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Chinese Journal of Applied Ecology ›› 2022, Vol. 33 ›› Issue (10): 2785-2795.doi: 10.13287/j.1001-9332.202210.021

• Original Articles • Previous Articles     Next Articles

Evaluation of gap-filling methods for CH4 flux data based on eddy covariance method in the Lake Taihu, China

QIU Ji-li1, ZHANG Mi1*, PU Yi-ni1, ZHANG Zhen2, JIA Lei1, ZHAO Jia-yu1, XIAO Wei1, LIU Shou-dong1   

  1. 1Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science & Technology, Nanjing 210044, China;
    2Jiangning District Meteorological Bureau, Nanjing 211100, China
  • Received:2021-11-17 Revised:2022-07-18 Online:2022-10-15 Published:2023-04-15

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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