基于中国陆地生态系统通量观测研究网络(ChinaFLUX)4个站点(2个森林站和2个草地站)的涡度相关通量观测资料,分析了CO2通量数据处理过程中异常值剔除参数设置、夜间摩擦风速(u*)临界值(u*c)确定及数据插补模型选择对CO2通量组分估算的影响.结果表明:3种数据处理方法均对净生态系统碳交换量(NEE)年总量估算有显著影响,其中u*c确定是影响NEE估算的重要因子;异常值剔除、u*c确定及数据插补模型选择导致NEE年总量估算偏差分别为0.62～21.31 g C.m-2.a-1(0.84%～65.31%)、4.06～30.28 g C.m-2.a-1(3.76%～21.58%)和0.69～27.73 g C.m-2.a-1(0.23%～55.62%),草地生态系统NEE估算对数据处理方法参数设置更敏感;数据处理方法不确定性引起的总生态系统碳交换量和生态系统呼吸年总量估算相对偏差分别为3.88%～11.41%和6.45%～24.91%.
光能利用效率(light use efficiency,LUE)是指初级生产力与植被冠层所吸收的光合有效辐射(absorbed pho-tosynthetically active radiation,APAR)之比,它反映了植被利用光能的能力。定量化生产力的时空变化是定量化全球碳循环的重要研究内容,而LUE作为光能生产力模型中的一个重要参数,是定量化生产力时空变化的基础。因此,定量化全球植被的LUE是定量化全球碳循环的重要组成部分。基于MODIS光能利用效率算法,本研究模拟了2004-2005年藏北高寒草甸生态系统的光能利用效率(LUEMODIS),并用观测的光能利用效率(LUEEC)对模型进行了验证。在MODIS算法中,日最低气温(Tamin)和饱和水汽压亏缺(VPD)分别被用来计算温度胁迫因子(Tscalar)和水分胁迫因子(Wscalar)。相关分析和多重逐步回归分析结果表明,相对于Wscalar,Tscalar更能够解释观测的LUE的季节变化。2004和2005年的模拟值分别高估了约14.97%和16.57%的观测值,但配对T检验显示模拟值和观测值差异不显著,即基于MODIS的LUE算法在模拟藏北高寒草甸LUE方面具有较高的精度。相关分析表明,观测的LUE与Tamin的相关性好于观测的LUE与平均气温的相关性,这表明在反应藏北高寒草甸生态系统LUE的季节变异方面,Tamin优于平均气温。总之,基于MODIS算法的LUE模型能够比较准确地定量化藏北高寒草甸生态系统的LUE。
Drought may impact the net ecosystem exchange of CO2 （NEE） between grassland ecosystems and the atmosphere during growth seasons. Here, carbon dioxide exchange and controlling factors in alpine grassland under drought stress in the hinterland of Tibetan Plateau （Damxung, Tibet, China） were investigated. Data were obtained using the covariance eddy technique in 2009. Severe drought stress appeared in the early growing season （May to early July） and September. Drought conditions during the early growing season limited grass production and the green leaf area index （GLAD increased slowly, with an obvious decline in June. When encountering severe water stress, diurnal patterns of NEE in the growth season altered with a peak carbon release around 16：00 h or a second carbon uptake period before sunset. NEE variations in daytime related most closely with O other than PAR when daily averaged @〈0.1 m3 m 3. Seasonal patterns of gross primary production （GPP） and NEE were also influenced by drought： the maximum and minimum of daily-integrated NEE were 0.9 g C m-2 d-1 on 3 July 2009, and -1.3 g C m-2 d-1 on 12 August 2009 with a GPP peak （-2.3 g C m-2 d-1） on the same day, respectively. Monthly NEE from May to July remained as carbon release and increased gradually; peak values of monthly NEE and GPP both appeared in August, but that of ecosystem respiration （R^co） was reached in July. Annual NEE, GPP and Reco of the alpine grassland ecosystem were 52.4, -158.1 and 210.5 g C m-2, respectively. Therefore, the grassland was a moderate source of COs to the atmosphere in this dry year. Interannual variation in NEE was likely related to different water conditions in the growing season. The three greatest contributors to seasonal variation in NEE, GPP and R^co respectively were GLAI〉Ta〉O, GLAI〉O〉PPT, and Ta〉GLAI〉PAR. Seasonality of GLAI explained 60.7% and 76.1% of seasonal variation in NEE and GPP, respectively. GPP or NEE was more sensitive than Reco to variation in GLAI, and ecosystem
