Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method
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 we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/11/12/1369 |
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| author | Chenjian Liu Xiaoman Zheng Yin Ren |
| author_facet | Chenjian Liu Xiaoman Zheng Yin Ren |
| author_sort | Chenjian Liu |
| collection | DOAJ |
| description | 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 <i>Chinese fir</i> 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. |
| first_indexed | 2024-03-10T13:53:12Z |
| format | Article |
| id | doaj.art-f7f1c9ac0b094f10883b0500fa8b0ca9 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T13:53:12Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-f7f1c9ac0b094f10883b0500fa8b0ca92023-11-21T01:55:21ZengMDPI AGForests1999-49072020-12-011112136910.3390/f11121369Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian MethodChenjian Liu0Xiaoman Zheng1Yin Ren2Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaKey Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaKey Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaSensitivity 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 <i>Chinese fir</i> 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.https://www.mdpi.com/1999-4907/11/12/1369Chinese firMarkov chain Monte Carlo (MCMC)parameter estimationstand models |
| spellingShingle | Chenjian Liu Xiaoman Zheng Yin Ren Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method Forests Chinese fir Markov chain Monte Carlo (MCMC) parameter estimation stand models |
| title | Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method |
| title_full | Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method |
| title_fullStr | Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method |
| title_full_unstemmed | Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method |
| title_short | Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method |
| title_sort | parameter optimization of the 3pg model based on sensitivity analysis and a bayesian method |
| topic | Chinese fir Markov chain Monte Carlo (MCMC) parameter estimation stand models |
| url | https://www.mdpi.com/1999-4907/11/12/1369 |
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