4.4. synergistic effect and prediction models
Mande believed that the soil temperature and moisture explained the
spatial and temporal variation in soil CO2 flux.
Likewise, the changes in the soil properties and forest carbon
significantly increased the soil CO2 efflux indicating a
strong positive correlation(Mande et al., 2015). In our study,
multivariate correlation analysis was carried out on soil temperature,
moisture, concentration of organic carbon and soil CO2flux at different layers (5-20cm, 80-200cm). The results showed that
there was a significant correlation (P<0.01)whether at the
deep layer (80-200 cm) or shallow layers. All of above suggested that
soil temperature, moisture, and soil organic carbon substrates all
exhibited synergistic responses to soil CO2 flux whether
at deep layers or shallow layers.
Chen et al. developed a T&P&C-model including SOC as an additional
predictor of annual R-s. This extended but still simple model performed
better than the T&P-model and explained 69%, 89%, and 47% of the
interannual and intersite variability of R-s for croplands, grasslands
and forests, respectively. And the modeling efficiency of the
T&P&C-model was nearly 60% across ecosystems. (Chen et al., 2010). In
this study, based on the traditional hydrothermal two-factor models
(T&M models), combined with the environment and substrate
characteristics of artificial Robinia pseudoacacia , a
three-factor model of temperature(T), moisture(M) and organic carbon
substrate (C) was established at the deep and shallow layer,
respectively(Table 4). Better estimates of CO2 flux at
deep layers than shallow layers would be obtained with the new model
driven by temperature(T), moisture(M) and organic carbon substrate
together. In order to further verify the superiority of the T&M&C
model, we selected vertical CO2 flux data of different
ecosystems in different research areas through literature, and compared
the accuracy of the T&M&C model with the T&M model. The prediction
accuracy (RMSE/MAE) was increased by average of 20~26%,
and T&M&C model was of better results in prediction effect. Therefore,
the new multi-factor model driven by climate and soil properties can
better estimate CO2 flux at different layers and should
be widely used to predict global carbon emissions.