Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten model

In many vadose zone hydrological studies, it is imperative that the soil's unsaturated hydraulic conductivity is known. Frequently, the Mualem-van Gen uchten model (MVG) is used for this purpose because it allows prediction of unsaturated hydraulic conductivity from water retention parameters. For th is and similar equations, it is often assumed that a measured saturated hyd raulic conductivity (K-5) can be used as a matching point (K-o) while a fac tor S-e(L) is used to account for pore connectivity and tortuosity (where S -e is the relative saturation and L = 0.5). 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 usi ng K-o = K-s and L = 0.5 resulted in a root mean square error for log(K) (R MSEK) of 1.31. Optimization of the matching point (K.) and L to the hydraul ic conductivity data yielded a RMSEK of 0.41. The fitted K-o were, on avera ge, about one order of magnitude smaller than measured K-5. Furthermore, L was predominantly negative, casting doubt that the MVG can be interpreted i n a physical way. Spearman rank correlations showed that both K-o and L wer e related to van Genuchten water retention parameters and neural network an alyses confirmed that K-o and L could indeed be predicted in this way. The corresponding RMSEK was 0.84, which was half an order of magnitude better t han the traditional MVG model. Bulk density and textural parameters were po or predictors while addition of K-5 improved the RMSEK only marginally. Boo tstrap analysis showed that the uncertainty in predicted unsaturated hydrau lic conductivity was about one order of magnitude near saturation and large r at lower water contents.