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| Titel |
Dual States Estimation of a Subsurface Flow-Transport Coupled Model using Ensemble Kalman Filtering |
| 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 |
250091676
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| Publikation (Nr.) |
 EGU/EGU2014-5980.pdf |
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| Zusammenfassung |
| Modeling the spread of subsurface contaminants requires coupling a groundwater flow model
with a contaminant transport model. Such coupling may provide accurate estimates of future
subsurface hydrologic states if essential flow and contaminant data are assimilated in the
model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data
assimilation into the transport model. This is, however, a crude assumption as flow models
can be subject to many sources of uncertainty. If the flow is not accurately simulated,
contaminant predictions will likely be inaccurate even after successive Kalman updates of the
contaminant model with the data. The problem is better handled when both flow and
contaminant states are concurrently estimated using the traditional joint state augmentation
approach.
In this study, we introduce a dual estimation strategy for data assimilation into a one-way
coupled system by treating the flow and the contaminant models separately while
intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals
with the estimation of state variables but it can also be used for state and parameter estimation
problems. This EnKF-based dual state-state estimation procedure presents a number of novel
features: (i) it allows for simultaneous estimation of both flow and contaminant states in
parallel; (ii) it provides a time consistent sequential updating scheme between the two models
(first flow, then transport); (iii) it simplifies the implementation of the filtering system;
and (iv) it yields more stable and accurate solutions than does the standard joint
approach.
We conduct synthetic experiments based on various time stepping and observation
strategies to evaluate the dual EnKF approach and compare its performance with
the joint state augmentation approach. Experimental results show that on average,
the dual strategy could reduce the estimation error of the coupled states by 15%
compared with the joint approach. Furthermore, the dual estimation is proven to be
very effective computationally, recovering accurate estimates at a reasonable cost. |
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