000279239 001__ 279239
000279239 005__ 20210129221018.0
000279239 0247_ $$2doi$$a10.1016/j.jhydrol.2015.08.019
000279239 0247_ $$2ISSN$$a0022-1694
000279239 0247_ $$2ISSN$$a1879-2707
000279239 0247_ $$2WOS$$aWOS:000366769200011
000279239 0247_ $$2altmetric$$aaltmetric:4744175
000279239 037__ $$aFZJ-2015-07254
000279239 041__ $$aEnglish
000279239 082__ $$a690
000279239 1001_ $$0P:(DE-Juel1)156219$$aTang, Q.$$b0$$eCorresponding author$$ufzj
000279239 245__ $$aCharacterisation of river–aquifer exchange fluxes: The role of spatial patterns of riverbed hydraulic conductivities
000279239 260__ $$bElsevier$$c2015
000279239 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1449739543_5825
000279239 3367_ $$2DataCite$$aOutput Types/Journal article
000279239 3367_ $$00$$2EndNote$$aJournal Article
000279239 3367_ $$2BibTeX$$aARTICLE
000279239 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000279239 3367_ $$2DRIVER$$aarticle
000279239 520__ $$aInteractions between surface water and groundwater play an essential role in hydrology, hydrogeology, ecology, and water resources management. A proper characterisation of riverbed structures might be important for estimating river–aquifer exchange fluxes. The ensemble Kalman filter (EnKF) is commonly used in subsurface flow and transport modelling for estimating states and parameters. However, EnKF only performs optimally for MultiGaussian distributed parameter fields, but the spatial distribution of streambed hydraulic conductivities often shows non-MultiGaussian patterns, which are related to flow velocity dependent sedimentation and erosion processes. In this synthetic study, we assumed a riverbed with non-MultiGaussian channel-distributed hydraulic parameters as a virtual reference. The synthetic study was carried out for a 3-D river–aquifer model with a river in hydraulic connection to a homogeneous aquifer. Next, in a series of data assimilation experiments three different groups of scenarios were studied. In the first and second group of scenarios, stochastic realisations of non-MultiGaussian distributed riverbeds were inversely conditioned to state information, using EnKF and the normal score ensemble Kalman filter (NS-EnKF). The riverbed hydraulic conductivity was oriented in the form of channels (first group of scenarios) or, with the same bimodal histogram, without channelling (second group of scenarios). In the third group of scenarios, the stochastic realisations of riverbeds have MultiGaussian distributed hydraulic parameters and are conditioned to state information with EnKF. It was found that the best results were achieved for channel-distributed non-MultiGaussian stochastic realisations and with parameter updating. However, differences between the simulations were small and non-MultiGaussian riverbed properties seem to be of less importance for subsurface flow than non-MultiGaussian aquifer properties. In addition, it was concluded that both EnKF and NS-EnKF improve the characterisation of non-MultiGaussian riverbed properties, hydraulic heads and exchange fluxes by piezometric head assimilation, and only NS-EnKF could preserve the initial distribution of riverbed hydraulic conductivities.
000279239 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000279239 588__ $$aDataset connected to CrossRef
000279239 65017 $$0V:(DE-MLZ)GC-170$$2V:(DE-HGF)$$aEarth, Environment and Cultural Heritage$$x0
000279239 7001_ $$0P:(DE-Juel1)140349$$aKurtz, W.$$b1$$ufzj
000279239 7001_ $$0P:(DE-HGF)0$$aBrunner, P.$$b2
000279239 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b3$$ufzj
000279239 7001_ $$0P:(DE-Juel1)138662$$aHendricks-Franssen, Harrie-Jan$$b4$$ufzj
000279239 770__ $$aGroundwater flow and transport in aquifers: Insights from modeling and characterization at the field scale
000279239 773__ $$0PERI:(DE-600)1473173-3$$a10.1016/j.jhydrol.2015.08.019$$gVol. 531, p. 111 - 123$$n1$$p111 - 123$$tJournal of hydrology$$v531$$x0022-1694$$y2015
000279239 8564_ $$uhttps://juser.fz-juelich.de/record/279239/files/1-s2.0-S002216941500579X-main-1.pdf$$yRestricted
000279239 8564_ $$uhttps://juser.fz-juelich.de/record/279239/files/1-s2.0-S002216941500579X-main-1.gif?subformat=icon$$xicon$$yRestricted
000279239 8564_ $$uhttps://juser.fz-juelich.de/record/279239/files/1-s2.0-S002216941500579X-main-1.jpg?subformat=icon-1440$$xicon-1440$$yRestricted
000279239 8564_ $$uhttps://juser.fz-juelich.de/record/279239/files/1-s2.0-S002216941500579X-main-1.jpg?subformat=icon-180$$xicon-180$$yRestricted
000279239 8564_ $$uhttps://juser.fz-juelich.de/record/279239/files/1-s2.0-S002216941500579X-main-1.jpg?subformat=icon-640$$xicon-640$$yRestricted
000279239 8564_ $$uhttps://juser.fz-juelich.de/record/279239/files/1-s2.0-S002216941500579X-main-1.pdf?subformat=pdfa$$xpdfa$$yRestricted
000279239 909CO $$ooai:juser.fz-juelich.de:279239$$pVDB:Earth_Environment$$pVDB
000279239 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000279239 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000279239 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ HYDROL : 2014
000279239 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000279239 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000279239 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000279239 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000279239 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000279239 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences
000279239 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000279239 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000279239 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000279239 9141_ $$y2015
000279239 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156219$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000279239 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)140349$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000279239 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aExternal Institute$$b2$$kExtern
000279239 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich GmbH$$b3$$kFZJ
000279239 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138662$$aForschungszentrum Jülich GmbH$$b4$$kFZJ
000279239 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0
000279239 920__ $$lyes
000279239 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000279239 980__ $$ajournal
000279239 980__ $$aVDB
000279239 980__ $$aI:(DE-Juel1)IBG-3-20101118
000279239 980__ $$aUNRESTRICTED