Improved Prediction of Unsaturated Hydraulic Conductivity with the Mualem‐van Genuchten Model

Abstract

In many vadose zone hydrological studies, it is imperative that the soil's unsaturated hydraulic conductivity is known. Frequently, the Mualem–van Genuchten model (MVG) is used for this purpose because it allows prediction of unsaturated hydraulic conductivity from water retention parameters. For this and similar equations, it is often assumed that a measured saturated hydraulic conductivity (Ks) can be used as a matching point (Ko) while a factor SLe is used to account for pore connectivity and tortuosity (where Se is the relative saturation and ). We used a data set of 235 soil samples with retention and unsaturated hydraulic conductivity data to test and improve predictions with the MVG equation. The standard practice of using and resulted in a root mean square error for log(K) (RMSEK) of 1.31. Optimization of the matching point (Ko) and L to the hydraulic conductivity data yielded a RMSEK of 0.41. The fitted Ko were, on average, about one order of magnitude smaller than measured Ks Furthermore, L was predominantly negative, casting doubt that the MVG can be interpreted in a physical way. Spearman rank correlations showed that both Ko and L were related to van Genuchten water retention parameters and neural network analyses confirmed that Ko and L could indeed be predicted in this way. The corresponding RMSEK was 0.84, which was half an order of magnitude better than the traditional MVG model. Bulk density and textural parameters were poor predictors while addition of Ks improved the RMSEK only marginally. Bootstrap analysis showed that the uncertainty in predicted unsaturated hydraulic conductivity was about one order of magnitude near saturation and larger at lower water contents.