3.4. Synergistic characteristics of impact factors and
prediction models
In order to accurately explore the effects of temperature, moisture, SOC
on soil CO2 flux, multivariate correlation analysis was
carried out among the three factors and soil CO2 flux at
deep and shallow layers. The results showed that there were significant
correlations between soil temperature, moisture, concentration of soil
organic carbon and soil CO2 flux in the shallow layers
(5-20 cm) (P<0.01). Moreover, there was a significant
correlation between soil moisture and temperature.(P<0.01)
(Fig7. Heat map: A). In deep layers( 80-200 cm), soil temperature,
moisture, concentration of soil organic carbon and soil
CO2 flux were significantly correlated
(P<0.01), and there was a significant correlation between
temperature, moisture and concentration of soil organic carbon
(P<0.01) (Fig7. Heat map: B). For generality, whether in deep
or shallow layers, soil temperature, moisture, and SOC had a synergistic
effect on responses to soil CO2 flux.
Because soil temperature, moisture, and concentration of soil organic
carbon cooperatively contributed to on soil CO2 flux,
and there was a certain interaction between these influencing factors
(Fig.7). The CO2 flux under different temperature,
moisture and SOC distributed relatively concentrated in deep layers, and
the distribution of CO2 flux in shallow layers was
discrete (Fig.8). If only a single-factor model or hydrothermal
two-factor model was used to predict soil CO2 flux, it
is inevitable that the influence of other factors will be ignored, and
the change law of soil CO2 flux would not be well
described. Therefore, on the basis of previous research in the
hydrothermal two-factor models (Exponential-Power model,
Q10-hyperbolic model )(Table 4), we adopted piecewise
fitting method to build up a mathematic model for soil temperature,
moisture, concentration of soil organic carbon and soil
CO2 flux at different layer (Table 5).
In general, value of R2 in T&M&C model was further
improved compared to the T&M model, and the fitting results of these
models were significant (Table 4, Table 5). In order to further analyze
the prediction accuracy of the model and the possibility of model
generalization, we integrated some literatures which study on the
vertical CO2 flux, especially the deep
CO2 flux and its influencing factors, to obtain the
required data including CO2 flux, temperature, moisture
and SOC of each profile for analysis (Appendix A ). Based on
T&M&C model and T&M model, we used the obtained temperature,
moisture, and organic carbon data to simulate the predicted
CO2 flux and compare it with the measured
CO2 flux, the simulated CO2 flux and
measured CO2 flux fit well with the linear relationship
in various types of plots (Fig.9, Fig.10). The prediction accuracy was
evaluated by three variables of different dimensions
(R2, RMSE, MAE). Compared with the T&M model, the
R2 value of T&M&C model increased, but RMSE and MAE
differently decreased(Appendix B ). The prediction accuracy of
T&M&C model had an increase of about 20%-25%, and prediction
accuracy of CO2 flux steadily improved in deep layers
rather than shallow layers (Fig. 11).