Evaluation of Gross Primary Production Models of Varying Complexity Using a Three-Dimensional Forest Simulation Framework

Gross primary production (GPP) models are widely used to estimate carbon fluxes at local and global scales, and play a crucial role in understanding the dynamics of terrestrial carbon cycling. While numerous studies have compared the performance of various GPP models, most evaluations rely on in situ GPP derived from eddy covariance flux towers, which may be constrained by estimation uncertainties and limited spatial representativeness. In this study, we employed a three-dimensional (3D) simulation framework characterized by high accuracy and strong environmental controllability to evaluate the performance of GPP models of varying complexity (FvCB, MOD17, VPM, and MVPM) under different leaf area index (LAI) levels and environmental stress conditions. The results revealed significant differences among the models at both instantaneous and daily scales. Under high-temperature stress, the performance of VPM was most comparable to that of MOD17. FvCB and MOD17 exhibited strong consistency in their sensitivity to environmental variations, whereas MVPM generally produced lower GPP estimates but showed the highest responsiveness to environmental changes. The process-based FvCB model was the most sensitive to canopy structure and light distribution, and its resilience to environmental stress increased with LAI. These findings provide a novel methodological perspective for evaluating GPP models and offer important insights into the structural and mechanistic factors driving performance differences among the models.