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| Hauptseite > Publikationsdatenbank > Multivariate Distributions of Soil Hydraulic Parameters |
| Hauptseite > Publikationsdatenbank > Multivariate Distributions of Soil Hydraulic Parameters |
| Poster | FZJ-2015-00470 |
; ; ; ; ;
2014
Please use a persistent id in citations: http://hdl.handle.net/2128/9164
Abstract: Statistical distributions of soil hydraulic parameters have to be known when synthetic fields of soil hydraulic prop-erties need to be generated in ensemble modeling of soil water dynamics and soil water content data assimilation.Pedotransfer functions that provide statistical distributions of water retention and hydraulic conductivity parame-ters for textural classes are most often used in the parameter field generation. Presence of strong correlations cansubstantially influence the parameter generation results. The objective of this work was to review and evaluateavailable data on correlations between van Genuchten-Mualem (VGM) model parameters. So far, two different ap-proaches were developed to estimate these correlations. The first approach uses pedotransfer functions to generateVGM parameters for a large number of soil compositions within a textural class, and then computes parameter cor-relations for each of the textural classes. The second approach computes the VGM parameter correlations directlyfrom parameter values obtained by fitting VGM model to measured water retention and hydraulic conductivitydata for soil samples belonging to a textural class. Carsel and Parish (1988) used the Rawls et al. (1982) pedo-transfer functions, and Meyer et al. (1997) used the Rosetta pedotransfer algorithms (Schaap, 2002) to developcorrelations according to the first approach. We used the UNSODA database (Nemes et al. 2001), the US SouthernPlains database (Timlin et al., 1999), and the Belgian database (Vereecken et al., 1989, 1990) to apply the secondapproach. A substantial number of considerable (>0.7) correlation coefficients were found. Large differences wereencountered between parameter correlations obtained with different approaches and different databases for thesame textural classes. The first of the two approaches resulted in generally higher values of correlation coefficientsbetween VGM parameters. However, results of the first approach application depend on pedotransfer relationshipsnot only within a given textural class but also on pedotransfer relationships within other textural classes since thepedotransfer relationships are developed across the database containing data for several textural classes. Therefore,joint multivariate parameter distributions for a specific class may not be sufficiently accurate. Currently PTF maygive the best prediction of the parameter itself, but they are not designed to estimate correlations between parame-ters. Covariance matrices for soil hydraulic parameters present an additional type of pedotransfer information thatneeds to be acquired and used whenever random sets of those parameters are to be generated.
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