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| Titel |
An adaptive hybrid EnKF-OI scheme for efficient state-parameter estimation of reactive contaminant transport models |
| VerfasserIn |
Mohamad El Gharamti, Johan Valstar, Ibrahim Hoteit |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250091639
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| Publikation (Nr.) |
 EGU/EGU2014-5940.pdf |
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| Zusammenfassung |
| 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. Our results suggest that the proposed scheme allows a reduction
of around 80% of the ensemble size as compared to the standard EnKF scheme. |
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