基于过程模型CROBAS的全局灵敏度分析方法比较

过程机理模型在开发过程中常受限于生理学参数无法直接或准确测量.全局灵敏度分析可以评估模型预测结果对于生理学参数变化的响应,为模型结构改进、数据收集和参数校准提供参考.本研究基于过程模型CROBAS,以华山松为例,选取模型中描述树木结构关系的10个参数,以树高和各器官生物量的Nash-Sutcliffe效率(NSE)为目...

Comparison of global sensitivity analysis techniques based on a process-based model CROBAS

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 Pinus armandii 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.