## 3 Results
### Model Development in a Subtropical Coniferous Forest
#### 3.1.1 Empirical Model of Ecosystem Respiration in Daytime: Re (LR) Method  
During the daytime and nighttime, Re and NEP displayed small variations in this coniferous forest compared to that of GPP. NEP showed evident diurnal, seasonal, and inter-annual variations, and was influenced by the driving factors, e.g., PAR, air and soil temperature, precipitation, clouds, water vapor, vapor pressure deficit (VPD), soil water content [46; 59; 60; 54], and higher values appeared around noon and in the summer. Selecting the hourly observational data described in Section 2.3 together with that corresponding to S/Q < 0.5 and S/Q \(\geq\) 0.5, 67 and 985 group data (sample point \(n\) = 67 and 985) from 23 May 2013 to 31 December 2014 were used to develop the 3-factor and 2-factor Re empirical models during the daytime, respectively. Meanwhile, the water vapor pressure at the ground and Re were also used to determine the coefficients B\({}_{1}\) of the EMRe models (defined as Re (LR)). Tables 1 and 2 show the results and statistical metrics, including coefficient of determination (R\({}^{2}\)), average and maximum of the absolute relative bias, \(\delta_{\mathrm{avg}}\) and \(\delta_{\mathrm{max}}\) (%), (\(\delta=|\,y_{cal}-y_{obs}\,|\,\times\,100/\,|\,y_{obs}\,|\), where \(y_{cal}\) and \(y_{obs}\) are calculated and observed Re values), normalized mean square error (\(\mathrm{NMSE}=\left(y_{cal}-y_{obs}\right)^{2}/\left(\overline{y}_{cal}\, \times\,\overline{y}_{obs}\right)\)[61]), standard deviations of calculated and observed fluxes (\(\sigma_{\mathrm{cal}}\) and \(\sigma_{\mathrm{obs}}\)), mean absolute deviations (MAD, in mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and percentage of mean measured value, %), and root mean square errors (RMSE, in mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and in percentage of mean measured value). Figures 1 and 2 show the scatter plots of the calculated versus the measured daytime hourly Re during 2013-2014 using the 3-factor and 2-factor Re models for S/Q \(<\) 0.5 and S/Q \(\geq\) 0.5 situations. In general, the 3-factor and 2-factor Re models displayed reasonable simulations for S/Q \(<\) 0.5 and S/Q \(\geq\) 0.5 conditions, and larger estimation uncertainties were found for \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Model** & **B\({}_{1}\)** & **B\({}_{2}\)** & **B\({}_{3}\)** & **B\({}_{6}\)** & **R\({}^{2}\)** & **\(\delta\)avg** & **NMSE** & **cal/obs** & \(\sigma_{\rm cal}\) & \(\sigma_{\rm obs}\) & \multicolumn{2}{c}{**MAD**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{1-1} \cline{6-12}
3-F & 0.026 & 1.036 & 0.000 & 0.057 & 0.999 & 24.31 & 0.085 & 1.001 & 0.07 & 0.02 & 0.05 & 24.04 & 0.06 & 29.37 \\ \hline
2-F & 0.026 & 1.036 & & 0.057 & 0.999 & 24.31 & 0.085 & 1.001 & 0.07 & 0.02 & 0.05 & 24.04 & 0.06 & 29.37 \\ \hline \end{tabular}
\end{table} 
Table 1: Coefficients of the 3-factor and 2-factor EMRe models (3-F, 2-F) in the daytime (Equation (2)) determined using observational data in the subtropical coniferous plantation during 2013–2014, statistical metrics, i.e., coefficient of determination (R\({}^{2}\)), average of the absolute relative bias (\(\delta_{\rm avg}\), %), normalized mean square error (NMSE), ratio of calculated Re to observed Re (cal/obs), standard deviations of calculated and observed fluxes (\(\sigma_{\rm cal}\) and \(\sigma_{\rm obs}\)), together with mean bias errors (MAD, mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and %) and root mean square errors (RMSE, mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and %) (S/Q \(<\) 0.5, \(n=67\)). Figure 1: Scatter plot of calculated versus measured daytime hourly Re using the 3-factor and 2-factor Re models (left and right) during 2013–2014 (S/Q \(<\) 0.5, \(n=67\)). The lines are a linear fit to the data for calculated versus measured Re. Figure 2: Scatter plot of calculated versus measured daytime hourly Re using the 3-factor and 2-factor Re models (left and right) during 2013–2014 (S/Q \(\geq\) 0.5, \(n=985\)). The lines are a linear fit to the data for calculated versus measured Re.