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| Titel |
Quantifying the carbon uptake by vegetation for Europe on a 1 km2 resolution using a remote sensing driven vegetation model |
| VerfasserIn |
K. Wißkirchen, M. Tum, K. P. Günther, M. Niklaus, C. Eisfelder, W. Knorr |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 6, no. 5 ; Nr. 6, no. 5 (2013-10-08), S.1623-1640 |
| Datensatznummer |
250084997
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| Publikation (Nr.) |
 copernicus.org/gmd-6-1623-2013.pdf |
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| Zusammenfassung |
| In this study we compare monthly gross primary productivity (GPP) time
series (2000–2007), computed for Europe with the Biosphere Energy
Transfer Hydrology (BETHY/DLR) model with monthly data from the eddy covariance measurements network FLUXNET. BETHY/DLR with a spatial resolution
of 1 km2 is designed for regional and continental applications (here Europe)
and operated at the German Aerospace Center (DLR). It was adapted
from the BETHY scheme to be driven by remote sensing data (leaf area index
(LAI) and land cover information) and meteorology. Time series of LAI
obtained from the CYCLOPES database are used to control the phenology of
vegetation. Meteorological time series from the European Centre for
Medium-Range Weather Forecasts (ECMWF) are used as driver. These comprise
daily information on temperature, precipitation, wind speed and radiation.
Additionally, static maps such as land cover, elevation, and soil type are
used. To validate our model results we used eddy covariance measurements
from the FLUXNET network of 74 towers across Europe. For forest sites we
found that our model predicts between 20 and 40% higher annual GPP
sums. In contrast, for cropland sites BETHY/DLR results show about 18%
less GPP than eddy covariance measurements. For grassland sites, between 10%
more and 16% less GPP was calculated with BETHY/DLR. A mean total
carbon uptake of 2.5 PgC a−1 (±0.17 PgC a−1) was found for
Europe. In addition, this study reports on risks that arise from the
comparison of modelled data to FLUXNET measurements and their interpretation
width. Furthermore we investigate reasons for uncertainties in model results
and focus here on Vmax values, and finally embed our results into a
broader context of model validation studies published during the last years
in order to evaluate differences or similarities in analysed error sources. |
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