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Since the advancement in CH4 gas analyser technology and itsapplicability to eddy covariance flux measurements, monitoring of CH4emissions is becoming more widespread. In order to accurately determine thegreenhouse gas balance, high quality gap-free data is required. Currentlythere is still no consensus on CH4 gap-filling methods, and methodsapplied are still study-dependent and often carried out on low resolution,daily data. In the current study, we applied artificial neural networks to sixdistinctively different CH4 time series from high latitudes, explainthe method and test its functionality. We discuss the applicability ofneural networks in CH4 flux studies, the advantages and disadvantagesof this method, and what information we were able to extract from suchmodels. Three different approaches were tested by including drivers such as air andsoil temperature, barometric air pressure, solar radiation, wind direction(indicator of source location) and in addition the lagged effect of watertable depth and precipitation. In keeping with the principle of parsimony,we included up to five of these variables traditionally measured at CH4flux measurement sites. Fuzzy sets were included representing the seasonalchange and time of day. High Pearson correlation coefficients (r) of up to0.97 achieved in the final analysis are indicative for the high performanceof neural networks and their applicability as a gap-filling method forCH4 flux data time series. This novel approach which we show to beappropriate for CH4 fluxes is a step towards standardising CH4gap-filling protocols

biogeosciences

Testing the applicability of neural networks as a gap-filling method using CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; flux data from high latitude wetlands