Abstract: Eddy covariance method has become a key technique to measure CH₄ flux continuously in lakes. A large number of CH₄ flux data was missing due to variable reasons. In order to reconstruct a complete time series of CH₄ flux, it is necessary to find an appropriate gap-filling method to insert the CH₄ flux data gap. Based on the routine meteorological data and CH₄ flux data measured at Bifenggang site in the eastern part of the Taihu eddy flux network during 2014 to 2017, we analyzed the control factors of CH₄ flux at the half-hour scale and daily scale. With those data, we tested that whether nonlinear regression method and two machine learning methods, random forest algorithm and error back propagation algorithm, could fill the CH₄ flux gap at the half-hour scale and daily scale. The results showed that CH₄ flux at the half-hour scale was mainly influenced by sediment temperature, friction velocity, air temperature, relative humidity, latent heat flux and water temperature at 20 cm in the growing season, and was mainly affected by relative humidity, latent heat flux, wind speed, sensible heat flux and sediment temperature in non-growing season. The CH₄ flux at the daily scale was mainly affected by latent heat flux and relative humidity. Random forest model was the best in CH₄ flux data gap filling at both time scales. The random forest model with the input variables of day of year, solar elevation angle, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, and wind speed was more suitable for filling the CH₄ flux data gap at the half-hour scale. The random forest model with the input variables of day of year, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, wind speed, and downward shortwave radiation was more suitable for filling CH₄ flux data gap at the day scale. The interpolation models could fill the data gap better at daily scale than that at the half-hour scale.

Key words: eddy covariance method, CH₄ flux, gap filling, random forest, back propagation neural network