Calibration of AquaCrop-OSPy Model for Greenhouse Tomato Under High Temperature Conditions Based on Whale Optimization Algorithm

Calibration of crop model parameters using optimization algorithms can substantially improve model performance, particularly under different environmental scenarios. Here, we take greenhouse drip-irrigated tomato as an example. Two temperature treatments (high temperature, TH: 35 °C ≤ Tmax ≤ 40 °C; non-high temperature, TD: Tmax ≤ 35 °C) and two water treatments (well watered, WH: 100%Epan; water deficit, WD: 60%Epan, where Epan is the pan evaporation coefficient) were combined and implemented in 2024 and 2025. Canopy cover, yield, aboveground biomass, and water consumption of tomatoes were measured, and then a global sensitivity analysis was conducted using the extended Fourier amplitude sensitivity test (EFAST). Thereafter, in order to evaluate the performance of AquaCrop-OSPy under different combinations of temperature and water conditions, the whale optimization algorithm (WOA) was coupled with the AquaCrop-OSPy model, and an automatic parameter optimization framework was developed using the aforementioned measured indicators as objective functions. The results showed that CCx, Senescence_CD, CGC_CD, WP, HI0, and Kcb were highly sensitive parameters of the AquaCrop-OSPy model across different temperature and water treatments. Compared with the traditional trial and error (TAE) method, WOA demonstrated superior global optimization capability and significantly improved model performance. Validation indicated that the root mean square error (RMSE) was ≤4.52% for canopy cover, ≤0.67 t/hm2 for yield, and ≤0.98 t/hm2 for aboveground biomass. Notably, even after WOA optimization, water consumption simulation under combined high temperature and water deficit conditions still showed some deviation, which was attributed to limitations in soil water simulation and the oversimplification of model mechanisms under combined stress. Nevertheless, the model remained reliable for estimating canopy cover, yield, and biomass, while ET simulation under combined stress should be interpreted with caution. Therefore, future improvements of AquaCrop-OSPy should focus on addressing the effects of soil water and refining the inhibitory feedback of physiological stress to better adapt the model to the combined conditions of high temperature and water deficit.