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

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.

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