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001     17040
005     20200702121606.0
024 7 _ |2 pmid
|a pmid:21210793
024 7 _ |2 DOI
|a 10.1111/j.1745-6584.2010.00784.x
024 7 _ |2 WOS
|a WOS:000297070200011
037 _ _ |a PreJuSER-17040
041 _ _ |a eng
082 _ _ |a 550
084 _ _ |2 WoS
|a Geosciences, Multidisciplinary
084 _ _ |2 WoS
|a Water Resources
100 1 _ |a Huber, E.
|b 0
|0 P:(DE-HGF)0
245 _ _ |a The role of prior model calibration on predictions with Ensemble Kalman Filter
260 _ _ |a Oxford [u.a.]
|b Wiley-Blackwell
|c 2011
300 _ _ |a 845 - 858
336 7 _ |a Journal Article
|0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|0 0
|2 EndNote
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a article
|2 DRIVER
440 _ 0 |a Ground Water
|x 0017-467X
|0 19221
|y 6
|v 49
500 _ _ |3 POF3_Assignment on 2016-02-29
500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a This 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.
536 _ _ |a Terrestrische Umwelt
|c P24
|2 G:(DE-HGF)
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|x 0
588 _ _ |a Dataset connected to Web of Science, Pubmed
650 _ 2 |2 MeSH
|a Environmental Monitoring
650 _ 2 |2 MeSH
|a Groundwater
650 _ 2 |2 MeSH
|a Models, Theoretical
650 _ 7 |a J
|2 WoSType
700 1 _ |a Hendricks Franssen, H.J.
|b 1
|u FZJ
|0 P:(DE-Juel1)VDB99007
700 1 _ |a Kaiser, H.P.
|b 2
|0 P:(DE-HGF)0
700 1 _ |a Stauffer, F.
|b 3
|0 P:(DE-HGF)0
773 _ _ |a 10.1111/j.1745-6584.2010.00784.x
|g Vol. 49, p. 845 - 858
|p 845 - 858
|q 49<845 - 858
|0 PERI:(DE-600)2066386-9
|t Ground water
|v 49
|y 2011
|x 0017-467X
856 7 _ |u http://dx.doi.org/10.1111/j.1745-6584.2010.00784.x
909 C O |o oai:juser.fz-juelich.de:17040
|p VDB
|p VDB:Earth_Environment
913 1 _ |k P24
|v Terrestrische Umwelt
|l Terrestrische Umwelt
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913 2 _ |a DE-HGF
|b Marine, Küsten- und Polare Systeme
|l Terrestrische Umwelt
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|v Addenda
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914 1 _ |y 2011
915 _ _ |0 StatID:(DE-HGF)0010
|a JCR/ISI refereed
920 1 _ |k IBG-3
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980 _ _ |a journal
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 _ _ |a UNRESTRICTED

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