Improving Agro-Hydrological Process Simulations in Cropping Systems by Coupling WOFOST and HYDRUS-1D with Data Assimilation

Integrated simulation of crop growth and soil water dynamics is essential for improving the understanding of agro-hydrological processes and advancing agricultural water resource management. In this study, a coupled agro-hydrological modeling framework was developed by integrating the crop growth model WOFOST with the soil water flow model HYDRUS-1D to explicitly represent interactions among crop development, root water uptake, and soil moisture dynamics. The framework was applied to a maize cropping system located in an arid and semi-arid region characterized by shallow groundwater, where strong soil–crop–atmosphere interactions and groundwater influences pose significant challenges to conventional modeling approaches. Model parameters were calibrated and validated using field observations collected during the 2017–2018 growing seasons, incorporating site-specific climate data, cultivar parameters, and detailed agricultural management information. To address uncertainties arising from parameter variability and model structural limitations, data assimilation techniques were further embedded into the coupled framework. Observations of soil water content (SWC), leaf area index (LAI), and evapotranspiration (ET) were assimilated using the Ensemble Kalman Filter (EnKF) and four-dimensional variational data assimilation (4D-Var), enabling dynamic correction of both soil hydrological states and crop growth variables. The results demonstrate that the coupled WOFOST–HYDRUS-1D system reliably captures crop–soil–groundwater interactions under shallow groundwater conditions. Data assimilation substantially improves simulation accuracy by reducing soil moisture bias, constraining crop growth trajectories, enhancing ET estimation, and lowering predictive uncertainty throughout the growing season. The proposed framework provides a robust and potentially transferable tool for agro-hydrological simulation in water-scarce regions and supports improved irrigation management and decision-making in precision agriculture.