Abstract
Sensitivity analysis and parameter optimization of stand models can improve their efficiency and accuracy, and increase their applicability. In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of the Physiological Principles in Predicting Growth (3PG) model were performed by combining a sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian posterior estimation theory. Additionally, a nine-year observational dataset of Chinese fir trees felled in the Shunchang Forest Farm, Nanping, was used to analyze, screen, and optimize the 63 model parameters of the 3PG model. The results showed the following: (1) The parameters that are most sensitive to stand stocking and diameter at breast height (DBH) are nWs(power in stem mass vs. diameter relationship), aWs(constant in stem mass vs. diameter relationship), alphaCx(maximum canopy quantum efficiency), k(extinction coefficient for PAR absorption by canopy), pRx(maximum fraction of NPP to roots), pRn(minimum fraction of NPP to roots), and CoeffCond(defines stomatal response to VPD); (2) MCMC can be used to optimize the parameters of the 3PG model, in which the posterior probability distributions of nWs, aWs, alphaCx, pRx, pRn, and CoeffCond conform to approximately normal or skewed distributions, and the peak value is prominent; and (3) compared with the accuracy before sensitivity analysis and a Bayesian method, the biomass simulation accuracy of the stand model was increased by 13.92%, and all indicators show that the accuracy of the improved model is superior. This method can be used to calibrate the parameters and analyze the uncertainty of multi-parameter complex stand growth models, which are important for the improvement of parameter estimation and simulation accuracy.
Highlights
Experimental observations and model simulations are common methods for estimating the pattern and variability of the global carbon cycle in order to understand the key processes and control mechanisms of this cycle
The results show that the root-mean-square error (RMSE)((Root Mean Square Error) of the stem values with the value and posterior value in the model simulation are 1.24 and 0.98, respectively; initial value and posterior value in the model simulation are 1.24 and 0.98, the RMSEs of the height values are 0.34 and 0.32, respectively; and the RMSEs of the diameter at breast height (DBH) values are respectively; the RMSEs of the height values are 0.34 and 0.32, respectively; and the RMSEs of
The constant in the stem mass versus diameter relationship, the maximum fraction of NPP to roots, and the minimum fraction of NPP to roots, which are all related to the allocation relationship and proportion of biomass, as well as the maximum canopy quantum efficiency, which defines stomatal response to VPD (CoeffCond), and the extinction coefficient for PAR absorption by canopy (k)
Summary
Experimental observations and model simulations are common methods for estimating the pattern and variability of the global carbon cycle in order to understand the key processes and control mechanisms of this cycle. To reduce the uncertainties of the parameters of ecological models and improve the ability of such models to simulate and predict ecosystem processes and changes, researchers have successively carried out a series of parameter estimation studies for terrestrial ecosystem models focusing on sample plots at regional and global scales [1,2,3,4,5,6]. Determining how to obtain the key parameters of forest growth and estimate forest biomass have become hot research topics. The accurate acquisition of model parameters is a prerequisite for model application and the improvement of model prediction accuracy [7,8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
