Xue Hai-Lian, Tian Xiang-Lin, Wang Bin, Sun Shuai-Chao, Cao Tian-Jian
Northwest A＆F University, Yangling 712100, Shaanxi, China.
Academy of Agriculture and Forestry, Qinghai University, Xining 810016, China.
Ying Yong Sheng Tai Xue Bao. 2021 Jan;32(1):134-144. doi: 10.13287/j.1001-9332.202101.005.
Constructions of process or mechanistic models are limited by physiological parameters, due to difficulty in direct and precise measurement. Global sensitivity analysis could evaluate the response of model outputs to changes in physiological parameters, and provide information for improving model structure, data collection, and parameter calibration. Based on a process model CROBAS, 10 parameters related to tree structure of were selected to compare three widely used global sensitivity analysis methods (the Morris screening method, the variance-based Sobol indices, and the Extended Fourier Amplitude Sensitivity Test (EFAST)), with the objective function formulated by the Nash-Sutcliffe Efficiency (NSE) of tree height and biomass. The results showed that the sensitivity order of parameters slightly varied across different methods, which considerably changed with different objective functions. Both the Morris method and the EFAST method outperformed the Sobol method in terms of time consuming and convergence efficiency. All outputs were sensitive to the maximum rate of canopy photosynthesis per unit area, the specific leaf area, and the extinction coefficient. The light interception of tree canopy played a key role in the simulation of tree growth with CROBAS, suggesting that the module of photosynthetic carbon fixation took priority over any other modules for data collection and model validation during module calibration and tree growth simulation for CROBAS. The calculation and validation of foliage biomass module were crucial when applying carbon balance theory to biomass simulations. In conclusion, for the sensitivity analysis of a complex process-based model, the Morris method was suitable for qualitative studies, while the EFAST method was recommended for quantitative studies.
由于直接和精确测量存在困难，过程模型或机理模型的构建受到生理参数的限制。全局敏感性分析可以评估模型输出对生理参数变化的响应，并为改进模型结构、数据收集和参数校准提供信息。基于过程模型CROBAS，选择了10个与树结构相关的参数，以树高和生物量的纳什-萨特克利夫效率（NSE）作为目标函数，比较三种广泛使用的全局敏感性分析方法（莫里斯筛选法、基于方差的索博尔指数法和扩展傅里叶幅度敏感性测试（EFAST））。结果表明，不同方法的参数敏感性顺序略有不同，且随着目标函数的不同而有很大变化。在耗时和收敛效率方面，莫里斯方法和EFAST方法均优于索博尔方法。所有输出对单位面积冠层光合作用的最大速率、比叶面积和消光系数均敏感。树冠的光截获在CROBAS对树木生长的模拟中起关键作用，这表明在CROBAS的模块校准和树木生长模拟过程中，光合碳固定模块在数据收集和模型验证方面比任何其他模块都更具优先级。在将碳平衡理论应用于生物量模拟时，叶生物量模块的计算和验证至关重要。总之，对于基于复杂过程的模型的敏感性分析，莫里斯方法适用于定性研究，而EFAST方法推荐用于定量研究。
