中亚热带人工针叶林生态系统碳通量拆分差异分析

涡度通量观测可直接获取陆地生态系统与大气之间CO₂净交换量(NEE),但深入认识碳循环过程和校验生态系统模型需要不同时间尺度总初级生产力(GPP)和生态系统呼吸(Re)等碳通量数据。利用中国陆地生态系统通量观测与研究网络(ChinaFLUX)中亚热带人工针叶林生态系统2003-2009年的涡度通量和气象观测数据,分析了两种NEE拆分方法对不同时间尺度GPP和Re评估的影响,结果表明:(1)两种拆分方法得到的生态系统碳通量组分(GPP和Re)的季节动态变化一致,都在生长季7、8月份达到峰值;(2)非线性回归模型拆分得到的全年Re和GPP相较于光响应曲线模型分别高出2%-28.6%和1.6%-23%,最大高出317.6 gC·m^-2·a^-1(2006年),逐月最大差值主要发生在8、9月份;(3)不同时间尺度上,两种方法拆分得到的GPP 和Re之间差值的环境响应因子不同。在广泛采用非线性回归模型进行拆分时,如果当月光合有效辐射接近到905 mol·m^-2·月^-1,月平均空气饱和水汽压差接近1.18 kPa时,需要考虑使用光响应曲线模型拆分该月通量,结合两种拆分方法以减小全年的误差。

An analysis of carbon flux partition differences of a mid-subtropical planted coniferous forest in southeastern China

Net ecosystem exchange (NEE) between terrestrial ecosystem and atmosphere can be directly observed by eddy covariance flux observation system, but we need to get accurately ecosystem gross primary productivity (GPP) and respiration (Re) under different time scales in order to get insights into carbon cycle process. This paper analyzed the eddy carbon flux and meteorology measurement data of a mid-subtropical planted coniferous forest at Qianyanzhou station from 2003 to 2009, and explored the impacts of two different NEE partition methods on estimation of ecosystem GPP and Re under different time scales. Results indicated that ecosystem Re and GPP estimated by different eddy flux partition methods showed similar seasonal dynamics, both of which reached the peaks in July or August of growing season. However, the annual Re and GPP estimated by nonlinear regression model were 2%-28.6% and 1.6%-23% higher than those estimated by light response curve model, respectively. The maximum annual ecosystem respiration difference between two methods existed in 2006 (317.6 gC·m^-2·a^-1), and the maximum monthly ecosystem respiration difference mostly appeared in August or September. Also, we found that environmental factors significantly affect differences between two derived Re (or GPP) with various time scales. For example, the vapor pressure deficit and photosynthetic active radiation were found to explain 63% and 60% of the ecosystem respiration difference between two methods at daily time scale, respectively. Moreover, precipitation, vapor pressure deficit and photosynthetic active radiation could explain 48%, 85% and 89% of the ecosystem respiration difference between two methods at monthly time scale. Third, 78% of the ecosystem respiration difference between two methods could be explained by the photosynthetic active radiation at yearly time scale. It means that the photosynthetic active radiation could explain the most of Re difference between two methods under three time scales. In spite of the wide application of the nonlinear regression model, it was necessary to allow for the light response curve model to partition the carbon flux of that month whose monthly PAR is about 905mol·m^-2·mon^-1 and the vapor pressure deficit is around 1.18 KPa as a reference, compared with those partitioned by the nonlinear regression model. Furthermore, the research got access to improvements on the partition results of ecosystem carbon flux and reduced the partition uncertainty.