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dc.contributor.authorKhaki, M.
dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorForootan, E.
dc.contributor.authorAwange, J.
dc.contributor.authorKuhn, M.
dc.date.accessioned2018-08-27T08:20:12Z
dc.date.available2018-08-27T08:20:12Z
dc.date.issued2018-07-05
dc.identifier.citationKhaki M, Ait-El-Fquih B, Hoteit I, Forootan E, Awange J, et al. (2018) Unsupervised ensemble Kalman filtering with an uncertain constraint for land hydrological data assimilation. Journal of Hydrology 564: 175–190. Available: http://dx.doi.org/10.1016/j.jhydrol.2018.06.080.
dc.identifier.issn0022-1694
dc.identifier.doi10.1016/j.jhydrol.2018.06.080
dc.identifier.urihttp://hdl.handle.net/10754/628078
dc.description.abstractThe standard ensemble data assimilation schemes often violate the dynamical balances of hydrological models, in particular, the fundamental water balance equation, which relates water storage and water flux changes. The present study aims at extending the recently introduced Weak Constrained Ensemble Kalman Filter (WCEnKF) to a more general framework, namely unsupervised WCEnKF (UWCEnKF), in which the covariance of the water balance model is no longer known, thus requiring its estimation along with the model state variables. This extension is introduced because WCEnKF was found to be strongly sensitive to the (manual) choice of this covariance. The proposed UWCEnKF, on the other hand, provides a more general unsupervised framework that does not impose any (manual, thus heuristic) value of this covariance, but suggests an estimation of it, from the observations, along with the state. The new approach is tested based on numerical experiments of assimilating Terrestrial Water Storage (TWS) from Gravity Recovery and Climate Experiment (GRACE) and remotely sensed soil moisture data into a hydrological model. The experiments are conducted over different river basins, comparing WCEnKF, UWCEnKF, and the standard EnKF. In this setup, the UWCEnKF constrains the system state variables with TWS changes, precipitation, evaporation, and discharge data to balance the summation of water storage simulations. In-situ groundwater and soil moisture measurements are used to validate the results of the UWCEnKF and to evaluate its performances against the EnKF. Our numerical results clearly suggest that the proposed framework provides more accurate estimates of groundwater storage changes and soil moisture than WCEnKF and EnKF over the different studied basins.
dc.description.sponsorshipM. Khaki is grateful for the research grant of Curtin International Postgraduate Research Scholarships (CIPRS)/ORD Scholarship provided by Curtin University (Australia). This work is a TIGeR publication.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S002216941830502X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, [, , (2018-07-05)] DOI: 10.1016/j.jhydrol.2018.06.080 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectConstrained data assimilation
dc.subjectEnsemble Kalman Filter (EnKF)
dc.subjectUnsupervised Weak Constrained Ensemble Kalman Filter (UWCEnKF)
dc.subjectWater budget closure
dc.subjectHydrological modeling
dc.titleUnsupervised ensemble Kalman filtering with an uncertain constraint for land hydrological data assimilation
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalJournal of Hydrology
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Engineering, University of Newcastle, Callaghan, New South Wales, Australia
dc.contributor.institutionSchool of Earth and Planetary Sciences, Discipline of Spatial Sciences, Curtin University, Perth, Australia
dc.contributor.institutionSchool of Earth and Ocean Sciences, Cardiff University, Cardiff, UK
kaust.personAit-El-Fquih, Boujemaa
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-08-27T12:45:11Z
dc.date.published-online2018-07-05
dc.date.published-print2018-09


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