## 2 Materials and Methods
### Data Sources and Meteorological Scenarios  
We established five weather stations in our study area, basing them on five tea plantations for meteorological and hydrological observations from 2010 to 2021 (Figure 1c). Meanwhile, an eddy-covariance flux was launched in 2014 (Figure 1c), and it obtained high-frequency energy, water, and carbon fluxes in the tea plantations. For the weather stations, ET\({}_{\text{a}}\) was calculated using water balance methods during days without rain for 3 consecutive days. For the flux station, ET\({}_{\text{a}}\) was obtained from the direct measurement of the energy flux. The details of flux data processing can be found in [6]. A brief description of our weather and flux stations is provided in Table 1. Table 2 presents the correlation matrix of tea plantation ET\({}_{\text{a}}\) with soil water, vegetation parameters, and meteorological data input variables. The purpose of these correlations was to determine the parameters that could provide the best estimation of overall tea plantation ET\({}_{\text{a}}\). The results showed that the relationship between net radiation (R\({}_{\text{n}}\)) and ET\({}_{\text{a}}\) (0.83) is higher than the relationship with other variables; that is, R\({}_{\text{n}}\) exerts the greatest effect on ET\({}_{\text{a}}\) among the input parameters. The second highest correlation, 0.71, was obtained between mean air temperature (T\({}_{\text{mean}}\)) and ET\({}_{\text{a}}\). Previous studies have also reported that radiation conditions exhibit higher relevance with ET\({}_{\text{a}}\) in humid regions where water is relatively sufficient [34; 35]. Soil moisture (S\({}_{\text{m}}\)) and mean relative humidity (RH) were negatively correlated with ET\({}_{\text{a}}\) (values: \(-\)0.22 and \(-\)0.17, respectively). Meanwhile, the vegetation parameter leaf area index (LAI) was also considered in the ET\({}_{\text{a}}\) estimation. Accordingly, the parameters were classified by adding  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Name** & **Longitude** & **Latitude** & **Elevation (m)** & **MAP (mm)** & **MAT (\({}^{\circ}\)C)** & **Period** & **LULC** \\ \hline SSY & 119.316 & 31.268 & 55 & 1249.3 & 15.8 & 2010–2017 & 3–10 years old tea \\ TMOM & 119.397 & 31.313 & 39 & 1507.4 & 17.8 & 2020–2021 & 5–6 years old tea \\ TMHS & 119.411 & 31.269 & 28 & 1371.3 & 16.7 & 2016–2021 & 5–10 years old tea \\ Tea plantation flux site & 119.453 & 31.269 & 103 & 1216.3 & 16.2 & 2014–2021 & 4–11 years old tea \\ HB & 119.432 & 31.237 & 91 & 1237.6 & 15.9 & 2014–2017 & 5–8 years old tea \\ PQ & 119.446 & 31.217 & 94 & 1109.5 & 16.3 & 2017–2021 & 6–10 years old tea \\ \hline \end{tabular}
\end{table}
Table 1: Weather and eddy covariance flux site information (MAP: mean annual precipitation; MAT: mean annual temperature).  
the next closest correlation. Six scenarios of meteorological, soil, and vegetation parameters were analyzed using different machine learning algorithms (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\mathbf{R_{n}}\) & \(\mathbf{T_{mean}}\) & \(\mathbf{W_{s}}\) & \(\mathbf{RH}\) & \(\mathbf{S_{m}}\) & \(\mathbf{ET_{a}}\) \\ \hline \(\mathrm{R_{n}}\) & \(1\) & & & & & \\ \(\mathrm{T_{mean}}\) & \(0.65\) & \(1\) & & & & \\ \(\mathrm{W_{s}}\) & \(0.08\) & \(-0.06\) & \(1\) & & & \\ RH & \(-0.37\) & \(0.13\) & \(-0.29\) & \(1\) & & \\ \(\mathrm{S_{m}}\) & \(-0.13\) & \(-0.35\) & \(-0.04\) & \(0.06\) & \(1\) & \\ \(\mathrm{ET_{a}}\) & \(0.83\) & \(0.71\) & \(0.18\) & \(-0.17\) & \(-0.22\) & \(1\) \\ \hline \end{tabular}
\end{table}
Table 2: Correlation matrix between daily tea plantation \(\mathrm{ET_{a}}\) and meteorological data (\(\mathrm{R_{n}}\), \(\mathrm{T_{mean}}\), \(\mathrm{W_{s}}\), RH, and \(\mathrm{S_{m}}\)).  
Figure 1: China Map (**a**), Location of Taihu Lake basin (**b**), Tianmu Lake catchment, and the monitoring sites (**c**).