Variance-based sensitivity analysis of a forest growth model

Computer models are increasingly used to simulate and predict the behaviour of forest systems. Uncertainties in both parameter calibration and outputs co-exist in these models due to both the incomplete understanding of the system under simulation, and biased model structure. We used sensitivity analysis, including both screening and global variance-based methods, to explore these uncertainties. We applied these techniques to the widely used forest growth model Physiological Principles for Predicting Growth (3-PG2) using field data from 141 plots of Corymbia maculata and Eucalyptus cladocalyx in Australia. The screening method was used to select influential input parameters for the subsequent variance-based analysis and thereby reduce its computational cost. We assessed model outputs including biomass partitioning and water balance, and the sensitivities of the soil texture group, which includes 7 parameters. We also compared the screening and variance-based methods, and assessed the convergence of the variance-based method, and the change in sensitivities over time. Using these techniques, we quantified the relative sensitivities of each model output to each input parameter. The variance-based method exhibited good convergence and stable sensitivity rankings. The results indicated changes in input parameter sensitivities over longer simulation periods. The variance-based global sensitivity analysis can be very effective in calibration and identification of important processes within forest models.