001     840115
005     20210131030728.0
024 7 _ |a 10.1016/j.advwatres.2017.11.003
|2 doi
024 7 _ |a WOS:000418592800016
|2 WOS
024 7 _ |a altmetric:29918864
|2 altmetric
037 _ _ |a FZJ-2017-07678
082 _ _ |a 550
100 1 _ |a Zhang, Hongjuan
|0 P:(DE-Juel1)161265
|b 0
|e Corresponding author
245 _ _ |a Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model
260 _ _ |a Amsterdam [u.a.]
|c 2018
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1511947472_9734
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a The linkage between root zone soil moisture and groundwater is either neglected or simplified in most land surface models. The fully-coupled subsurface-land surface model TerrSysMP including variably saturated groundwater dynamics is used in this work. We test and compare five data assimilation methodologies for assimilating groundwater level data via the ensemble Kalman filter (EnKF) to improve root zone soil moisture estimation with TerrSysMP. Groundwater level data are assimilated in the form of pressure head or soil moisture (set equal to porosity in the saturated zone) to update state vectors. In the five assimilation methodologies, the state vector contains either (i) pressure head, or (ii) log-transformed pressure head, or (iii) soil moisture, or (iv) pressure head for the saturated zone only, or (v) a combination of pressure head and soil moisture, pressure head for the saturated zone and soil moisture for the unsaturated zone. These methodologies are evaluated in synthetic experiments which are performed for different climate conditions, soil types and plant functional types to simulate various root zone soil moisture distributions and groundwater levels. The results demonstrate that EnKF cannot properly handle strongly skewed pressure distributions which are caused by extreme negative pressure heads in the unsaturated zone during dry periods. This problem can only be alleviated by methodology (iii), (iv) and (v). The last approach gives the best results and avoids unphysical updates related to strongly skewed pressure heads in the unsaturated zone. If groundwater level data are assimilated by methodology (iii), EnKF fails to update the state vector containing the soil moisture values if for (almost) all the realizations the observation does not bring significant new information. Synthetic experiments for the joint assimilation of groundwater levels and surface soil moisture support methodology (v) and show great potential for improving the representation of root zone soil moisture.
536 _ _ |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255)
|0 G:(DE-HGF)POF3-255
|c POF3-255
|f POF III
|x 0
700 1 _ |a Kurtz, Wolfgang
|0 P:(DE-Juel1)140349
|b 1
700 1 _ |a Kollet, Stefan
|0 P:(DE-Juel1)151405
|b 2
700 1 _ |a Vereecken, Harry
|0 P:(DE-Juel1)129549
|b 3
700 1 _ |a Hendricks-Franssen, Harrie-Jan
|0 P:(DE-Juel1)138662
|b 4
|e Corresponding author
773 _ _ |a 10.1016/j.advwatres.2017.11.003
|0 PERI:(DE-600)2023320-6
|p 224-238
|t Advances in water resources
|v 111
|y 2018
|x 0309-1708
856 4 _ |u https://juser.fz-juelich.de/record/840115/files/1-s2.0-S0309170817304888-main.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/840115/files/1-s2.0-S0309170817304888-main.gif?subformat=icon
|x icon
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/840115/files/1-s2.0-S0309170817304888-main.jpg?subformat=icon-1440
|x icon-1440
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/840115/files/1-s2.0-S0309170817304888-main.jpg?subformat=icon-180
|x icon-180
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/840115/files/1-s2.0-S0309170817304888-main.jpg?subformat=icon-640
|x icon-640
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/840115/files/1-s2.0-S0309170817304888-main.pdf?subformat=pdfa
|x pdfa
|y Restricted
909 C O |o oai:juser.fz-juelich.de:840115
|p VDB
|p VDB:Earth_Environment
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)161265
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)140349
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)151405
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)129549
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)138662
913 1 _ |a DE-HGF
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF3-250
|0 G:(DE-HGF)POF3-255
|2 G:(DE-HGF)POF3-200
|v Terrestrial Systems: From Observation to Prediction
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Erde und Umwelt
914 1 _ |y 2017
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b ADV WATER RESOUR : 2015
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21