 |
| Titel |
Bayesian inversions of a dynamic vegetation model at four European grassland sites |
| VerfasserIn |
J. Minet, E. Laloy, B. Tychon, L. François |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 12, no. 9 ; Nr. 12, no. 9 (2015-05-13), S.2809-2829 |
| Datensatznummer |
250117932
|
| Publikation (Nr.) |
 copernicus.org/bg-12-2809-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Eddy covariance data from four European grassland sites are used to
probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10
unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo
(MCMC) sampler. We focus on comparing model inversions, considering both
homoscedastic and heteroscedastic eddy covariance residual errors, with
variances either fixed a priori or jointly inferred together with the model
parameters. Agreements between measured and simulated data during calibration
are comparable with previous studies, with root mean square errors (RMSEs) of
simulated daily gross primary productivity (GPP), ecosystem respiration
(RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m−2 day−1 and 0.50 to 1.28 mm day−1,
respectively. For the calibration period, using a homoscedastic
eddy covariance residual error model resulted in a better agreement between
measured and modelled data than using a heteroscedastic residual error model.
However, a model validation experiment showed that CARAIB models calibrated
considering heteroscedastic residual errors perform better. Posterior
parameter distributions derived from using a heteroscedastic model of the
residuals thus appear to be more robust. This is the case even though the classical
linear heteroscedastic error model assumed herein did not fully remove
heteroscedasticity of the GPP residuals. Despite the fact that the calibrated
model is generally capable of fitting the data within measurement errors,
systematic bias in the model simulations are observed. These are likely due
to model inadequacies such as shortcomings in the photosynthesis modelling.
Besides the residual error treatment, differences between model parameter
posterior distributions among the four grassland sites are also investigated.
It is shown that the marginal distributions of the specific leaf area and
characteristic mortality time parameters can be explained by site-specific
ecophysiological characteristics. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|