Quantifying the impact of emission outbursts and non-stationary flow on eddy covariance CH4 flux measurements using wavelet techniques

Methane flux measurements by the eddy-covariance technique are subject to large uncertainties, particularly linked to the partly highly intermittent nature of methane emissions. Outbursts of high methane emissions, termed event fluxes, hold the potential to introduce systematic biases into derived methane budgets, since under such conditions the assumption of stationarity of the flow is violated. In this study, we investigate the net impact of this effect by comparing eddy-covariance fluxes against a wavelet-derived reference that is not negatively influenced by non-stationarity. Our results demonstrate that methane emission events influenced 3-4 % of the flux measurements, and did not lead to systematic biases in methane budgets for the analyzed summer season; however, the presence of events substantially increased uncertainties in short-term flux rates. The wavelet results provided an excellent reference to evaluate the performance of three different gapfilling approaches for eddy-covariance methane fluxes, and we show that none of them could reproduce the range of observed flux rates. The integrated performance of the gapfilling methods for the longer-term dataset varied between the two eddy-covariance towers involved in this study, and we show that gapfilling remains a large source of uncertainty linked to limited insights into the mechanisms governing the short-term variability in methane emissions. With the capability to broaden our observational methane flux database to a wider range of conditions, including the direct resolution of short term variability at the order of minutes, wavelet-derived fluxes hold the potential to generate new insight into methane exchange processes with the atmosphere, and therefore also improve our understanding of the underlying processes.