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[Comparison of global sensitivity analysis techniques based on a process-based model CROBAS.]

Hai-Lian Xue1, Xiang-Lin Tian1, Bin Wang2

  • 1Northwest A＆F University, Yangling 712100, Shaanxi, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|January 21, 2021
PubMed
Summary
This summary is machine-generated.

Global sensitivity analysis methods were compared for the CROBAS model of Pinus armandii. The Morris and EFAST methods are efficient for model calibration and parameterization, with light interception being key for tree growth simulation.

Keywords:
EFASTMorrisSobolglobal sensitivityprocess-based model

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Area of Science:

  • Ecological modeling
  • Forestry science
  • Computational biology

Background:

  • Process-based models are crucial for ecological studies but often limited by physiological parameter measurement difficulties.
  • Global sensitivity analysis (GSA) is vital for understanding model behavior and guiding improvements in structure, data collection, and parameter calibration.

Purpose of the Study:

  • To compare the performance of three GSA methods (Morris, Sobol, EFAST) for a process-based tree growth model (CROBAS).
  • To identify key physiological parameters influencing Pinus armandii growth simulations.
  • To evaluate the efficiency and convergence of different GSA methods.

Main Methods:

  • Applied Morris, Sobol, and EFAST methods to 10 tree structure parameters of the CROBAS model for Pinus armandii.
  • Utilized Nash-Sutcliffe Efficiency (NSE) of tree height and biomass as objective functions.
  • Assessed methods based on parameter sensitivity ranking, time consumption, and convergence efficiency.

Main Results:

  • Parameter sensitivity rankings varied slightly across methods and significantly with different objective functions.
  • Morris and EFAST methods demonstrated superior time and convergence efficiency compared to the Sobol method.
  • Maximum canopy photosynthesis rate, specific leaf area, and extinction coefficient were identified as highly influential parameters.
  • Light interception significantly impacts tree growth simulation, highlighting the importance of the photosynthetic carbon fixation module.

Conclusions:

  • The Morris method is suitable for qualitative sensitivity analysis, while EFAST is recommended for quantitative analysis of complex process-based models.
  • Prioritizing data collection and validation for photosynthetic carbon fixation and foliage biomass modules is crucial for accurate tree growth simulations using CROBAS.
  • GSA effectively guides model improvement and parameterization in ecological modeling.