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dc.contributor.authorXia, Chuan-An
dc.contributor.authorHu, Bill X.
dc.contributor.authorTong, Juxiu
dc.contributor.authorGuadagnini, Alberto
dc.date.accessioned2021-04-01T21:20:01Z
dc.date.available2021-04-01T21:20:01Z
dc.date.issued2018-09-13
dc.identifier.citationXia, C. A., Hu, B. X., Tong, J., & Guadagnini, A. (2018). Data assimilation in density‐dependent subsurface flows via localized iterative ensemble Kalman filter. Water Resources Research, 54(9), 6259-6281.en_US
dc.identifier.issn0043-1397
dc.identifier.doi10.1029/2017wr022369
dc.identifier.urihttp://hdl.handle.net/10150/657289
dc.description.abstractParameter estimation in variable-density groundwater flow systems is confronted with challenges of strong nonlinearity and heavy computational burden. Relying on a variant of the Henry problem, we evaluate the performance of a domain localization scheme of the iterative ensemble Kalman filter in the framework of data assimilation settings for variable-density groundwater flows in a seawater intrusion scenario. The performance of the approach is compared against (a) the corresponding domain localization scheme of the ensemble Kalman filter in its standard formulation as well as (b) a covariance localization scheme of the latter. The equivalent freshwater head, h(f), and salinity, (S)a, are set as the target state variables. The randomly heterogeneous field of equivalent freshwater hydraulic conductivity, K-f, is considered as the system parameter field. Density-independent and density-driven flow settings are considered to evaluate the assimilation results using various methods and data. When only hf data are assimilated, all tested approaches perform generally well and a localization scheme embedded in the iterative ensemble Kalman filter appears to consistently outperform the domain localized version of the standard ensemble Kalman filter (EnKF) in a density-driven scenario; Dirichlet boundary conditions tend to show a more pronounced negative effect on estimating K-f for density-independent than for density-dependent flow conditions; hf data are more informative in a density-dependent than in a density-independent setting. The sole use of Sa information does not yield satisfactory updates of hf for the covariance localization scheme of the standard EnKF, while the sole use of hf does. The domain localization scheme leads to difficulties in the attainment of global filter convergence when only S-a data are used. A covariance localization scheme associated with a standard EnKF can significantly alleviate this issue.en_US
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_US
dc.language.isoenen_US
dc.publisherAMER GEOPHYSICAL UNIONen_US
dc.rights© 2018. American Geophysical Union. All Rights Reserved.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectvariable density flowen_US
dc.subjectvalue of dataen_US
dc.subjectiterative ensemble Kalman filteren_US
dc.subjectensemble Kalman filteren_US
dc.titleData Assimilation in Density-Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filteren_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien_US
dc.identifier.journalWATER RESOURCES RESEARCHen_US
dc.description.note6 month embargo; first published online 13 September 2018en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleWater Resources Research
dc.source.volume54
dc.source.issue9
dc.source.beginpage6259
dc.source.endpage6281
refterms.dateFOA2019-03-13T00:00:00Z


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