RESUMO
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.
Assuntos
Fotossíntese , Pinus , Biomassa , Carbono , Folhas de PlantaRESUMO
The complexity and uncertainty of forest regeneration is crucial for predicting forest ecosystem dynamics. A natural regeneration model of pine-oak forests in Qinling Mountains was constructed with competition, climate and topography factors using Bayesian statistics and global sensitivity analysis (GSA). The alternative models were based on Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models. According to the uncertainty of model parameter transfer, the analysis results were quantified, and the dominant factors of small probability events affecting forest regeneration were explained. The results showed that the ZINB model was the best one in the simulation of Pinus tabuliformis and Quercus aliena var. acuteserrata. Stand basal area, light interception, slope location and minimum temperature during growing season were the most critical factors affecting natural regeneration of P. tabuliformis, while stand basal area, cosine of aspect interacted with the natural logarithm of elevation, annual mean temperature, and precipitation of the warmest quarter were the most critical factors for Q. aliena var. acuteserrata. The contributions of various factors to the predictive uncertainty were: competition factor (25%) < climate factor (29%) < topography factor (46%) for the simulation of P. tabuliformis regeneration, and climate factor (12%) < competition factor (24%) < topography factor (64%) for the simulation of Q. aliena var. acuteserrata regeneration. The natural regeneration quantity of P. tabuliformis was positively correlated with mean annual temperature and minimum precipitation during growing season, and negatively correlated with the mean temperature in the driest quarter. The natural regeneration quantity of Q. aliena var. acuteserrata was positively correlated with mean annual temperature, minimum precipitation during growing season, precipitation of the warmest quarter, and negatively correlated with mean temperature of the driest quarter. The ZINB model based on Bayesian methods could effectively quantify the major factors driving forest regeneration and interpret the uncertainty propagated from parameters, which was useful for predicting forest regeneration.
