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| Titel |
Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach |
| VerfasserIn |
M. Marshall, K. Tu, C. Funk, J. Michaelsen, P. Williams, C. Williams, J. Ardö, M. Boucher, B. Cappelaere, A. Grandcourt, A. Nickless, Y. Nouvellon, R. Scholes, W. Kutsch |
| 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 ; 17, no. 3 ; Nr. 17, no. 3 (2013-03-12), S.1079-1091 |
| Datensatznummer |
250018825
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| Publikation (Nr.) |
 copernicus.org/hess-17-1079-2013.pdf |
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| Zusammenfassung |
| Climate change is expected to have the greatest impact on the world's
economically poor. In the Sahel, a climatically sensitive region where
rain-fed agriculture is the primary livelihood, expected decreases in water
supply will increase food insecurity. Studies on climate change and the
intensification of the water cycle in sub-Saharan Africa are few. This is
due in part to poor calibration of modeled evapotranspiration (ET), a key
input in continental-scale hydrologic models. In this study, a remote
sensing model of transpiration (the primary component of ET), driven by a
time series of vegetation indices, was used to substitute transpiration from
the Global Land Data Assimilation System realization of the National Centers
for Environmental Prediction, Oregon State University, Air Force, and
Hydrology Research Laboratory at National Weather Service Land Surface Model
(GNOAH) to improve total ET model estimates for monitoring purposes in
sub-Saharan Africa. The performance of the hybrid model was compared against
GNOAH ET and the remote sensing method using eight eddy flux towers
representing major biomes of sub-Saharan Africa. The greatest improvements
in model performance were at humid sites with dense vegetation, while
performance at semi-arid sites was poor, but better than the models before
hybridization. The reduction in errors using the hybrid model can be
attributed to the integration of a simple canopy scheme that depends
primarily on low bias surface climate reanalysis data and is driven
primarily by a time series of vegetation indices. |
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