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 (R_s) across different ecosystems as well as the evaluation model of R_s is critical to the accurate prediction of future changes in carbon exchange between ecosystems and the atmosphere. R _s data from 50 different forest ecosystems in China were summarized and the influences of environmental variables on the spatio-temporal variability of R_s were analyzed. The results showed that both the mean annual air temperature and precipitation were weakly correlated with annual R _s, but strongly with soil carbon turnover rate. R_s 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 R_s model which predicted monthly mean R_s (R_s,monthly) from air temperature and precipitation. Both the original model and the reparameterized model poorly explained the monthly variability of R_s and failed to capture the inter-site variability of R_s. However, the residual of R_s,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 R _s. The TPS model explained monthly and inter-site variability of R_s for 56% and 25%, respectively. Moreover, the simulated annual R_s 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 R_s prediction, although a site-specific calibration is needed for using at a different region.