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Predicting saturated hydraulic conductivity from air permeability: Application in stochastic water infiltration modeling
Author(s) -
Loll Per,
Moldrup Per,
Schjønning Per,
Riley Hugh
Publication year - 1999
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/1999wr900137
Subject(s) - hydraulic conductivity , ponding , soil water , soil science , infiltration (hvac) , spatial variability , permeability (electromagnetism) , mathematics , hydrology (agriculture) , environmental science , statistics , geotechnical engineering , geology , chemistry , meteorology , geography , membrane , drainage , biology , ecology , biochemistry
Several relationships exist for predicting unsaturated hydraulic conductivity K (ψ) from saturated hydraulic conductivity K s 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 k a at ψ = −100 cm H 2 O, than K s . This study explores the existence of a general prediction relationship between k a , measured at −100 cm H 2 O, and K s . Comparative analyses between k a ‐ K s 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 k a ‐ K s relationship. The general k a ‐ K s relationship is based on data from a total of 1614 undisturbed, 100‐cm 3 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 k a ‐ K s relationship, but also revealed that sampling uncertainty associated with estimation of field scale variability in K s from a limited number of samples could easily be larger than the possible prediction bias.

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