Spatio-Temporal Variability of Soil Respiration of Forest Ecosystems in China: Influencing Factors and Evaluation Model

Understanding the influencing factors of the spatio-temporal variability of soil respiration (Rs) across different ecosystems as well as the evaluation model of Rs is critical to the accurate prediction of future changes in carbon exchange between ecosystems and the atmosphere. Rs data from 50 different forest ecosystems in China were summarized and the influences of environmental variables on the spatio-temporal variability of Rs were analyzed. The results showed that both the mean annual air temperature and precipitation were weakly correlated with annual Rs, but strongly with soil carbon turnover rate. Rs at a reference temperature of 0°C was only significantly and positively correlated with soil organic carbon (SOC) density at a depth of 20 cm. We tested a global-scale Rs model which predicted monthly mean Rs (Rs,monthly) from air temperature and precipitation. Both the original model and the reparameterized model poorly explained the monthly variability of Rs and failed to capture the inter-site variability of Rs. However, the residual of Rs,monthly was strongly correlated with SOC density. Thus, a modified empirical model (TPS model) was proposed, which included SOC density as an additional predictor of Rs. The TPS model explained monthly and inter-site variability of Rs for 56% and 25%, respectively. Moreover, the simulated annual Rs of TPS model was significantly correlated with the measured value. The TPS model driven by three variables easy to be obtained provides a new tool for Rs prediction, although a site-specific calibration is needed for using at a different region.