Jia Wen Xiao, Liu Min, She Qian Nan, Yin Cai, Zhu Xi Yang, Xiang Wei Ning
Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China.
School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
Ying Yong Sheng Tai Xue Bao. 2016 Apr 22;27(4):1095-1102. doi: 10.13287/j.1001-9332.201604.010.
Gross primary productivity (GPP) plays an important role in global carbon cycle. Vegetation maximum light use efficiency (Δ) is the key parameter for GPP simulation of terrestrial ecosystem. Based on the vegetation photosynthesis model (VPM) and the eddy covariance flux data at 40 stations from FLUXNET (179 site-years of data), we identified the key model parameters influencing the simulation of GPP with VPM through one-at-a-time (OAT) method. The cross validation method was employed to optimize the key model parameters and evaluate the model perfor-mance for global forest ecosystems. The results showed that the prediction of GPP was mostly affec-ted by Δ, maximum temperature for photosynthesis (T), and optimum temperature for photosynthesis (T). There were distinguishable differences for the key optimized parameters among different forest ecosystems. The optimized Δ ranged from 0.05 to 0.08 μmol CO·μmol PAR (evergreen broad-leaved forest>evergreen coniferous forest>mixed forest>deciduous broad-leaved forest). The optimized T ranged from 38 to 48 ℃,while T ranged from 18 to 22 ℃. With the optimized key parameters based on ecosystem types, the VPM was able to simulate the seasonal and inter-annual variations of GPP in four forest ecosystems.
总初级生产力（GPP）在全球碳循环中起着重要作用。植被最大光能利用效率（Δ）是陆地生态系统GPP模拟的关键参数。基于植被光合作用模型（VPM）和FLUXNET中40个站点的涡度相关通量数据（179个站点年的数据），我们通过逐一改变法（OAT）确定了影响VPM模拟GPP的关键模型参数。采用交叉验证方法优化关键模型参数，并评估全球森林生态系统的模型性能。结果表明，GPP的预测主要受Δ、光合作用最高温度（T）和光合作用最适温度（T）的影响。不同森林生态系统的关键优化参数存在显著差异。优化后的Δ范围为0.05至0.08 μmol CO·μmol PAR（常绿阔叶林>常绿针叶林>混交林>落叶阔叶林）。优化后的T范围为38至48℃，而T范围为18至22℃。基于生态系统类型的优化关键参数，VPM能够模拟四种森林生态系统中GPP的季节和年际变化。
