Evaluation of grassland carbon pool based on TECO-R model and climate- driving function: A case study in the Xilingol typical steppe region of Inner Mongolia, China

摘要

The grassland ecosystems, which are the most widely distributed terrestrial ecosystems, play an important role in regional climate change and the global carbon cycle. While researchers worldwide have spent much effort on quantitatively evaluating organic carbon at the regional scale, few studies have examined organic carbon pools at different levels, or their driving factors. To better facilitate a deeper understanding of carbon pool mechanisms, comprehensive understanding and comparative analysis among carbon pools is necessary. In this study, the Xilingol Typical Steppe Region of Inner Mongolia was used as a case study to quantitatively model vegetation, litter, soil, and ecosystem carbon pools for the 2011-2018 period. The improved Terrestrial Ecosystem Regional (TECO-R) model was used to perform carbon stock simulations, which was modified and calibrated for local application. The organic carbon pools at different levels were compared and analyzed in terms of their spatial distribution, inter-annual variation, and climate-driving factors. The results showed that the modified TECO-R model accurately simulated carbon storage on the whole. From 2011 to 2018 year, the various organic carbon pools increased overall and were characterized by different degrees of clustering in their spatial distribution, inter-annual variation, and climate-driving factors. Clear formation mechanisms were observed in the soil, litter, and root carbon pools. As the soil depth increased, the carbon stock of the root carbon pool and the soil carbon pool decreased. Climate factors exerted different degrees of constraints on each carbon pool. These findings promote understanding of the compositional differences in grassland carbon pools and the driving mechanism for these carbon pools, which, taken together, can help shape the policy for carbon sink management in grasslands.