An adaptive hybrid EnKF-OI scheme for efficient state-parameter estimation of reactive contaminant transport models

Handle URI:
http://hdl.handle.net/10754/563724
Title:
An adaptive hybrid EnKF-OI scheme for efficient state-parameter estimation of reactive contaminant transport models
Authors:
El Gharamti, Mohamad ( 0000-0002-7229-8366 ) ; Valstar, Johan R.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Reactive contaminant transport models are used by hydrologists to simulate and study the migration and fate of industrial waste in subsurface aquifers. Accurate transport modeling of such waste requires clear understanding of the system's parameters, such as sorption and biodegradation. In this study, we present an efficient sequential data assimilation scheme that computes accurate estimates of aquifer contamination and spatially variable sorption coefficients. This assimilation scheme is based on a hybrid formulation of the ensemble Kalman filter (EnKF) and optimal interpolation (OI) in which solute concentration measurements are assimilated via a recursive dual estimation of sorption coefficients and contaminant state variables. This hybrid EnKF-OI scheme is used to mitigate background covariance limitations due to ensemble under-sampling and neglected model errors. Numerical experiments are conducted with a two-dimensional synthetic aquifer in which cobalt-60, a radioactive contaminant, is leached in a saturated heterogeneous clayey sandstone zone. Assimilation experiments are investigated under different settings and sources of model and observational errors. Simulation results demonstrate that the proposed hybrid EnKF-OI scheme successfully recovers both the contaminant and the sorption rate and reduces their uncertainties. Sensitivity analyses also suggest that the adaptive hybrid scheme remains effective with small ensembles, allowing to reduce the ensemble size by up to 80% with respect to the standard EnKF scheme. © 2014 Elsevier Ltd.
KAUST Department:
Earth Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group; Earth Sciences and Engineering Program
Publisher:
Elsevier BV
Journal:
Advances in Water Resources
Issue Date:
Sep-2014
DOI:
10.1016/j.advwatres.2014.05.001
Type:
Article
ISSN:
03091708
Appears in Collections:
Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorEl Gharamti, Mohamaden
dc.contributor.authorValstar, Johan R.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-03T12:07:52Zen
dc.date.available2015-08-03T12:07:52Zen
dc.date.issued2014-09en
dc.identifier.issn03091708en
dc.identifier.doi10.1016/j.advwatres.2014.05.001en
dc.identifier.urihttp://hdl.handle.net/10754/563724en
dc.description.abstractReactive contaminant transport models are used by hydrologists to simulate and study the migration and fate of industrial waste in subsurface aquifers. Accurate transport modeling of such waste requires clear understanding of the system's parameters, such as sorption and biodegradation. In this study, we present an efficient sequential data assimilation scheme that computes accurate estimates of aquifer contamination and spatially variable sorption coefficients. This assimilation scheme is based on a hybrid formulation of the ensemble Kalman filter (EnKF) and optimal interpolation (OI) in which solute concentration measurements are assimilated via a recursive dual estimation of sorption coefficients and contaminant state variables. This hybrid EnKF-OI scheme is used to mitigate background covariance limitations due to ensemble under-sampling and neglected model errors. Numerical experiments are conducted with a two-dimensional synthetic aquifer in which cobalt-60, a radioactive contaminant, is leached in a saturated heterogeneous clayey sandstone zone. Assimilation experiments are investigated under different settings and sources of model and observational errors. Simulation results demonstrate that the proposed hybrid EnKF-OI scheme successfully recovers both the contaminant and the sorption rate and reduces their uncertainties. Sensitivity analyses also suggest that the adaptive hybrid scheme remains effective with small ensembles, allowing to reduce the ensemble size by up to 80% with respect to the standard EnKF scheme. © 2014 Elsevier Ltd.en
dc.publisherElsevier BVen
dc.subjectContaminant transport modelingen
dc.subjectEnsemble Kalman filter (EnKF)en
dc.subjectHybrid EnKF-OIen
dc.subjectOptimal interpolation (OI)en
dc.subjectState-parameter estimationen
dc.titleAn adaptive hybrid EnKF-OI scheme for efficient state-parameter estimation of reactive contaminant transport modelsen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.contributor.departmentEarth Sciences and Engineering Programen
dc.identifier.journalAdvances in Water Resourcesen
dc.contributor.institutionSubsurface and Groundwater Systems, Deltares, Princetonlaan 6, 3584 CB Utrecht, Netherlandsen
kaust.authorEl Gharamti, Mohamaden
kaust.authorHoteit, Ibrahimen
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