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    Missing values in any data set create problems for researchers. The process by which missing values are replaced, and the data set is made complete, is generally referred to as imputation. Within the eddy flux community, the term "gap filling" is more commonly applied. A major challenge is that random errors in measured data result in uncertainty in the gap-filled values. In the context of eddy covariance flux records, filling long gaps (days to weeks), which are usually the result of instrument malfunction or system failure, is especially difficult because underlying properties of the ecosystem may change over time, resulting in additional uncertainties. We used synthetic data sets, derived by assimilating data from a range of FLUXNET sites into a simple ecosystem model, to evaluate the relationship between gap length and uncertainty in net ecosystem exchange (NEE) of CO2.

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    Richardson, Andrew D.; Hollinger, David Y. 2007. A method to estimate the additional uncertainty in gap-filled NEE resulting from long gaps in the CO2 flux record. Agricultural and Forest Meteorology. 147: 199-208.


    data assimilation, ecosystem physiology, eddy covariance, gap filling, Howland, Monte Carlo, phenology, random error, uncertainty

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