Verbeeck Hans, Samson Roeland, Verdonck Frederik, Lemeur Raoul
Laboratory of Plant Ecology, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
Tree Physiol. 2006 Jun;26(6):807-17. doi: 10.1093/treephys/26.6.807.
The Monte Carlo technique can be used to propagate input variable uncertainty and parameter uncertainty through a model to determine output uncertainty. However, to carry out Monte Carlo simulations, the uncertainty distributions or the probability density functions (PDFs) of the model parameters and input variables must be known. This remains one of the bottlenecks in current uncertainty research in forest carbon flux modeling. Because forest carbon flux models involve many parameters, we questioned whether it is necessary to take into account all parameters in the uncertainty analysis. A sensitivity analysis can determine the parameters contributing most to the overall model output uncertainty. This paper illustrates the usefulness of the Monte Carlo simulation technique for ranking parameters for sensitivity and uncertainty in process-based forest flux models. The uncertainty of the output (net ecosystem exchange, NEE) of the FORUG model was estimated for the Hesse beech forest (1997). Based on the arbitrary uncertainty of ten key parameters, a standard deviation of 0.88 Mg C ha(-1) year(-1) NEE was found which is equal to 24% of the mean value of NEE. Sensitivity analysis showed that the overall output uncertainty of the FORUG model can largely be determined by accounting for the uncertainty of only a few key parameters. The results led to the identification of the key FORUG parameters and to the recommendation for a process-based description of the soil respiration process in the FORUG model.
蒙特卡罗技术可用于通过模型传播输入变量不确定性和参数不确定性，以确定输出不确定性。然而，要进行蒙特卡罗模拟，必须知道模型参数和输入变量的不确定性分布或概率密度函数（PDF）。这仍然是当前森林碳通量建模不确定性研究的瓶颈之一。由于森林碳通量模型涉及许多参数，我们质疑在不确定性分析中是否有必要考虑所有参数。敏感性分析可以确定对总体模型输出不确定性贡献最大的参数。本文阐述了蒙特卡罗模拟技术在基于过程的森林通量模型中对参数敏感性和不确定性进行排序的有用性。针对黑森山毛榉林（1997年）估算了FORUG模型输出（净生态系统交换量，NEE）的不确定性。基于十个关键参数的任意不确定性，发现NEE的标准偏差为0.88 Mg C ha(-1) 年(-1)，相当于NEE平均值的24%。敏感性分析表明，FORUG模型的总体输出不确定性在很大程度上可以通过仅考虑少数关键参数的不确定性来确定。研究结果确定了FORUG模型的关键参数，并为在FORUG模型中基于过程描述土壤呼吸过程提供了建议。
