## 2 Materials and Methods
### Methods  
The overall research framework of this paper is illustrated in Figure 2 and consists of the following main steps. First, GPP estimation was conducted using the RF method, a machine learning algorithm based on decision trees. RF offers strong nonlinear fitting capabilities, requires minimal assumptions about input variable distributions, and can handle diverse data types. Multiple data sources, including topographic, environmental, and meteorological data, were integrated to build the GPP estimation model. The estimation results were validated using ground-based observation data to assess accuracy. Subsequently, the spatiotemporal distribution patterns of GPP and its interannual variation trends were analyzed. Multiple evapotranspiration datasets were integrated to obtain more accurate ET estimates. Five long-term ET datasets, covering monthly data for the QTP from 1982 to 2018, were analyzed for their characteristics and uncertainties. A multiple regression model was used for data fusion, leveraging the strengths of each dataset to enhance the reliability of ET estimates. The accuracy of the means and regression results for the five datasets was validated using flux site observation data. Metrics such as the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) were calculated to evaluate model performance and dataset applicability. Third, ecosystem WUE was calculated after completing the GPP and ET estimations. The spatial and temporal distribution characteristics of WUE were then  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Data Name** & **Spatial Resolution** & **Temporal Resolution** & **Website** \\ \hline \multirow{7}{*}{Observation data} & & & day & [[https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2015283118/-/DCSupplemental](https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2015283118/-/DCSupplemental)]([https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2015283118/-/DCSupplemental](https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2015283118/-/DCSupplemental)) & \\  & & & day & 10.1073/pnas.2015283118/-/DCSupplemental & \\  & & & & (accessed on 13 December 2023) & \\  & & & & [[http://www.chinaflux.org](http://www.chinaflux.org)]([http://www.chinaflux.org](http://www.chinaflux.org)) (accessed on 31 December 2023) & \\  & & & month & [[https://fluxnet.fluxdata.org/data/fluxnet2015](https://fluxnet.fluxdata.org/data/fluxnet2015)]([https://fluxnet.fluxdata.org/data/fluxnet2015](https://fluxnet.fluxdata.org/data/fluxnet2015)) & \\  & & & & (accessed on 23 November 2023) & \\  & & & 30 min & [[http://www.chinaflux.org](http://www.chinaflux.org)]([http://www.chinaflux.org](http://www.chinaflux.org)) (accessed on 16 July 2023) & \\  & ET & & 30 min & [[https://fluxnet.fluxdata.org/data/fluxnet2015](https://fluxnet.fluxdata.org/data/fluxnet2015)]([https://fluxnet.fluxdata.org/data/fluxnet2015](https://fluxnet.fluxdata.org/data/fluxnet2015)) & \\  & & & & (accessed on 16 September 2023) & \\  & & & 30 min & [[https://poles.tcp.dc.cn](https://poles.tcp.dc.cn)]([https://poles.tcp.dc.cn](https://poles.tcp.dc.cn)) (accessed on 9 April 2024) & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 1: Ground observation data and spatial data sources.  
analyzed. Fourth, correlation analysis was performed to explore the relationship between climate change, GPP, and ET. The interannual variation (IAV) of WUE was also examined. This comprehensive framework provides a systematic approach to analyzing GPP, ET, and WUE, offering insights into their dynamics and interactions.