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@INPROCEEDINGS{Qu:186394,
      author       = {Pachepsky, Yakov and Huisman, Johan Alexander and Martinez,
                      Gonzalo and Bogena, Heye and Vereecken, Harry},
      collaboration = {Qu, Wei},
      title        = {{M}ultivariate {D}istributions of {S}oil {H}ydraulic
                      {P}arameters},
      reportid     = {FZJ-2015-00470},
      year         = {2014},
      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.},
      month         = {Apr},
      date          = {2014-04-27},
      organization  = {EGU, Vienna (Austria), 27 Apr 2014 - 2
                       May 2014},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246) / 255 - Terrestrial Systems:
                      From Observation to Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF2-246 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/186394},
}