中国陆地生态系统通量观测站点空间代表性

涡度相关技术是测定大气与陆地生态系统之间CO₂交换、水分和能量通量最直接的方法，可用于研究土壤、植被与大气间的CO₂交换及其调控机制。收集了11个影响净碳交换量的主要变量信息，包括气象因素、土壤因素和地形因素的非生物因子、实际植被状态以及植被生产力，采用多元地理变量空间聚类分析方法，绘制出不同聚类数（25、50、75、85、100、150和200类）的通量生态区。结合中国现有通量观测站点的空间分布格局，与新生成的通量生态区和已有的自然地理区划进行对比分析，发现由于中国地形复杂，生态系统类型多样，现有85个涡度相关通量观测站点仅能刻画部分中国生态系统类型的净碳交换量时空特征，通量生态区划分为100-150类比较合适。考虑到涡度相关通量观测运行成本，通量站点可增加至150个，从而使得优化后的通量观测网络能够代表中国主要类型的生态系统，并且有利于通量观测数据与遥感资料的有效结合，提高碳水通量观测从站点扩展到区域尺度的精度，从而更好地检验过程机理模型的模拟结果。

Assessing the spatial representativeness of eddy covariance flux observation stations of terrestrial ecosystems in China

Eddy covariance (EC) technique is the most direct way to measure the exchanges of carbon dioxide (CO₂),water vapor,and energy flux between terrestrial ecosystems and atmosphere, which can be used to explore CO₂ exchanges between terrestrial ecosystems and atmosphere and its controlling mechanism. In this paper,we used the multivariate geographic clustering approach to generate flux-ecoregions with different clustering number(25,50,75,85,100,150,200 clusters) in China based on 11 variables affecting carbon flux,including meteorological factor,soil factor,abiotic factor of topography, actual vegetation (Leaf area index (LAI) and Enhanced vegetation index (EVI)) and vegetation productivity variables (Gross primary productivity,GPP). Based on the spatial distribution pattern of the existing flux observation stations in China and the comparative analysis between newly generated flux ecoregions and the existing geographical regionalization, the results showed that the existing 85 eddy covariance flux observation stations in China cannot reflect the spatial and temporal characteristics of carbon flux of all ecosystems because of the country's complex topography and the diverse ecosystem types. It is also recommended that the number of the flux-ecoregions be 100-150. Considering the building and operating costs of the flux towers, the number of eddy flux tower stations can be added to 150 sites. Thus,the optimized flux network is supposed to represent major ecosystems and facilitate the integration of flux and remote sensing data,consequently, improve the accuracy of upscaling CO₂ and water vapor flux observations from tower to regional scales to better exam the simulation result of the process based ecosystem model.