The role of prior model calibration on predictions with ensemble kalman filter

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The role of prior model calibration on predictions with ensemble kalman filter

JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)

ID	981763
Author(s)	Huber, E.; Hendricks-Franssen, H. J.; Kaiser, H. P.; Stauffer, F.
Author(s) at UniBasel	Huggenberger, Peter
Year	2011
Title	The role of prior model calibration on predictions with ensemble kalman filter
Journal	Ground Water
Volume	49
Number	6
Pages / Article-Number	845-858
Abstract	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.
Publisher	Wiley
ISSN/ISBN	0017-467X ; 1745-6584
edoc-URL	http://edoc.unibas.ch/46588/
Full Text on edoc	No
Digital Object Identifier DOI	10.1111/j.1745-6584.2010.00784.x
PubMed ID	http://www.ncbi.nlm.nih.gov/pubmed/21210793
ISI-Number	WOS:000297070200011
Document type (ISI)	Article

02/05/2024

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