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Scientia Silvae Sinicae ›› 2021, Vol. 57 ›› Issue (9): 21-33.doi: 10.11707/j.1001-7488.20210903

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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

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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