## 2 Results
### The Performance of the rMLR Model  
The trained parameters were applied to the testing dataset, and the resulting model performance in simulating \(V_{\rm{cmax}}\), \(J_{\rm{max}}\), \(\phi_{\rm P}\), NPQ \(A_{\rm{net}}\), and G\({}_{\rm S}\) was quantified using linear regression analysis and described using the coefficient of determination (\(R^{2}\)) and the root mean squared error (RMSE) between the simulated and measured values. A relatively small decrease was observed in both \(V_{\rm{cmax}}\) and \(J_{\rm{max}}\) between WS1 and WS6 (Figure 6). As the drought continued, however, they dropped substantially, and were almost zero after WS10 (Figure 6). As water stress progressed, the rMLR model was able to track the decreasing trends in \(V_{\rm{cmax}}\), and \(J_{\rm{max}}\) well, explaining 83% (RMSE = 8.25 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), Figure 6a) and 79% (RMSE = 24.35%, Figure 6b) in their variance between WS1 and WS12. However, the model tended to underestimate the large values of \(V_{\rm{cmax}}\), and \(J_{\rm{max}}\) during WS1-WS6, and overestimate the small values between WS7 and WS9 (Figure 6). During the first week of the experiment, \(\phi_{\rm P}\) gradually declined from 1.0 to approximately 0.3 as PAR increased from 0 to 1000 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (Figure 7). As the drought intensified, \(\phi_{\rm P}\) became less responsive to PAR: \(\phi_{\rm P}\) decreased more steeply with increased PAR and leveled off over a broader range of light intensities (Figure 7). The simulated \(\phi_{\rm P}\) light-curve shapes were similar to those estimated from the fluorescence parameters: between WS1 and WS12, the model explained 94.3% of the variation in \(\phi_{\rm P}\) (RMSE = 0.07, Figure 7). Under severe water stress conditions (WS8-WS12), the model underestimated \(\phi_{\rm P}\), with \(R^{2}\) = 0.93 (RMSE = 0.08, Figure 7). Figure 6: (**a**) Comparisons of measured versus simulated \(V_{\rm{cmax}}\) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), \(V_{\rm{cmax}\,meas}\) vs. \(V_{\rm{cmax}\,model}\)) between 1 and 12 days after imposing drought stress; (**b**) comparisons of measured versus simulated \(J_{\rm{max}}\) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), \(J_{\rm{max}\,meas}\) vs. \(J_{\rm{max}\,model}\)) between 1 and 12 days after imposing drought stress. Datapoints and error bars represent the mean and standard deviation of four replicates. The NPQ light-response curves have an approximately opposite form to those of \(\phi_{\rm P}\). NPQ tended to saturate at lower PAR levels with an increase in the water deficit, and substantially decrease under severe stress (Figure 8). The proposed model was able to track the light response of NPQ well: \(R^{2}\) between measured and modelled NPQ reached a value of 0.97 (RMSE = 0.16) for the period between WS0 and WS12 (Figure 8). The decrease in \(\theta_{\rm SWC}\) had no obvious effect on the performance of the model in simulating NPQ, with \(R^{2}\) remaining at 0.97 between WS9 and WS12 (RMSE = 0.16, Figure 8). Despite the overall good performance, a small underestimation in NPQ was observed under high light intensities (PAR > 1700 umol m\({}^{-2}\) s\({}^{-1}\), Figure 8). The simulated response of \(A_{\rm net}\) to variations in light intensity shows a similar pattern to the response measured by the gas-exchange system: simulated \(A_{\rm net}\) increases rapidly with increasing illumination intensity at low light levels, and gradually reaches a plateau under high light conditions (Figure 9). The rMLR model is able to reproduce \(A_{\rm net}\) well under drought conditions; it accounted for 97.2% (RMSE = 1.532 umol m\({}^{-2}\) s\({}^{-1}\)) of the variability in \(A_{\rm net}\) from WS0 to WS12 (Figure 9).