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| Titel |
Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France |
| VerfasserIn |
C. Albergel, J.-C. Calvet, J.-F. Mahfouf, C. Rüdiger, A. L. Barbu, S. Lafont, J.-L. Roujean, J. P. Walker, M. Crapeau, J.-P. Wigneron |
| 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 ; 14, no. 6 ; Nr. 14, no. 6 (2010-06-29), S.1109-1124 |
| Datensatznummer |
250012347
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| Publikation (Nr.) |
 copernicus.org/hess-14-1109-2010.pdf |
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| Zusammenfassung |
| A Land Data Assimilation System (LDAS) able to ingest
surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested
at local scale to increase prediction accuracy for water and carbon fluxes.
The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the
soil water content observations of a grassland at the SMOSREX experimental
site in southwestern France for a seven-year period (2001–2007). Three
configurations corresponding to contrasted model errors are considered: (1)
best case (BC) simulation with locally observed atmospheric variables and
model parameters, and locally observed SSM and LAI used in the assimilation,
(2) same as (1) but with the precipitation forcing set to zero, (3) real
case (RC) simulation with atmospheric variables and model parameters derived
from regional atmospheric analyses and from climatological soil and
vegetation properties, respectively. In configuration (3) two SSM time
series are considered: the observed SSM using Thetaprobes, and SSM derived
from the LEWIS L-band radiometer located 15m above the ground. Performance
of the LDAS is examined in the three configurations described above with
either one variable (either SSM or LAI) or two variables (both SSM and LAI)
assimilated. The joint assimilation of SSM and LAI has a positive impact on
the carbon, water, and heat fluxes. It represents a greater impact than
assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to
counterbalance large errors in the precipitation forcing given as input to
the model. |
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