Home > Publications database > Impacts of hydrological model calibration on projected hydrological changes under climate change—a multi-model assessment in three large river basins > print

001     887682
005     20210104120107.0
024 7 _ |a 10.1007/s10584-020-02872-6
|2 doi
024 7 _ |a 0165-0009
|2 ISSN
024 7 _ |a 1573-1480
|2 ISSN
024 7 _ |a 2128/26632
|2 Handle
024 7 _ |a altmetric:91174701
|2 altmetric
024 7 _ |a WOS:000572592300001
|2 WOS
037 _ _ |a FZJ-2020-04343
082 _ _ |a 550
100 1 _ |a Huang, Shaochun
|0 0000-0001-7426-5181
|b 0
|e Corresponding author
245 _ _ |a Impacts of hydrological model calibration on projected hydrological changes under climate change—a multi-model assessment in three large river basins
260 _ _ |a Dordrecht [u.a.]
|c 2020
|b Springer Science + Business Media B.V
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 1609264997_12842
|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 This study aimed to investigate the influence of hydrological model calibration/validation on discharge projections for three large river basins (the Rhine, Upper Mississippi and Upper Yellow). Three hydrological models (HMs), which have been firstly calibrated against the monthly discharge at the outlet of each basin (simple calibration), were re-calibrated against the daily discharge at the outlet and intermediate gauges under contrast climate conditions simultaneously (enhanced calibration). In addition, the models were validated in terms of hydrological indicators of interest (median, low and high flows) as well as actual evapotranspiration in the historical period. The models calibrated using both calibration methods were then driven by the same bias corrected climate projections from five global circulation models (GCMs) under four Representative Concentration Pathway scenarios (RCPs). The hydrological changes of the indicators were represented by the ensemble median, ensemble mean and ensemble weighted means of all combinations of HMs and GCMs under each RCP. The results showed moderate (5–10%) to strong influence (> 10%) of the calibration methods on the ensemble medians/means for the Mississippi, minor to moderate (up to 10%) influence for the Yellow and minor (< 5%) influence for the Rhine. In addition, the enhanced calibration/validation method reduced the shares of uncertainty related to HMs for three indicators in all basins when the strict weighting method was used. It also showed that the successful enhanced calibration had the potential to reduce the uncertainty of hydrological projections, especially when the HM uncertainty was significant after the simple calibration.
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
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Shah, Harsh
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Naz, Bibi S.
|0 P:(DE-Juel1)169794
|b 2
700 1 _ |a Shrestha, Narayan
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Mishra, Vimal
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Daggupati, Prasad
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Ghimire, Uttam
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Vetter, Tobias
|0 P:(DE-HGF)0
|b 7
773 _ _ |a 10.1007/s10584-020-02872-6
|0 PERI:(DE-600)1477652-2
|p 1143–1164
|t Climatic change
|v 163
|y 2020
|x 1573-1480
856 4 _ |u https://juser.fz-juelich.de/record/887682/files/Huang2020_Article_ImpactsOfHydrologicalModelCali.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:887682
|p openaire
|p open_access
|p driver
|p VDB:Earth_Environment
|p VDB
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)169794
913 1 _ |a DE-HGF
|b Erde und Umwelt
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF3-250
|0 G:(DE-HGF)POF3-255
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-200
|4 G:(DE-HGF)POF
|v Terrestrial Systems: From Observation to Prediction
|x 0
914 1 _ |y 2020
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2020-09-08
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2020-09-08
915 _ _ |a DEAL Springer
|0 StatID:(DE-HGF)3002
|2 StatID
|d 2020-09-08
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-09-08
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2020-09-08
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1060
|2 StatID
|b Current Contents - Agriculture, Biology and Environmental Sciences
|d 2020-09-08
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b CLIMATIC CHANGE : 2018
|d 2020-09-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-09-08
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2020-09-08
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-09-08
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21