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| Titel |
Inverse Estimation of the Parameters of a Canopy Gross Photosynthesis Model |
| VerfasserIn |
Christoph Irschick, Thomas Liener, Albin Hammerle, Georg Wohlfahrt |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250047025
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| Zusammenfassung |
| Inverse estimation of model parameters by Bayesian methods is a powerful means for
deriving parameter values and their uncertainties from bulk measurements, in particular for
parameters whose values are otherwise hard to quantify. Gross canopy photosynthesis is a key
conceptual component of most carbon cycle models as it provides the carbon input available
for growth and respiration. Here we employ DREAM (Differential Evolution Adaptive
Algorithm; Vrugt et al., 2008) for estimating the parameters of a canopy gross photosynthesis
model which is a combination of a modified version of the two-leaf big-leaf model of De
Pury and Farquhar (1997) and the leaf-level light response curve model by Smith et al.
(1937). Canopy gross photosynthesis was derived from eddy covariance CO2 flux
measurements above a managed temperate mountain grassland in Austria as described
in Wohlfahrt et al. (2008). Further inputs to the model include total and diffuse
photosynthetically active radiation and the green area index. All four model parameters,
including two related to the phytoelement inclination distribution and optical properties,
were well constrained by the available data, suggesting that measured canopy gross
photosynthesis possesses enough information content for reliably estimating the
parameters of this simple model. Parameter inversions were conducted at different
time scales (sub-season, year, decade) and using different assumptions regarding
the uncertainty of calibration and input data – these are discussed with respect to
implications for modelling canopy gross photosynthesis of the investigated grassland. |
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