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000017040 0247_ $$2DOI$$a10.1111/j.1745-6584.2010.00784.x
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000017040 041__ $$aeng
000017040 082__ $$a550
000017040 084__ $$2WoS$$aGeosciences, Multidisciplinary
000017040 084__ $$2WoS$$aWater Resources
000017040 1001_ $$0P:(DE-HGF)0$$aHuber, E.$$b0
000017040 245__ $$aThe role of prior model calibration on predictions with Ensemble Kalman Filter
000017040 260__ $$aOxford [u.a.]$$bWiley-Blackwell$$c2011
000017040 300__ $$a845 - 858
000017040 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000017040 440_0 $$019221$$aGround Water$$v49$$x0017-467X$$y6
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000017040 520__ $$aThis paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained.
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000017040 650_2 $$2MeSH$$aEnvironmental Monitoring
000017040 650_2 $$2MeSH$$aGroundwater
000017040 650_2 $$2MeSH$$aModels, Theoretical
000017040 650_7 $$2WoSType$$aJ
000017040 7001_ $$0P:(DE-Juel1)VDB99007$$aHendricks Franssen, H.J.$$b1$$uFZJ
000017040 7001_ $$0P:(DE-HGF)0$$aKaiser, H.P.$$b2
000017040 7001_ $$0P:(DE-HGF)0$$aStauffer, F.$$b3
000017040 773__ $$0PERI:(DE-600)2066386-9$$a10.1111/j.1745-6584.2010.00784.x$$gVol. 49, p. 845 - 858$$p845 - 858$$q49<845 - 858$$tGround water$$v49$$x0017-467X$$y2011
000017040 8567_ $$uhttp://dx.doi.org/10.1111/j.1745-6584.2010.00784.x
000017040 909CO $$ooai:juser.fz-juelich.de:17040$$pVDB$$pVDB:Earth_Environment
000017040 9131_ $$0G:(DE-Juel1)FUEK407$$bErde und Umwelt$$kP24$$lTerrestrische Umwelt$$vTerrestrische Umwelt$$x0
000017040 9132_ $$0G:(DE-HGF)POF3-259H$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$aDE-HGF$$bMarine, Küsten- und Polare Systeme$$lTerrestrische Umwelt$$vAddenda$$x0
000017040 9141_ $$y2011
000017040 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
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