## 3 Results and Discussion
### Stability Appraisal and Uncertainty of Machine Learning Models  
Figure 6 shows the average RMSE of all machine learning algorithms in our study during the validation and testing phases under six input scenarios. The results show that RF and bagging models exhibited improved steadiness with low RMSE values increasing in the validation phase over the testing phase. By contrast, MLP was the worst-performing model compared with the Ad, KNN, SVM, bagging, and RF models. The RF and bagging models reliably demonstrated the lowest percentage increase (\(-\)15.3% to +18.5%) in validation-phase RMSE over testing phase RMSE among the six machine learning algorithms, suggesting that the RF and bagging models would considerably improve the prediction accuracy when using new climate datasets with high stability. Our results are consistent with the outcomes of [32]. They also reported that the RF model presented a lower increase in testing RMSE from 0 to 49% in the validation phase over the testing phase. The authors of [68] revealed that RF exhibited high performance in evapotranspiration estimation by using the FLUXNET2015 dataset. However, the results of our study disagreed with those of [69], who reported that the RMSE increase rate for the bagging model in the test phase was typically higher than the rates for other machine learning models. Moreover, EL algorithms, including bagging, RF, and Ad, exhibited inferior performance in regression problems due to the impaired ability to provide a constant output [69]. In our study, however, the RF and bagging models displayed good stability with a satisfactory percentage increase, wherein the decision tree might provide more weight for points that were not predicted using the previous predictor and finally reserved the weighted voting right to overcome the estimation difficulty of overfitting [70]. However, the ensemble model Ad could not produce good prediction accuracy in our case. In particular, in the 6-8 mm day\({}^{-1}\) range (early-growing and mid-growing seasons), it had a higher RMSE than the RF and bagging models (Figures 7 and 8). This result was largely due to the high sensitivity to \"abnormal samples\" of Ad during that period (high plant physiology limits), which obtained a high weight in iteration, affecting the prediction accuracy of the final strong learner. This phenomenon was also documented by many researchers who used the Ad model to resolve regression problems in hydrology and agriculture studies [71; 72]. The levels of accuracy in different estimation ranges and seasons of daily tea plantation \(\text{ET}_{\text{a}}\) were assessed (Figures 7 and 8). The range of daily \(\text{ET}_{\text{a}}\) observed values in the validation phase was divided into four sub-intervals: 0-2, 2-4, 4-6, and 6-8 mm day\({}^{-1}\). Meanwhile, the performance of different seasons, i.e., the early-growing season (E), mid-growing season (M), late-growing season (L), and non-growing season (N) with different estimation ranges of \(\text{ET}_{\text{a}}\), were also investigated. All models presented the highest RMSE values in the sub-interval of 6-8 mm day\({}^{-1}\) (Figure 7). Except for the RF and bagging models, RMSE was always above 1.0 mm day\({}^{-1}\) for the other models in the sub-interval under all the scenarios. It even exceeded 1.5 mm day\({}^{-1}\) for the MLP model. In Scenarios 5 and 6, RMSE values even exceeded 2.0 mm day\({}^{-1}\) for the MLP model (Figure 7e,f). By contrast, in the sub-intervals of 0-2 mm day\({}^{-1}\) and 2-4 mm day\({}^{-1}\), all the models had a higher prediction accuracy (RMSE < 0.6 mm day\({}^{-1}\), except for Scenario 6, Figure 7f). In general, the RF and bagging models exhibited the highest stability with a lower RMSE increase (RMSE < 0.32-0.81 mm day\({}^{-1}\)) than the other models at all sub-intervals.