https://doi.org/10.1016/j.agrformet.2025.110700

Crop models depend on field experiments to provide a numerical representation of the complex interactions between crops, soil, and the atmosphere. However, when applied at a regional scale, these models require parameter recalibration to maintain their performance and accuracy. Here, we comprehensively evaluated the performance of a crop model, AquaCrop-OS, across the agricultural region of Saskatchewan from 1981 to 2016. The model was configured with 167 0.5° × 0.5° grids and initialized by daily meteorological data. To realistically reproduce soil moisture conditions in early spring, we coupled the SHAW (Simultaneous Heat and Water) model with AquaCrop-OS to physically simulate heat and water fluxes based on cultivation types, snow accumulation, and soil hydrological characteristics. The results show that the performance of AquaCrop-OS is improved significantly by coupling with SHAW. The degree of improvement varies depending on different climate conditions. In extreme drought years (Standardized Precipitation Evapotranspiration Index (SPEI)<-2), the normalized root-mean-square error (NRMSE) is reduced by 60 %. In moderate drought (-2

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