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林业科学 ›› 2021, Vol. 57 ›› Issue (9): 21-33.doi: 10.11707/j.1001-7488.20210903

• 论文与研究报告 • 上一篇    下一篇

利用经验-过程混合建模方法优化华山松过程模型的参数

薛海连1,田相林2,曹田健3,*   

  1. 1. 西北农林科技大学理学院 杨凌 712100
    2. 赫尔辛基大学林学系 赫尔辛基 FI-00014
    3. 西北农林科技大学生态仿真优化实验室 杨凌 712100
  • 收稿日期:2020-01-19 出版日期:2021-09-25 发布日期:2021-11-29
  • 通讯作者: 曹田健
  • 基金资助:
    国家自然科学基金面上项目(31670646);全国森林经营样板基地科技支撑专项(1692016-07)

Optimizing Parameters of a Process-Based Model for Pinus armandii: A Compromise between Empirical and Process-Based Modelling Approaches

Hailian Xue1,Xianglin Tian2,Tianjian Cao3,*   

  1. 1. College of Science, Northwest A & F University Yangling 712100
    2. Department of Forest Sciences, University of Helsinki Helsinki FI-00014
    3. Ecological Simulation-Optimization Laboratory, Northwest A & F University Yangling 712100
  • Received:2020-01-19 Online:2021-09-25 Published:2021-11-29
  • Contact: Tianjian Cao

摘要:

目的: 以基于碳平衡的过程模型CROBAS为例,提出一种结合经验模型与过程模型的混合建模方法,优化华山松过程模型CROBAS-PA的参数,探索在建模数据不足情况下预估复杂过程模型参数的有效途径。方法: 参数优化模型的目标函数设为过程模型CROBAS-PA与经验模型QUASSI 1.0在树高和生物量预测上的离差,优化模型的决策变量选取过程模型中10个随气候和树种调整的参数(树冠树叶分形维数、消光系数、比叶面积、最大光合速率、树叶衰老率、叶表面积密度、自然整枝参数、树枝边材率、树干边材衰老率和树枝边材衰老率),约束条件为过程模型参数的可行域。选用差分演化算法,采用Sobol一阶灵敏度和全局灵敏度系数进行参数敏感性分析与评估,利用平均误差(ME)、平均绝对误差(MAE)和平均相对误差(MRE)进行模型检验。结果: 经参数优化后的华山松过程模型CROBAS-PA的有效预测时间可达20年,树高和胸径预测值平均绝对误差分别小于1.03 m和1.19 cm,平均相对误差分别低于5.59%和2.59%。灵敏度分析显示,最大光合速率、比叶面积、消光系数、树冠树叶分形维数对树高和胸径的生长变化有明显影响,而叶表面积密度对胸径和树高的生长变化影响较小。结论: 经参数优化后的华山松过程模型CROBAS-PA可以较准确预测华山松的树高和胸径生长以及林木各器官中的碳分配,基于经验-过程混合建模方法在复杂过程模型参数预估中具有一定应用潜力。

关键词: 生物量, 差分演化算法, 参数预估, 华山松, 过程模型, 林分生长

Abstract:

Objective: Process-based models often consist of photosynthesis, respiration, and carbon allocation modules. This leads to a higher dimensionality of variables than empirical models. Thus, it is prone to the problem of insufficient data for traditional biological modelling. Based on a carbon balance model CROBAS, the study applied a hybrid modelling approach to optimize the parameters of CROBAS-PA for Pinus armandii, to explore an effective way to parameterize complex process-based models under the condition of sparse data. Method: The objective function of the parametric optimization model was set as the deviation of the process model CROBAS-PA from the empirical model QUASSI 1.0 for tree height and biomass predictions. The decision variables for the optimization model were selected from the process model with ten parameters that vary with climate and species: the "fractal dimension" of foliage in crown, the extinction coefficient, the specific leaf area, the maximum rate of canopy photosynthesis per unit area, the specific senescence rate of foliage, the "surface area" density of foliage, the parameter relative to self-pruning, the form factor of sapwood in branches, the form factor of senescent sapwood in stem inside crown, and the form factor of senescent sapwood in branches. The constraints are the feasible domains of the process-based model parameters. A differential evolution algorithm was chosen for the optimization. A sensitivity analysis for parameters was implemented with Sobol's first-order indices and total-effect indices. Model performance was judged by mean error(ME), mean absolute error(MAE), and mean relative error(MRE). Result: Model simulations showed that the effective prediction period of the process-based CROBAS-PA could reach 20 years. The average absolute errors of tree height and diameter at breast height were less than 1.03 m and 1.19 cm, respectively; The average relative errors were less than 5.59% and 2.59%, respectively. The sensitivity analysis showed that the maximum rate of canopy photosynthesis per unit area, the specific leaf area, the extinction coefficient, and the "fractal dimension" of foliage in crown had apparent effects on the growth of height and DBH, while the effect of "surface area" density of foliage was negligible. Conclusion: The parameter-optimized CROBAS-PA can accurately predict or explain tree diameter and height growth, as well as the carbon allocation in each organ of Pinus armandii. This indicates that the hybrid modelling technique has a promising potential for the parameter estimation of complex process-based models.

Key words: biomass, differential evolution, parameterization, Pinus armandii, process-based model, stand development

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