Pedotransfer functions for solute transport parameters of Portuguese soils

Summary The purpose of this study is to quantify solute transport parameters of fine‐textured soils in an irrigation district in southern Portugal and to investigate their prediction from basic soil properties and unsaturated hydraulic parameters. Solute displacement experiments were carried out on 24 undisturbed soil samples by applying a 0.05 m KCl pulse during steady flow. The chloride breakthrough curves (BTCs) were asymmetric, with early breakthrough and considerable tailing characteristic of non‐equilibrium transport. The retardation factor (R), dispersion coefficient (D), partitioning coefficient (β), and mass transfer coefficient (ω) were estimated by optimizing the solution of the non‐equilibrium convection–dispersion equation (CDE) to the breakthrough data. The solution could adequately describe the observed data as proved by a median of 0.972 for the coefficient of determination (r2) and a median for the mean squared error (MSE) of 5.1 × 10−6. The median value for R of 0.587 suggests that Cl– was excluded from a substantial part of the liquid phase. The value for β was typically less than 0.5, but the non‐equilibrium effects were mitigated by a large mass transfer coefficient (ω > 1). Pedotransfer functions (PTFs) were developed with regression and neural network analyses to predict R, D, β and ω from basic soil properties and unsaturated hydraulic parameters. Fairly accurate predictions could be obtained for logD (r2 ≈ 0.9) and β (r2 ≈ 0.8). Prediction for R and logω were relatively poor (r2 ≈ 0.5). The artificial neural networks were all somewhat more accurate than the regression equations. The networks are also more suitable for predicting transport parameters because they require only three input variables, whereas the regression equations contain many predictor variables.