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@ARTICLE{Mertens:5538,
      author       = {Mertens, J. and Kahl, G. and Gottesbüren, B. and
                      Vanderborght, J.},
      title        = {{I}nverse {M}odeling of {P}esticide {L}eaching in
                      {L}ysimeters: {L}ocal versus {G}lobal and {S}equential
                      {S}ingle-{O}bjective versus {M}ultiobjective {A}pproaches},
      journal      = {Vadose zone journal},
      volume       = {8},
      issn         = {1539-1663},
      address      = {Madison, Wis.},
      publisher    = {SSSA},
      reportid     = {PreJuSER-5538},
      pages        = {793 - 804},
      year         = {2009},
      note         = {We are grateful to Dr. Jasper Vrugt for kindly providing us
                      with the SCEM MATLAB code. Th is research was supported by
                      BASF SE and a research grant of the Katholieke Universiteit
                      Leuven, Belgium (GOA-06-07-TBA).},
      abstract     = {This study used field lysimeter leachate and pesticide
                      concentration data within an inverse modeling framework to
                      estimate pesticide degradation and sorption on parameters.
                      Experimental data comprising four pesticide applications
                      during 3 yr were used to compare a local parameter
                      estimation algorithm (Levenberg-Marquardt, LM) with a global
                      algorithm (Shuffled Complex Evolution on Metropolis, SCEM).
                      Good model fits (only marginally better model fits using
                      SCEM) with respect to both the observed leachate volumes and
                      corresponding pesticide concentrations were obtained using
                      both algorithms. Parameter optima found with LM and SCEM
                      were very similar, thus suggesting that LM correctly located
                      the global optimum for our experimental data. Equally as
                      important as the optimal parameter values, however, are the
                      estimated parameter uncertainities. This study revealed that
                      LM (using a Jacobian-based approach) provided too large
                      parameter uncertainities. A logarithmic transformation of
                      the parameter tended to decrease the uncertainty in most
                      cases. The overestimation of parameter uncertainty by LM
                      suggests that model sensitivity close to the optimal
                      parameter set was relatively small and underestimated the
                      sensitivity to large parameter changes. A multiobjective
                      Pareto analysis was subsequently compared with a sequential
                      single-objective approach to reveal the capability of the
                      multiobjective approach to verify model structure and model
                      concept. Our results indicate that a multiobjective SCEM
                      approach is recommended when the objective is to estimate
                      pesticide degradation and sorption parameters and their
                      uncertainty.},
      keywords     = {J (WoSType)},
      cin          = {ICG-4},
      ddc          = {550},
      cid          = {I:(DE-Juel1)VDB793},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Environmental Sciences / Soil Science / Water Resources},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000268871900026},
      doi          = {10.2136/vzj2008.0029},
      url          = {https://juser.fz-juelich.de/record/5538},
}