TR32-Database: Database of Transregio 32

[871] - Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of river-aquifer interactions

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Kurtz, W., Hendricks-Franssen, H., Kaiser, H., Vereecken, H., 2014. Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of river-aquifer interactions. Water Resources Research, 50, 1665 - 1688. DOI: 10.1002/2013WR014823.
Identification
Title(s):Main Title: Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of river-aquifer interactions
Description(s):Abstract: The ensemble Kalman filter (EnKF) is increasingly used to improve the real-time prediction of groundwater states and the estimation of uncertain hydraulic subsurface parameters through assimilation of measurement data like groundwater levels and concentration data. At the interface between surface water and groundwater, measured groundwater temperature data can provide an additional source of information for subsurface characterizations with EnKF. Additionally, an improved prediction of the temperature field itself is often desirable for groundwater management. In this work, we investigate the worth of a joint assimilation of hydraulic and thermal observation data on the state and parameter estimation with EnKF for two different model setups: (i) a simple synthetic model of a river-aquifer system where the parameters and simulation conditions were perfectly known and (ii) a model of the Limmat aquifer in Zurich (Switzerland) where an exhaustive set of real-world observations of groundwater levels (87) and temperatures (22) was available for assimilation (year 2007) and verification (year 2011). Results for the synthetic case suggest that a joint assimilation of piezometric heads and groundwater temperatures together with updating of uncertain hydraulic parameters gives the best estimation of states and hydraulic properties of the model. For the real-world case, the prediction of groundwater temperatures could also be improved through data assimilation with EnKF. For the validation period, it was found that parameter fields updated with piezometric heads reduced RMSE's of states significantly (heads −49%, temperature −15%), but an additional conditioning of parameters on groundwater temperatures only influenced the characterization of the temperature field.
Identifier(s):DOI: 10.1002/2013WR014823
Citation Advice:Kurtz, W., H.-J. Hendricks Franssen, H.-P. Kaiser, and H. Vereecken (2014), Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of river-aquifer interactions, Water Resour. Res., 50, 1665–1688, doi:10.1002/2013WR014823.
Responsible Party
Creator(s):Author: Wolfgang Kurtz
Author: Harrie-Jan Hendricks-Franssen
Author: H.P. Kaiser
Author: Harry Vereecken
Publisher:American Geophysical Union
Topic
TR32 Topic:Other
Subject(s):CRC/TR32 Keywords: Data Assimilation
Topic Category:Enviroment
File Details
File Name:wrcr20774.pdf
Data Type:Text
File Size:1808 kB (1.766 MB)
Date(s):Date Accepted: 2014-02-01
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:Other
Measurement Location:--Other--
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2014
Type:Article
Article Type:Journal
Source:Water Resources Research
Volume:50
Page Range:1665 - 1688
Metadata Details
Metadata Creator:Wolfgang Kurtz
Metadata Created:2014-06-02
Metadata Last Updated:2014-06-02
Subproject:C6
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:159
Metadata Downloads:0
Dataset Downloads:3
Dataset Activity
Features