Aber John D, Reich Peter B, Goulden Michael L
Complex Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, 03824, Durham, NH, USA.
Department of Forest Resources, University of Minnesota, 55455, St. Paul, MN, USA.
Oecologia. 1996 Apr;106(2):257-265. doi: 10.1007/BF00328606.
Over the last 4 years, two data sets have emerged which allow increased accuracy and resolution in the definition and validation of a photosynthesis model for whole forest canopies. The first is a greatly expanded set of data on the nitrogen-photosynthesis relationship for temperate and tropical woody species. The second is a unique set of long-term (4 year) daily carbon balance measurements at the Harvard Forest, Petersham, Massachusetts, collected by the eddy-correlation technique. A model (PhET-Day) is presented which is derived directly from, and validated against, these data sets. The PnET-Day model uses foliar nitrogen concentration to calculate maximum instantaneous rates of gross and net photosynthesis which are then reduced for suboptimal temperature, photosynthetically active radiation (PAR), and vapor pressure deficit (VPD). Predicted daily gross photosynthesis is closely related to gross carbon exchange at the Harvard Forest as determined by eddy-correlation measurements. Predictions made by the full canopy model were significantly better than those produced by a multiple linear regression model. Sensitivity analyses for this model for a deciduous broad-leaved forest showed results to be much more sensitive to parameters related to maximum leaf-level photosynthetic rate (A ) than to those related to light, temperature, VPD or total foliar mass. Aggregation analyses suggest that using monthly mean climatic data to drive the canopy model will give results similar to those achieved by averaging daily eddy correlation measurements of gross carbon exchange (GCE).
在过去4年中，出现了两组数据集，它们提高了整个森林冠层光合作用模型定义和验证的准确性与分辨率。第一组是关于温带和热带木本植物氮素与光合作用关系的大幅扩充数据集。第二组是通过涡度相关技术在马萨诸塞州彼得舍姆的哈佛森林收集的一套独特的长期（4年）每日碳平衡测量数据。本文提出了一个直接从这些数据集推导并经其验证的模型（PhET-Day）。PnET-Day模型利用叶片氮浓度来计算总光合作用和净光合作用的最大瞬时速率，然后针对次优温度、光合有效辐射（PAR）和水汽压差（VPD）进行修正。预测的每日总光合作用与哈佛森林通过涡度相关测量确定的总碳交换密切相关。全冠层模型的预测结果明显优于多元线性回归模型。对一个落叶阔叶林该模型的敏感性分析表明，结果对与最大叶片水平光合速率（A）相关的参数比对与光照、温度、VPD或总叶质量相关的参数更为敏感。汇总分析表明，使用月平均气候数据驱动冠层模型将得到与通过平均每日总碳交换（GCE）涡度相关测量结果类似的结果。
