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dc.contributor.authorZhang XF
dc.contributor.authorvan Eijkeren JCH
dc.contributor.authorHeemink AW
dc.date.accessioned2014-01-17T13:17:48
dc.date.issued1994-06-30
dc.identifier959101008
dc.description.abstractAs part of a research program directed towards the development of data assimiliation procedures for environmental models, in this report kriging techniques to integrate models and monitoring networks are studied. Simulated data are obtained from a random concentration field Z(x,t) which is generated as a sum of a deterministic component mu (x,t) giving the main trend of Z (x,t) and a stochastic component e (x) giving the natural variation of Z (x,t) around mu (x,t) with zero mean, constant variance and high spatial correlation. The deterministic part mu (x,t) with known mu (x,0) is formed as a superposition of a constant background concentration field and a rotating cone interpreted as the representation of a local pollution. To integrate models and data simple kriging and universal kriging techniques are implemented and compared. In each experiment the number of observations N is varied, as well as the spatial observation pattern which may be regular or irregular. Every case is subdivided into the situations that the spatial correlation structure, i.e. the semivariogram, is known or not. The method for fitting a semivariogram model is also investigated. In particular, some drawbacks existing in a popularly used cost criterion of weighted least squares method for fitting a semivariogram model are pointed out, and a new cost criterion is proposed. The simulation study illustrates the advantages of the proposed new cost criterion. Further, for a specified underlying realization of a stochastic process, different semivariogram models are employed for kriging. Since the real concentration is known in all these cases, the data assimilation methods are quantitatively compared with the real concentration field. In the next research phases emphasis will be on the elaboration of data assimilation procedures, the testing of their applicability in practice under various conditions, and if necessary and feasible the incorporation of more physics-based information into procedures.<br>
dc.description.sponsorshipRIVM
dc.format.extent29 p
dc.language.isoen
dc.publisherRijksinstituut voor Volksgezondheid en Milieu RIVM
dc.relation.ispartofRIVM Rapport 959101008
dc.relation.urlhttp://www.rivm.nl/bibliotheek/rapporten/959101008.html
dc.subject05nl
dc.subjectgrondwaternl
dc.subjectkwaliteitnl
dc.subjectwiskundig modelnl
dc.subjectgegevensbestandnl
dc.subjectverwerkennl
dc.subjectgroundwateren
dc.subjectqualityen
dc.subjectmodellingen
dc.subjectdata processingen
dc.subjectoptimum interpolationen
dc.subjectweighted least squaresen
dc.subjectsemi-variogram model fittingen
dc.subjectnon-stationarityen
dc.subjectoptimum interpolatieen
dc.subjectgewogen kleinste kwadratenen
dc.subjectsemi-variogram model fittenen
dc.subjectniet-stationariteiten
dc.titleData Assimilation in Groundwater Quality Models Using Kriging. Part Ien
dc.title.alternativeDataverwerking in grondwaterkwaliteitsmodellen met behulp van Kriging. Deel Inl
dc.typeReport
dc.contributor.departmentTUD
dc.contributor.departmentCWM
dc.date.updated2014-01-17T12:20:06Z
html.description.abstractAs part of a research program directed towards the development of data assimiliation procedures for environmental models, in this report kriging techniques to integrate models and monitoring networks are studied. Simulated data are obtained from a random concentration field Z(x,t) which is generated as a sum of a deterministic component mu (x,t) giving the main trend of Z (x,t) and a stochastic component e (x) giving the natural variation of Z (x,t) around mu (x,t) with zero mean, constant variance and high spatial correlation. The deterministic part mu (x,t) with known mu (x,0) is formed as a superposition of a constant background concentration field and a rotating cone interpreted as the representation of a local pollution. To integrate models and data simple kriging and universal kriging techniques are implemented and compared. In each experiment the number of observations N is varied, as well as the spatial observation pattern which may be regular or irregular. Every case is subdivided into the situations that the spatial correlation structure, i.e. the semivariogram, is known or not. The method for fitting a semivariogram model is also investigated. In particular, some drawbacks existing in a popularly used cost criterion of weighted least squares method for fitting a semivariogram model are pointed out, and a new cost criterion is proposed. The simulation study illustrates the advantages of the proposed new cost criterion. Further, for a specified underlying realization of a stochastic process, different semivariogram models are employed for kriging. Since the real concentration is known in all these cases, the data assimilation methods are quantitatively compared with the real concentration field. In the next research phases emphasis will be on the elaboration of data assimilation procedures, the testing of their applicability in practice under various conditions, and if necessary and feasible the incorporation of more physics-based information into procedures.&lt;br&gt;


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