Statistical modeling of ecosystem respiration using eddy covariance data: Maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three…

By Andrew D. Richardson, David Y. Hollinger. 2005

192 citations recorded by the source corpus.

Whether the goal is to fill gaps in the flux record, or to extract physiological parameters from eddy covariance data, researchers are frequently interested in fitting simple models of ecosystem physiology to measured data. Presently, there is no consensus on the best models to use, or the ideal optimization criteria. We demonstrate that, given our estimates of the distribution of the stochastic uncertainty in nighttime flux measurements at the Howland (Maine, USA) AmeriFlux site, it is incorrect to fit ecosystem respiration models using ordinary least squares (OLS) optimization. Results indicate that the flux uncertainty follows a double-exponential (Laplace) distribution, and the standard deviation of the uncertainty (s(d)) follows a strong seasonal pattern, increasing as an exponential function of temperature. These characteristics both violate OLS assumptions. We propose that to obtain maximum likelihood estimates of model parameters, fitting should be based on

Subfields: Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/bf5b7fd4be15bf907efb3842a06ec6f40c1111ec

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Citations in the corpus

Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect (2013, methodology): “A second-order Fourier regression was fitted to the nighttime Net Ecosystem Exchange (NEE) series, which is assumed to be a representative of Ecosystem Respiration (Reco), using the method proposed by Richardson and Hollinger (2005): Reco ¼ f 0 ¼ s1 sin Dπð Þ þ c1 cos Dπð Þ þ s2 sin 2 Dπð Þ þ C2 cos”
A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes (2006, methodology): “One area where the choice of fitting paradigm is highly relevant is gap filling: Richardson and Hollinger (2005) report that when the standard Howland gap-filling routine is implemented using the absolute deviation criterion, the mean (1996–2002, 1 S.D.) annual NEE is boosted by 44 9 g C m 2 y 1.”
‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems (2008, background): “Other groups of investigators are inverting flux data to derive stand-scale model parameters (Wang et al. 2001; Reichstein et al. 2003; Aalto et al. 2004; Jarvis et al. 2004; Schulz and Jarvis 2004; Wang et al. 2004; Braswell et al. 2005; Knorr and Kattge 2005; Raupach et al. 2005; Richardson and Ho”
Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes (2007, background): “Richardson and Hollinger (2005) noted that random flux measurement uncertainty, which cannot be captured by models because of its stochastic nature, placed an upper limit on the level of agreement between measured and modeled (gap-filled) fluxes.This suggests that the gapfilling techniques are alre”
Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals (2008, unknown): “For example, Richardson and Hollinger (2005) reported that the standard deviation of model residuals for the Lloyd and Taylor (1994) respiration model, fit to nocturnal data, was only 14% larger than the random flux measurement uncertainty estimated using paired measurements, suggesting that model…”
Improving land surface models with FLUXNET data (2009, background): “For instance, a least squares weighting is required if flux error is Gaussian, while mean absolute error is required if flux error is Laplacian (Richardson and Hollinger, 2005).”
Whole ecosystem metabolic pulses following precipitation events (2008, methodology): “Similar procedures have been widely used in ecology and particularly for understanding eddy-covariance derived fluxes (Braswell et al . 2005; Richardson & Hollinger 2005; Sacks, Schimel & Monson 2007; Wang et al . 2007).”
A conceptual and practical approach to data quality and analysis procedures for high‐frequency soil respiration measurements (2008, methodology): “Given the error characteristics of the random SR measurement error, a weighted absolute deviations optimization scheme (rather than ordinary least squares optimization) is recommended to obtain maximum likelihood estimates of model parameters (Press et al. 1992; Richardson & Hollinger 2005).”