Water scarcity in semi-arid regions necessitates accurate soil water modeling to optimize irrigation management. This study compares three HYDRUS-1D parameterization approaches-based on the drying-branch soil water retention curve (SWRC), wetting-branch SWRC (using Shani's drip method), and inverse modeling-to simulating soil water content at 15 cm and 45 cm depths under center-pivot irrigation in a semi-arid region. Field experiments in three maize fields provided daily soil water, soil hydraulic, and meteorological data. Inverse modeling achieved the highest accuracy (NRMSE: 2.29-7.40%; RMSE: 0.006-0.023 cm3 cm-3), particularly at 15 cm depth, by calibrating van Genuchten parameters against observed water content. The wetting-branch approach outperformed the drying branch at the same depth, capturing irrigation-induced wetting processes more effectively. Statistical validation confirmed the robustness of inverse modeling in reproducing temporal patterns, while wetting-branch data improved deep-layer accuracy. The results demonstrate that inverse modeling is a reliable approach for soil water simulation and irrigation management, whereas the wetting-branch parameterization offers a practical, field-adaptable alternative. This study provides one of the first side-by-side evaluations of these three modeling approaches under real-world semi-arid conditions.