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Uncertainty of evapotranspiration fluxes measured with eddy covariance

To achieve this goal, we need reliable information about the ecosystem and its drivers. We measure, for example, mass and energy exchange between the ecosystem and the lower atmosphere with the eddy covariance method, which allows us to obtain data on a half-hourly scale. Nevertheless, unfavorable weather conditions, as well as malfunctions of the instruments, can lead to a serious amount of data gaps. Different gap-filling methods are available, with the Marginal Distribution Sampling (MDS) by Reichstein et al. (2005) being the most common one. Here, we investigate, how different filling approaches influence the uncertainty of evapotranspiration (ET) data for a German forest. We especially focus on the imputation of evaporation from intercepted canopy water, because open-path EC systems rarely work correctly during and after rain events.