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<p>Although <i>Quercus mongolica</i> is a widely distributed, economically and ecologically important deciduous tree in northern China, models to accurately predict stand growth at a regional scale are limited. The physiological process model (3-PG) has the potential to predict stand growth dynamics under varying site conditions and climate change scenarios. Here, we used field inventory, tree ring sampling, and Bayesian calibration to parameterize a model for <i>Q. mongolica</i>. Stand volume and productivity were then predicted under present conditions and three future climate scenarios (RCP26, RCP45 and RCP85). Our results demonstrated that after Bayesian calibration, the posterior ranges of the sensitivity parameters aphaCx, wSx1000 and pRn accounted for 34%, 45% and 65%, respectively, of their prior range. Calibration and validation results revealed a strong correlation between predicted and measured values (<i>R</i><sup>2</sup> &gt; 0.87, <i>P</i> &lt; 0.01), with &lt; 20% bias for all growth indicators. Stand volume was projected to increase by 145% and productivity by 80% by the year 2100 under the RCP85 scenario, although these projections may vary across regions. The present study developed a tailored set of 3-PG model parameters for <i>Q. mongolica</i>, based on a comprehensive range of climate conditions, stand structure, and age classes. These parameters offer a scientific basis to accurately predict growth of other monospecific oak or mixed-species stands.</p>

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Parameterization of the 3-PG model for Quercus mongolica by using tree-ring data and Bayesian calibration

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Wen Nie,
  • Qi Wang,
  • Ruizhi Huang,
  • Shaowei Yang,
  • Yipei Zhao,
  • Jingyi Sun,
  • Xiangfen Cheng,
  • Zuyuan Wang,
  • Wenfa Xiao,
  • Jianfeng Liu

摘要

Although Quercus mongolica is a widely distributed, economically and ecologically important deciduous tree in northern China, models to accurately predict stand growth at a regional scale are limited. The physiological process model (3-PG) has the potential to predict stand growth dynamics under varying site conditions and climate change scenarios. Here, we used field inventory, tree ring sampling, and Bayesian calibration to parameterize a model for Q. mongolica. Stand volume and productivity were then predicted under present conditions and three future climate scenarios (RCP26, RCP45 and RCP85). Our results demonstrated that after Bayesian calibration, the posterior ranges of the sensitivity parameters aphaCx, wSx1000 and pRn accounted for 34%, 45% and 65%, respectively, of their prior range. Calibration and validation results revealed a strong correlation between predicted and measured values (R2 > 0.87, P < 0.01), with < 20% bias for all growth indicators. Stand volume was projected to increase by 145% and productivity by 80% by the year 2100 under the RCP85 scenario, although these projections may vary across regions. The present study developed a tailored set of 3-PG model parameters for Q. mongolica, based on a comprehensive range of climate conditions, stand structure, and age classes. These parameters offer a scientific basis to accurately predict growth of other monospecific oak or mixed-species stands.