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Estimation of Brooks‐Corey Parameters from water retention data
Author(s) -
Milly P. C. D.
Publication year - 1987
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/wr023i006p01085
Subject(s) - maxima and minima , mathematics , set (abstract data type) , function (biology) , nonlinear system , estimation theory , data set , realization (probability) , least squares function approximation , non linear least squares , algorithm , statistics , computer science , mathematical analysis , physics , quantum mechanics , evolutionary biology , estimator , biology , programming language
The parameters of the Brooks‐Corey soil moisture characteristic may be determined by either graphical or automatic numerical procedures. A popular log‐log procedure for automatic estimation leads to a linear least squares optimization problem. Apparently, it has been applied incorrectly in the past when all measurements were used for parameter estimation, and this realization explains the inconsistencies in some published data. Its proper application generally requires the solution of more than one linear problem for each data set. Alternatively, a nonlinear procedure will yield parameter estimates directly. In either case there may exist multiple local minima in the sum of squared deviations between model and measurements, and the parameter estimates so derived are demonstrably unreliable; this appears to be a result of the distinct air‐entry suction implicit in the Brooks‐Corey model and usually absent in field‐measured characteristic curves. A new procedure based on an integral objective function overcomes the problems cited.