Hauptseite > Publikationsdatenbank > Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models > print

001     878390
005     20210628225835.0
024 7 _ |a 10.3389/frwa.2020.00004
|2 doi
024 7 _ |a 2128/25515
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024 7 _ |a WOS:000659406000001
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037 _ _ |a FZJ-2020-02826
041 _ _ |a English
082 _ _ |a 333.7
100 1 _ |a Pauwels, Valentijn R. N.
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models
260 _ _ |a Lausanne
|c 2020
|b Frontiers Media
336 7 _ |a article
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336 7 _ |a Output Types/Journal article
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336 7 _ |a Journal Article
|b journal
|m journal
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|s 1597644984_7895
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
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336 7 _ |a JOURNAL_ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a For an accurate estimation of land surface state variables through remote sensing data assimilation, it is important to estimate the forecast and observation biases as well. This study focuses on the evaluation of a methodology to estimate land surface state variables, together with model forecast and observation biases. Two conceptual rainfall-runoff models (HBV and GRKAL) are used for this purpose. Soil moisture data, retrieved by the Soil Moisture Ocean Salinity (SMOS) mission, are assimilated into these models for 59 unregulated sub-basins of the Murray-Darling basin in Australia. When both models simulate similar soil moisture values, the methodology results in similar forecast and observation bias estimates for both models. The same behavior is obtained when the temporal evolution of the soil moisture simulations is different, but with a similar long-term mean climatology. However, when the long-term mean climatology of both models is different, but with a similar temporal evolution, the bias estimates from both models have a different climatology as well, but with a high temporal correlation. The overall conclusion from this paper is that observation bias estimation is of key importance when updating internal state variables in a conceptual rainfall-runoff system that is calibrated to produce realistic discharge output for possibly biased internal state variables, and that the relative partitioning of bias into forecast and observation bias remains a model-dependent challenge.
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 Hendricks-Franssen, Harrie-Jan
|0 P:(DE-Juel1)138662
|b 1
700 1 _ |a De Lannoy, Gabriëlle J. M.
|0 P:(DE-HGF)0
|b 2
773 _ _ |a 10.3389/frwa.2020.00004
|g Vol. 2, p. 4
|0 PERI:(DE-600)2986721-6
|p 4
|t Frontiers in water
|v 2
|y 2020
|x 2624-9375
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/878390/files/Pauwelsetal2020.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/878390/files/Pauwelsetal2020.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:878390
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910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
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|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
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|v Terrestrial Systems: From Observation to Prediction
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|b Erde und Umwelt
914 1 _ |y 2020
915 _ _ |a OpenAccess
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915 _ _ |a Creative Commons Attribution CC BY 4.0
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920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
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980 _ _ |a journal
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980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 1 _ |a FullTexts

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