Testing the applicability of neural networks as a gap-filling method using CH₄ flux data from high latitude wetlands

Abstract

Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes in order to recover missing data points, explained the method and tested its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. In keeping with the principle of parsimony, we included only five standard meteorological variables traditionally measured at CH4 flux measurement sites. These included drivers such as air and soil temperature, barometric air pressure, solar radiation, and in addition wind direction (indicator of source location). Four fuzzy sets were included representing the time of day. High Pearson correlation coefficients (r) of 0.76–0.93 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach that we showed to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.