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Analysis of Measured, Predicted, and Estimated Hydraulic Conductivity Using the RETC Computer Program
Author(s) -
Yates S. R.,
Genuchten M. Th.,
Warrick A. W.,
Leij F. J.
Publication year - 1992
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1992.03615995005600020003x
Subject(s) - hydraulic conductivity , conductivity , soil water , scaling , soil science , nonlinear system , non linear least squares , mathematics , materials science , biological system , statistics , environmental science , estimation theory , chemistry , physics , geometry , quantum mechanics , biology
A nonlinear least‐squares optimization program, RETC, which uses empirical relationships for describing the water‐retention curve and predictive models for characterizing the unsaturated hydraulic conductivity relationship was used to analyze 36 unsaturated hydraulic conductivity distributions obtained from the literature for 23 different soils. By comparing the measured, predicted, and estimated relative conductivity for the group of data, the efficiency and accuracy of this approach for characterizing the soil hydraulic properties was determined. The analysis consisted of comparing measured, predicted, and estimated conductivities using three predictive methods and two simultaneous methods (which include known values of the conductivity). The results indicate that for this group of data, the best method for determining the model parameters that will accurately describe both the water retention and unsaturated hydraulic conductivity relationships is to use a simultaneous approach with either five or six parameters. It was also found that the predictive approach introduces a bias into the estimates of the unsaturated hydraulic conductivity and a predictive approach with scaling did not significantly improve the estimates of the unsaturated hydraulic conductivity.