Abstract

The process of quality control of micrometeorological and carbon dioxide (CO2) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO2 flux data derived from a Bowen ratio energy balance system. The results w ere compared with those produced by five experts who applied the current and potentially subjective protocol. All protocols were tested on the same set of three 5-day periods, when measurements were conducted in an abandoned agricultural field. The concordance of the protocols was evaluated by using the experts' opinion (mean ± 1.96 standard deviations) as a reference interval (the Bland-Altman method). Analysing the 15 days together, the statistical protocol that combined multivariate distance, multiple linear regression and time series analysis showed a concordance of 93% on a 20-min flux basis and 87% on a daily basis (only 2 days fell outside the reference interval), and the overall flux differed only by 1.7% (3.2 g CO 2 m-2). An automated version of this or a similar statistical protocol could be used as a standard way of filling gaps and processing data from Bowen ratio energy balance and other techniques (e.g. eddy covariance). This would enforce objectivity in comparisons of CO2 flux data that are generated by different research groups and streamline the protocols for quality control. © 2007 Royal Statistical Society.