Quantitative Evaluation of the Applicability of Classical Forest Ecosystem Carbon Cycle Models in China: A Case Study of the Biome-BGC Model

The Biome-BGC model is a classic forest ecosystem carbon cycle model driven by remote sensing and plant trait data, and it has been widely applied in various regions of China over the years. However, does the Biome-BGC model have good applicability in all regions of China? This question implies that the rationality of some applications of the Biome-BGC model in China might be questionable. To quantitatively assess the overall spatial applicability of the Biome-BGC model in China’s vegetation ecosystems, this study selected ten representative forest and grassland ecosystem sites, all of which have publicly available carbon flux data. In this study, we first used the EFAST method to identify the sensitive ecophysiological parameters of the Biome-BGC model at these sites. Subsequently, we calibrated the optimal values of these sensitive parameters through a literature review and the PEST method and then used these to drive the Biome-BGC model to simulate the productivity (including GPP and NEP) of these ten forest and grassland ecosystems in China. Finally, we compared the simulation accuracy of the Biome-BGC model at these ten sites in detail and established the spatial pattern of the model’s applicability across China. The results show that the sensitive ecophysiological parameters of the Biome-BGC model vary with spatial distribution, plant functional types, and model output variables. After conducting parameter sensitivity analysis and optimization, the simulation accuracy of the Biome-BGC model can be significantly improved. Additionally, for forest ecosystems in China, the model’s simulation accuracy decreases from north to south, while for grassland ecosystems, the accuracy increases from north to south. This study provides a set of localized ecophysiological parameters and advocates that the use of the Biome-BGC model should be based on parameter sensitivity analysis and optimization.