Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5741799 | Ecological Indicators | 2017 | 13 Pages |
â¢Each of the carbon pool or carbon flow process was detached from the main model as independent component for study.â¢NPP and ξ are the most important parameters in the DCC model.â¢The carbon density of GRA and CRO is more sensitive to key parameters of the model than that of the other biomes.
Determining the equilibrium state of terrestrial carbon is a prerequisite for scientific analysis on the carbon cycle. However, the mechanism through which the carbon cycle reaches the equilibrium state remains unclear. Moreover, the carbon cycle in most of the short-term field experiments rarely reaches the equilibrium state. In this study, a detachable carbon cycle (DCC) model was proposed to simulate the equilibrium state of each carbon pool. The model was established based on a pool-and-flux scheme and contained 14 carbon pools, or carbon flow processes, each process could be detached from the main model and evaluated as an independent component. The environmental scalar algorithms of the Integrated Terrestrial Ecosystem Carbon budget model (InTEC) and Community Atmosphere Biosphere Land Exchange (CABLE) were incorporated in the DCC model. Four situations were compared using the two environmental scalar algorithms and model structure (9 vs. 14 carbon pools). Furthermore, the size and turnover time of each carbon pool were analyzed at the equilibrium state. A sensitivity analysis was then conducted to investigate the responses of carbon density and equilibrium time to 12 key parameters of the model. Results indicated that the combination of the CABLE environmental scalar algorithm and 14 pools exhibited improved performance on carbon storage simulation than that of the other combinations, and the effect of the environmental scalar algorithm was considerably larger than that of the carbon pool number. Sensitivity analysis indicated that the carbon density of grassland and cropland was more vulnerable and sensitive to key parameters of the model than that of the other biomes. This study elucidates influencing factors and underlying control mechanisms in the carbon accumulation, and provides a framework for quantitative analysis of each component of the carbon cycle.