001     859669
005     20220930130205.0
024 7 _ |a 10.5194/hess-23-277-2019
|2 doi
024 7 _ |a 1027-5606
|2 ISSN
024 7 _ |a 1607-7938
|2 ISSN
024 7 _ |a 2128/21338
|2 Handle
024 7 _ |a WOS:000456148000001
|2 WOS
024 7 _ |a altmetric:54053644
|2 altmetric
037 _ _ |a FZJ-2019-00511
082 _ _ |a 550
100 1 _ |a Naz, Bibi S.
|0 P:(DE-Juel1)169794
|b 0
|e Corresponding author
245 _ _ |a Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation
260 _ _ |a Katlenburg-Lindau
|c 2019
|b EGU
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 1555411622_2653
|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 Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.
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
536 _ _ |a EoCoE - Energy oriented Centre of Excellence for computer applications (676629)
|0 G:(EU-Grant)676629
|c 676629
|f H2020-EINFRA-2015-1
|x 1
536 _ _ |0 G:(DE-Juel1)IRTG-GRADUATE-20170406
|x 2
|c IRTG-GRADUATE-20170406
|a IRTG, Graduate School - Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation (TR32) (IRTG, Graduate School) (IRTG-GRADUATE-20170406)
536 _ _ |a Water4Enery (jibg31_20160501)
|0 G:(DE-Juel1)jibg31_20160501
|c jibg31_20160501
|f Water4Enery
|x 3
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 4
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Kurtz, Wolfgang
|0 P:(DE-Juel1)140349
|b 1
700 1 _ |a Montzka, Carsten
|0 P:(DE-Juel1)129506
|b 2
700 1 _ |a Sharples, Wendy
|0 P:(DE-Juel1)168536
|b 3
700 1 _ |a Görgen, Klaus
|0 P:(DE-Juel1)156253
|b 4
700 1 _ |a Keune, Jessica
|0 0000-0001-6104-2165
|b 5
700 1 _ |a Gao, Huilin
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Springer, Anne
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Hendricks-Franssen, Harrie-Jan
|0 P:(DE-Juel1)138662
|b 8
700 1 _ |a Kollet, Stefan
|0 P:(DE-Juel1)151405
|b 9
773 _ _ |a 10.5194/hess-23-277-2019
|g Vol. 23, no. 1, p. 277 - 301
|0 PERI:(DE-600)2100610-6
|n 1
|p 277 - 301
|t Hydrology and earth system sciences
|v 23
|y 2019
|x 1607-7938
856 4 _ |u https://juser.fz-juelich.de/record/859669/files/Naz2019aS1.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/859669/files/invoice_Helmholtz-PUC-2019-15.pdf
856 4 _ |u https://juser.fz-juelich.de/record/859669/files/Naz2019a.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/859669/files/Naz2019aS1.pdf?subformat=pdfa
|x pdfa
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/859669/files/invoice_Helmholtz-PUC-2019-15.pdf?subformat=pdfa
|x pdfa
856 4 _ |u https://juser.fz-juelich.de/record/859669/files/Naz2019a.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:859669
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB:Earth_Environment
|p VDB
|p ec_fundedresources
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)169794
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)129506
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)168536
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)156253
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 8
|6 P:(DE-Juel1)138662
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)151405
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
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 1
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2019
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b HYDROL EARTH SYST SC : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Peer review
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
920 1 _ |0 I:(DE-82)080012_20140620
|k JARA-HPC
|l JARA - HPC
|x 1
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 _ _ |a I:(DE-82)080012_20140620
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21