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 ( K s ) can be used as a matching point ( K o ) while a factor S L e is used to account for pore connectivity and tortuosity (where S e 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 ) (RMSE K ) of 1.31. Optimization of the matching point ( K o ) and L to the hydraulic conductivity data yielded a RMSE K of 0.41. The fitted K o were, on average, about one order of magnitude smaller than measured K s Furthermore, L was predominantly negative, casting doubt that the MVG can be interpreted in a physical way. Spearman rank correlations showed that both K o and L were related to van Genuchten water retention parameters and neural network analyses confirmed that K o and L could indeed be predicted in this way. The corresponding RMSE K 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 K s improved the RMSE K 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.