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| Titel |
Influences of observation errors in eddy flux data on inverse model parameter estimation |
| VerfasserIn |
G. Lasslop, M. Reichstein, J. Kattge, D. Papale |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 5, no. 5 ; Nr. 5, no. 5 (2008-09-17), S.1311-1324 |
| Datensatznummer |
250002828
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| Publikation (Nr.) |
 copernicus.org/bg-5-1311-2008.pdf |
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| Zusammenfassung |
| Eddy covariance data are increasingly used to estimate parameters of ecosystem
models. For proper maximum likelihood parameter estimates the error structure in the observed
data has to be fully characterized. In this study we propose a method to characterize the
random error of the eddy covariance flux data, and analyse error distribution, standard
deviation, cross- and autocorrelation of CO2 and H2O flux errors at four different
European eddy covariance flux sites. Moreover, we examine how the treatment of those
errors and additional systematic errors influence statistical estimates of parameters
and their associated uncertainties with three models of increasing complexity – a hyperbolic light response
curve, a light response curve coupled to water fluxes and
the SVAT scheme BETHY.
In agreement with previous studies we find that the error standard deviation scales
with the flux magnitude. The previously found strongly leptokurtic error distribution
is revealed to be largely due to a superposition of almost Gaussian distributions with
standard deviations varying by flux magnitude. The crosscorrelations of CO2 and H2O
fluxes were in all cases negligible (R2 below 0.2), while the autocorrelation is usually
below 0.6 at a lag of 0.5 h and decays rapidly at larger time lags. This implies that in
these cases the weighted least squares criterion yields maximum likelihood estimates.
To study the influence of the observation errors on model parameter estimates we used
synthetic datasets, based on observations of two different sites. We first fitted the
respective models to observations and then added the random error estimates described
above and the systematic error, respectively, to the model output. This strategy enables
us to compare the estimated parameters with true parameters.
We illustrate that the correct implementation of the random error standard deviation scaling with
flux magnitude significantly reduces the parameter uncertainty and often yields parameter
retrievals that are closer to the true value, than by using ordinary least squares.
The systematic error leads to systematically biased parameter estimates, but its impact
varies by parameter. The parameter uncertainty slightly increases, but the true parameter
is not within the uncertainty range of the estimate. This means that the uncertainty is
underestimated with current approaches that neglect selective systematic errors in flux
data. Hence, we conclude that potential systematic errors in flux data need to be addressed
more thoroughly in data assimilation approaches since otherwise uncertainties will be vastly underestimated. |
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