A dual-porosity extension of the Arya and Paris pedotransfer function for the hydraulic properties of structured soils

The primary objective of this paper is to create a bimodal pedotransfer function for predicting soil water retention (WRC) and hydraulic conductivity (HCC) curves. This new pedotransfer function (PTF) builds upon the Arya and Paris (AP) approach, which relies on particle size distribution (PSD), by integrating aggregate-size distribution (ASD) into the PTF to derive bimodal WRC. A bimodal porosity approach was formulated to quantify the proportions of each porous system (matrix and macropores) within the overall soil porosity. Saturated hydraulic conductivity, K0, was determined from WRC using the Kozeny-Carman equation, with parameters inferred from the behaviour of bimodal WRC near saturation. Subsequently, the Mualem model was applied to obtain the HCC. To calibrate the PTF, data on measured soil physical and hydraulic properties were utilised, originating from field infiltration experiments conducted in a 140-ha area of the "Sinistra Ofanto" irrigation system in Apulia, southern Italy. Infiltration data were fitted using both bimodal and unimodal hydraulic properties through an inverse solution of the Richards equation. The bimodal "measured" hydraulic properties were employed to calibrate the scaling parameter (αAP) of the proposed bimodal AP (bimAP) PTF. Similarly, for comparison with the bimodal results, the unimodal hydraulic properties were used to calibrate the αAP of the traditional unimodal AP (unimAP) PTF. In comparison to the unimAP PTF, the proposed bimAP. In contrast to the unimodal AP (unimAP) PTF, the suggested bimodal AP (bimAP) demonstrates a substantial enhancement in predicting mean soil water retention curve (WRC) parameters and saturated hydraulic conductivity (K0), as well as accurately capturing the overall shape of the hydraulic conductivity curve (HCC). Additionally, when compared to the unimodal approach, the bimodal approach proves effective in replicating statistical aspects of hydraulic parameters, such as variance, similar to those observed in the calibration dataset. Furthermore, a Multiple Linear Regression (MLR) analysis was conducted to assess the sensitivity of the bimodal parameter αAP to textural and structural characteristics, confirming the notable predictive influence of soil structure on the model.