Spatio-Temporal Variability of Soil Respiration of Forest Ecosystems in China: Influencing Factors and Evaluation Model |
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Authors: | Ze-Mei Zheng Gui-Rui Yu Xiao-Min Sun Sheng-Gong Li Yue-Si Wang Ying-Hong Wang Yu-Ling Fu Qiu-Feng Wang |
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Institution: | (1) Department of Environmental Science and Technology, East China Normal University, North Zhongshan Road 3663, Putuo District, Shanghai, 200062, China;(2) Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road 11A, Chaoyang District, Beijing, 100101, China;(3) Institute of Atmosphere Physics, Chinese Academy of Sciences, 40# Hua Yan Li, Qi Jia Huo Zi, Chaoyang District, Beijing, 100029, China |
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Abstract: | 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. |
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Keywords: | |
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