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Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter

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Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter

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dc.contributor.author Li, Liangping es_ES
dc.contributor.author Zhou, Haiyan es_ES
dc.contributor.author Hendricks-Franssen, Hendrikus Johannes es_ES
dc.contributor.author Gómez-Hernández, J. Jaime es_ES
dc.date.accessioned 2013-07-05T10:28:48Z
dc.date.available 2013-07-05T10:28:48Z
dc.date.issued 2012
dc.identifier.issn 1027-5606
dc.identifier.uri http://hdl.handle.net/10251/30679
dc.description.abstract [EN] The normal-score ensemble Kalman filter (NS-EnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i) the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii) localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii) the NS-EnKF works equally well under very different flow configurations. © Author(s) 2012. es_ES
dc.description.sponsorship The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. The two anonymous reviewers are gratefully acknowledged for their comments which helped improving the final version of the manuscript. en_EN
dc.language Inglés es_ES
dc.publisher European Geosciences Union (EGU) es_ES
dc.relation.ispartof Hydrology and Earth System Sciences and Discussions es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Bimodal distribution es_ES
dc.subject Connectivity pattern es_ES
dc.subject Ensemble Kalman Filter es_ES
dc.subject Flow configurations es_ES
dc.subject Flow problems es_ES
dc.subject Inverse modeling es_ES
dc.subject Performance assessment es_ES
dc.subject Pie- zometric Head es_ES
dc.subject Random function model es_ES
dc.subject Synthetic aquifers es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.5194/hess-16-573-2012
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CGL2011-23295/ES/MODELACION ESTOCASTICA INVERSA FUERA DE LO NORMAL/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Li, L.; Zhou, H.; Hendricks-Franssen, HJ.; Gómez-Hernández, JJ. (2012). Groundwater flow inverse modeling in non-MultiGaussian media: Performance assessment of the normal-score Ensemble Kalman Filter. Hydrology and Earth System Sciences and Discussions. 16(2):573-590. https://doi.org/10.5194/hess-16-573-2012 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.5194/hess-16-573-2012 es_ES
dc.description.upvformatpinicio 573 es_ES
dc.description.upvformatpfin 590 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 233948
dc.contributor.funder Ministerio de Ciencia e Innovación
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