Predicting saturated hydraulic conductivity from air permeability: Application in stochastic water infiltration modeling

Abstract

Several relationships exist for predicting unsaturated hydraulic conductivity K(ψ) from saturated hydraulic conductivity Ks and the soil-water retention curve. These relationships are convenient for modeling of field scale system sensitivity to spatial variability in K(ψ). It is, however, faster and simpler to measure air permeability ka at ψ = -100 cm H2O, than Ks. This study explores the existence of a general prediction relationship between ka, measured at -100 cm H2O, and Ks. Comparative analyses between ka-Ks relationships for nine Danish and Norwegian soils, six different soil treatments, and three horizons validated the establishment of a soil type, soil treatment, and depth/horizon independent log-log linear ka-Ks relationship. The general ka-Ks relationship is based on data from a total of 1614 undisturbed, 100-cm3 core samples and displays general prediction accuracy better than ±0.7 orders of magnitude. The accuracy and usefulness of the general relationship was evaluated through stochastic analyses of field scale infiltration and ponding during a rainstorm event. These analyses showed possible prediction bias associated with the general ka-Ks relationship, but also revealed that sampling uncertainty associated with estimation of field scale variability in Ks from a limited number of samples could easily be larger than the possible prediction bias.