## 3 Results  
### Model Performance  
Prior to using the model for quantifying the contributions of different driving factors, the DTVGM model was evaluated in simulating the ET and GPP in the six catchments. The DTVGM simulated ET and GPP were evaluated using the remote sensing product. The model was then calibrated using the data during 2000-2006 and validated for 2007-2010 on a monthly scale. Figure 3 shows the monthly ET by the DTVGM model and the MODIS data from 2000 to 2010 in the six catchments. Generally, the evaluation shows that the seasonal patterns of the simulated ET are consistent with the MODIS data. Comparing to the MODIS data, the ET was found to be underestimated in the winter season. The relative error (RE) is below 7.0%, and the Pearson correlation coefficient (r) is above 0.73 except for the THC, and the root mean squares error (RMSE) is below 0.67 mm in the six catchments as shown in the statistical results in Table 3. Figure 4 displays the estimated monthly GPP by DTVGM and MODIS in the catchments. The results show that the simulated GPP captures the MODIS GPP patterns and magnitudes reasonably well. The RE values are almost all below 11.0% except for the BSC, the r values even reach up to 0.90 except for the BGC about 0.86, and the RMSE is below 1.24 g C/m\({}^{2}\) (Table 3). Figure 5 shows the comparison of the annual simulated and remote-sensing based ET and GPP in the region. The least-squares linear fits to the simulated ET and GPP is near to the 1:1 line, while slightly underestimated, and the R\({}^{2}\) value of linear goodness of fit test is about 0.51 and 0.71 for ET and GPP, respectively. Furthermore, the simulated annual ET was also compared with the GLEAM annual ET data. The results show that the RE is \(-\)4.3%, 4.1% \(-\)5.7%, \(-\)17.2%, \(-\)12.7% and \(-\)20.9% in the GBC, JMC, BSC, THC, DSC and CHC, respectively and the average RE is \(-\)8.7%. Figure 5c shows that the simulated annual ET by DTVGM is mostly between the values of MODIS and GLEAM. Overall, the results are acceptable for ET and GPP simulations in the region. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cathment**} & \multicolumn{2}{c}{**ET**} & \multicolumn{3}{c}{**GPP**} \\ \cline{2-7}  & **RE** & **r** & **RMSE** & **r** & **RMSE** \\  & & **(mm)** & & & **(g C/m\({}^{2}\))** \\ \hline BGC & 0.6\% & 0.80 & 0.62 & 6.1\% & 0.86 & 1.20 \\ JMC & 5.9\% & 0.79 & 0.67 & 7.9\% & 0.94 & 1.17 \\ BSC & 5.9\% & 0.73 & 0.67 & 17.8\% & 0.90 & 1.24 \\ THC & 6.0\% & 0.66 & 0.60 & 4.3\% & 0.93 & 0.91 \\ DSC & 3.5\% & 0.74 & 0.59 & 10.1\% & 0.95 & 0.90 \\ CHC & 6.8\% & 0.76 & 0.61 & 11.0\% & 0.94 & 1.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance in monthly ET and GPP simulation comparing to MODIS data. Figure 3: Comparison of seasonal dynamics between DTVGM simulated and MODIS ET in the six catchments BGC (**a**), JMC (**b**), BSC (**c**), THC (**d**), DSC (**e**), CHC (**f**). Figure 4: Comparison of seasonal dynamics between DTVGM simulated and MODIS GPP in the six catchments BGC (**a**), JMC (**b**), BSC (**c**), THC (**d**), DSC (**e**), CHC (**f**). The monthly Mann-Kendall changing trends of the climate variables in the six catchments are shown in Figure 6. The color bar indicates the changing trends in the variables. The atmospheric pressure displayed a significant decrease in summer and autumn, particularly in August and September. The humidity shows a dropping trend, particularly in January. The monthly precipitation increased mostly in February, April, July, August and October, particularly in the wet season, while it decreased in the dry season, especially in January, November and December. For the sunshine duration, it decreased almost throughout the year, especially for July. The windspeed decreased in most of the months, particularly during March, April, October and November. Unlike the changes of windspeed, the monthly temperature increased gradually in most of the months, and fell mostly in February and July.