, 2006).…”
Section: Introductionmentioning
confidence: 99%