## 3 Results and Discussion
### Driving Factors of Seasonal CH\({}_{4}\) Fluxes  
The determination coefficients (R\({}^{2}\)) of the semiempirical multiplicative model are listed in Table 2. Model-1 performed better than Model-2 for 2018 and 2020 late rice, and Model-2 performed better for 2019 late rice. Unlike in the other seasons, for early rice in 2020, Model-2 outperformed Model-1 at P1, and Model-1 outperformed Model-2 at P2. The different performances of the two models in different growing seasons may be because only one peak was observed during the whole late rice seasons in 2018 and 2020; thus, constructing the model with data from the whole growing season would be more effective than constructing the model with partial-season data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model-1**}} & \multicolumn{6}{c}{**Model-2**} \\ \cline{2-7}  & \multicolumn{2}{c}{**PI \({}^{\mathbf{b}}\)**} & \multicolumn{2}{c}{**P2 \({}^{\mathbf{b}}\)**} & \multicolumn{2}{c}{**P3 \({}^{\mathbf{b}}\)**} \\ \hline
**Variable** & **R\({}^{2}\)** & **Variable** & **R\({}^{2}\)** & **Variable** & **R\({}^{2}\)** & **Variable** & **R\({}^{2}\)** \\ \hline \multicolumn{7}{c}{2018 late rice} \\ T\({}_{\text{s5}}\) & 0.921 & T\({}_{\text{s5}}\) & 0.690 & T\({}_{\text{s5}}\) & 0.789 & & \\ SWC & 0.941 & GPP & 0.735 & SWC & 0.839 & & \\ U\({}^{\ast}\) & 0.942 & RH & 0.737 & GPP & 0.850 & & \\ GPP & 0.942 & SWC & 0.739 & RH & 0.850 & & \\ RH & 0.942 & U\({}^{\ast}\) & 0.739 & U\({}^{\ast}\) & 0.850 & & \\ \hline \multicolumn{7}{c}{2019 late rice} \\ T\({}_{\text{s5}}\) & 0.793 & SWC & 0.809 & GPP & 0.856 & T\({}_{\text{s5}}\) & 0.903 \\ GPP & 0.836 & RH & 0.848 & RH & 0.872 & SWC & 0.906 \\ SWC & 0.847 & T\({}_{\text{s5}}\) & 0.878 & U\({}^{\ast}\) & 0.881 & GPP & 0.909 \\ RH & 0.854 & U\({}^{\ast}\) & 0.882 & T\({}_{\text{s5}}\) & 0.881 & U\({}^{\ast}\) & 0.911 \\ U\({}^{\ast}\) & 0.856 & GPP & 0.882 & SWC & 0.881 & RH & 0.911 \\ \hline \multicolumn{7}{c}{2020 early rice} \\ SWC & 0.163 & GPP & 0.108 & SWC & 0.838 & & \\ T\({}_{\text{s5}}\) & 0.584 & SWC & 0.534 & GPP & 0.860 & & \\ RH & 0.702 & RH & 0.541 & RH & 0.875 & & \\ GPP & 0.702 & T\({}_{\text{s5}}\) & 0.555 & T\({}_{\text{s5}}\) & 0.876 & & \\ U\({}^{\ast}\) & 0.702 & U\({}^{\ast}\) & 0.555 & U\({}^{\ast}\) & 0.876 & & \\ \hline \multicolumn{7}{c}{2020 late rice} \\ T\({}_{\text{s5}}\) & 0.826 & U\({}^{\ast}\) & 0.142 & RH & 0.458 & T\({}_{\text{s5}}\) & 0.791 \\ RH & 0.852 & RH & 0.292 & GPP & 0.578 & GPP & 0.814 \\ GPP & 0.874 & SWC & 0.328 & U\({}^{\ast}\) & 0.647 & SWC & 0.825 \\ SWC & 0.881 & T\({}_{\text{s5}}\) & 0.381 & SWC & 0.728 & U\({}^{\ast}\) & 0.827 \\ U\({}^{\ast}\) & 0.881 & GPP & 0.449 & T\({}_{\text{s5}}\) & 0.731 & RH & 0.827 \\ \hline \hline \end{tabular} (R\({}^{2}\))\({}^{\ast}\): R\({}^{2}\) corresponded to the coefficient of determination when the corresponding row and the elements above it were used to construct the multiplicative model. P1-P3 \({}^{\mathbf{b}}\): different periods of the whole growing season determined as described in Section 2.4.2.  
\end{table} 
Table 2: Coefficients of determination (R\({}^{2}\)) a for the stepwise multivariate regression models. As shown in Table 2, T\({}_{55}\) was able to explain 92.1% and 82.6% of the seasonality in CH\({}_{4}\) fluxes when Model-I was adopted for 2018 and 2020 late rice, respectively. When all five factors were used to construct the multiplicative model, R\({}^{2}\) were 0.942 and 0.881 for 2018 and 2020 late rice, respectively. The increase in R\({}^{2}\) was only 0.021 and 0.055 when the other four factors were all taken into consideration, indicating that T\({}_{55}\) was the dominant factor regulating seasonal CH\({}_{4}\) fluxes in late rice in 2018 and 2020. Similarly, T\({}_{55}\) was the main controlling factor for the whole growing season in late rice in 2019. SWC, GPP and T\({}_{55}\) were the main drivers at P1, P2 and P3, respectively.