Modeling root water uptake of landscape groundcovers with HYDRUS-1D and particle swarm optimization

Accurate parameterization of root water uptake (RWU) models for landscape groundcovers is essential for estimating soil water balance components. In this study, HYDRUS-1D was coupled with the Particle Swarm Optimization (PSO) algorithm to calibrate Feddes RWU parameters for four groundcover species— Acacia redolens , Arctotis acaulis , Chrysanthemoides incana , and Lippia nodiflora —using two inverse modeling scenarios. In Scenario I, a two-step approach was applied in which soil hydraulic parameters were optimized first, followed by RWU parameters using two independent datasets. In Scenario II, both parameter sets were calibrated simultaneously. Validation results for Scenario I demonstrated satisfactory model performance in simulating soil water content (SWC) dynamics at shallow depths (10 and 30 cm). However, significant deviations at deeper layers (50 and 75 cm) reduced Kling–Gupta efficiency (KGE) and coefficient of determination (R²) values for Arctotis acaulis (KGE = 0.68, R² = 0.49), Chrysanthemoides incana (0.64, 0.53), Lippia nodiflora (0.67, 0.79), and Acacia redolens (0.84, 0.72). In contrast, Scenario II substantially reduced the deviation between simulated and observed SWC, achieving KGE and R² values above 0.90 for all groundcovers except Arctotis acaulis (KGE = 0.86, R² = 0.80). These findings demonstrate that simultaneous optimization of RWU and soil hydraulic parameters significantly enhances HYDRUS-1D performance and enables more accurate simulation of SWC dynamics. • HYDRUS-1D was coupled with the particle swarm optimization (PSO) algorithm. • Root water uptake and soil hydraulic parameters were optimized simultaneously. • HYDRUS-1D accurately estimated soil moisture, water storage, and actual evapotranspiration vs field data. • The study provides a practical framework to simulate root-zone water dynamics in urban landscapes.