Influences of observation errors in eddy flux data on inverse model parameter estimation

By G. Lasslop, Markus Reichstein, Jens Kattge, Dario Papale. 2008

142 citations recorded by the source corpus.

Abstract. 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.

Subfields: Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cbc6825350710dec5feb07cf0acb97c90e6b20f4

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In Global and Planetary Change

Citations in the corpus

Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval (2008, background): “, when pooling nighttime and daytime data [Lasslop et al., 2008].”
Improving land surface models with FLUXNET data (2009, methodology): “First, whereas statistical analyses can be used to estimate the size of random errors (Lasslop et al., 2008), systematic errors are difficult to detect or quantify in data-based analyses (Moncrieff et al., 1996); but see Lee (1998).”
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data (2009, background): “Also large areas of 72 the globe are poorly sampled, and measurements are affected by systematic and random errors 73 (Lasslop et al. 2008, Richardson et al. 2008), both of which can be large.”
A new methodology to interpret high resolution measurements of net carbon fluxes between terrestrial ecosystems and the atmosphere (2012, methodology): “If the data follows a Laplace rather than a Gaussian distribution, not the RMSE but the standard deviation SDev should be used as a measure of the model residuals (Richardson et al., 2006; Lasslop et al., 2008): SDev = √ 2 ·MAE = √ 2 · 1 N ∑ |mi − ti|, (3.”
Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model (2009, methodology): “The role of the data uncertainties could be assessed for example by adding noise and bias to the (simulated) training data that are comparable to those inferred from studying the uncertainties of eddy covariance data (Lasslop et al., 2008; Richardson et al., 2006).”