ABSTRACT Solute transport dynamics in agricultural soils critically influence water quality, nutrient availability, and environmental sustainability. Intensive agricultural practices exacerbate risks of groundwater contamination through solute leaching, demanding robust predictive tools. This study addresses this need by developing an open‐source numerical framework to simulate coupled water flow and solute transport in variably saturated soils, targeting agricultural applications. Governing equations—Richards equation for unsaturated flow and the Advection‐Dispersion Equation (ADE) for solute transport—were discretized using the Crank‐Nicolson Finite Difference Method (FDM). Python libraries (NumPy, SciPy, and Matplotlib) enabled efficient implementation. The discretized equations were solved with iterative methods. Soil hydraulic properties were parameterized via van Genuchten model, and root water uptake was incorporated as a depth‐dependent function. Rigorous validation was performed against the Ogata‐Banks analytical solution (ADE) and benchmark infiltration cases (Richards equation). The model demonstrated high accuracy (, ) in validation tests, alongside mass balance errors cm for water flow. The study establishes the Python‐based framework as a transparent, adaptable tool for simulating solute fate in agricultural systems. Key findings demonstrate that crops with low water retention require frequent irrigation but face higher leaching risks, while high‐retention soils benefit from deeper, less frequent watering. Future work will extend the model to multi‐dimensional domains, incorporate dynamic climate effects and preferential flow, and conduct scenario‐based uncertainty analysis.
Alamirew et al. (2026) studied this question.
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