[Evaluation of gap-filling methods for CH4 flux data based on eddy covariance method in the Lake Taihu, China]. / æ¶¡åº¦ç›¸å ³è§‚æµ‹çš„å¤ªæ¹–CH4通量数据插补方法评价.

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.

[Temporal and Spatial Variation Characteristics of Methane, Carbon Dioxide, and Nitrous Oxide Concentrations and the Influencing Factors in Small Aquaculture Pond].

As an important source of greenhouse gases, the changes in greenhouse gas concentrations of aquaculture ponds are not only the basis for accurate quantification of greenhouse gases emissions but are also important for identifying their influencing factors. The spatial and temporal variation characteristics of CH4, CO2, and N2O concentrations and the influencing factors in a typical small aquaculture pond in the Yangtze River Delta were analyzed based on the headspace equilibrium-gas chromatograph method. Except in spring, the concentrations of CH4, and N2O appeared high at noon or afternoon and were influenced by water temperature. Impacted by water temperature and aquatic plant photosynthesis, the concentrations of CO2 were high in the morning when photosynthesis was weak. The concentrations of CH4 and CO2 were the highest in autumn and the lowest in winter. The mean concentrations of CH4 in autumn and winter were 176.34 nmol·L-1 and 32.75 nmol·L-1, respectively, which were mainly affected by air temperature, water temperature, and dissolved oxygen. The average CO2 concentrations in autumn and winter were 134.37 µmol·L-1 and 23.10 µmol·L-1, respectively, and were mainly affected by aquatic vegetation photosynthesis and pH. N2O concentration was the highest in summer and the lowest in winter, with mean values of 97.05 nmol·L-1 and 19.41 nmol·L-1, respectively, which were mainly affected by air temperature and water temperature. In terms of the vertical spatial variations of the three greenhouse gases, the concentration of CH4decreased with water depth in summer, and the concentration differences between the surface layer and the bottom and middle layers were 71.28 nmol·L-1 and 42.80 nmol·L-1, respectively. The concentration of CH4 increased with water depth in autumn, and the concentration difference between the bottom layer and surface layer was 163.94 nmol·L-1. The CO2 concentration increased with water depth in summer and autumn. The concentration differences between the bottom and surface concentrations were 18.69 µmol·L-1 and 29.90 µmol·L-1, respectively. N2O concentration showed no obvious change in the vertical direction. For the horizontal variations, the concentrations of CH4, CO2, and N2O in the feeding area in summer and in chicken manure in spring were approximately 1.34-1.98 times and 1.95-2.42 times those in other areas, respectively, and the concentrations of N2O and CO2 in spring and summer were approximately 1.13-1.26 times and 1.39-1.74 times those in other areas.