## 5 Conclusions  
We evaluated the applicability of seven LUE models with typical water stress scalars for GPP simulation of a cork oak plantation in northern China and explored whether using PWSI to modify the above models could improve model performance. Compared to the temperature stress scalar, the water stress scalar has a significant impact on the performance of the LUE model. On sunny days, PWSI was more suitable than traditional water stress indicators for indicating the relationship between water limitation and photosynthesis. After using PWSI to modify the water stress scalar, the performance of all LUE models was improved. However, on cloudy days, the performance of the modified LUE models did not improve, and the EF was more suitable for indicating the limitation of water stress on photosynthesis. The modified GLO-PEM and ECM-LUE models had the optimal model structure for GPP simulation of cork oak plantations in northern China on sunny and cloudy days, respectively. This study provides a reference for simulating forest GPP in other regions.  
Conceptualization, L.L., J.Z. and X.G.; Methodology, L.L., H.H., B.C. and Y.B.; Software, L.L., [PERSON] and Y.Z.; Validation, L.L.; Writing--original draft preparation, [PERSON] and X.G.; Writing--review and editing, [PERSON], [PERSON] and J.C.; Visualization, [PERSON], X.G. and [PERSON]; Supervision: [PERSON] and [PERSON]; Funding acquisition, [PERSON] and X.G. [PERSON] and [PERSON] contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This study was sponsored by the National Key Research and Development Project (grant no. 2020 YFA0608101) and the National Nonprofit Institute Research Grant of the Chinese Academy of Forestry (CAFYBB2022 SY001, CAFYBB2018 ZA001, CAFZC2017M005).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that the data needs to be used in much future work.  
We thank [PERSON] from the Henan Agricultural University for advising. We thank [PERSON] from Nanshan Forest Farm, Jiyuan City for contributing to field measurements. We also appreciate the editors and three anonymous reviewers for their constructive suggestions and comments that improved the manuscript.  
The authors declare no conflict of interest.  
Appendix AFigure 2: Seasonal variations of gross primary production (GPP) simulated by MOD17\({}_{\text{pwsi}}\) and MODTEM\({}_{\text{pwsi}}\) (**a**), EC-LUE\({}_{\text{pwsi}}\) and ECM-LUE\({}_{\text{pwsi}}\) (**b**), SM-LUE\({}_{\text{pwsi}}\) and [PERSON]\({}_{\text{pwsi}}\) (**c**), GLO-PEM\({}_{\text{pwsi}}\) (**d**), and ECM-LUE+GLO-PEM\({}_{\text{pwsi}}\) (**e**) models and observed GPP. ECM-LUE+GLO-PEM\({}_{\text{pwsi}}\) means the combination of ECM-LUE model on cloudy days and the GLO-PEM\({}_{\text{pwsi}}\) model on sunny days. The subscript plant water stress index (PWSI) indicates that PWSI was used to modify the water stress scalar in the LUE model. Solid dots represent cloudy days, and the open circle dots represent sunny days. Data from June–September 2020 and May–June 2021.  
Figure 11: Seasonal variations of gross primary production (GPP) simulated by MOD17 and MOD-TEM (**a**), EC-LUE and ECM-LUE (**b**), SM-LUE and Wang (**c**), GLO-PEM (**d**), and ECM-LUE+GLO-PEM\({}_{\text{p}usi}\) (**e**) models and observed GPP. ECM-LUE+GLO-PEM\({}_{\text{p}usi}\) means the combination of ECM-LUE model on cloudy days and the GLO-PEM\({}_{\text{p}usi}\) model on sunny days. The subscript plant water stress index (PWSI) indicates that PWSI was used to modify the water stress scalar in the LUE model. Data from July–September 2021 and May–June 2022.