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Titel |
A global analysis of soil moisture derived from satellite observations and a land surface model |
VerfasserIn |
K. T. Rebel, R. A. M. Jeu, P. Ciais, N. Viovy, S. L. Piao, G. Kiely, A. J. Dolman |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
1027-5606
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Digitales Dokument |
URL |
Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 3 ; Nr. 16, no. 3 (2012-03-16), S.833-847 |
Datensatznummer |
250013214
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Publikation (Nr.) |
copernicus.org/hess-16-833-2012.pdf |
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Zusammenfassung |
Soil moisture availability is important in regulating photosynthesis and
controlling land surface-climate feedbacks at both the local and global
scale. Recently, global remote-sensing datasets for soil moisture have
become available. In this paper we assess the possibility of using remotely
sensed soil moisture – AMSR-E (LPRM) – to similate soil moisture dynamics of the
process-based vegetation model ORCHIDEE by evaluating the correspondence
between these two products using both correlation and autocorrelation
analyses. We find that the soil moisture product of AMSR-E (LPRM) and the
simulated soil moisture in ORCHIDEE correlate well in space and time, in
particular when considering the root zone soil moisture of ORCHIDEE.
However, the root zone soil moisture in ORCHIDEE has on average a higher
temporal autocorrelation relative to AMSR-E (LPRM) and in situ measurements.
This may be due to the different vertical depth of the two products – AMSR-E
(LPRM) at the 2–5 cm surface depth and ORCHIDEE at the root zone (max. 2 m)
depth – to uncertainty in precipitation forcing in ORCHIDEE, and to the fact
that the structure of ORCHIDEE consists of a single-layer deep soil, which
does not allow simulation of the proper cascade of time scales that
characterize soil drying after each rain event. We conclude that assimilating
soil moisture, using AMSR-E (LPRM) in a land surface model like ORCHIDEE with
an improved hydrological model of more than one soil layer, may significantly
improve the soil moisture dynamics, which could lead to improved CO2
and energy flux predictions. |
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