Diurnal cycle of lake methane flux

Abstract

Air-lake methane flux (FCH 4 ) and partial pressure of methane in the atmosphere (pCH 4a ) were measured using the eddy covariance method over a Swedish lake for an extended period. The measurements show a diurnal cycle in both FCH 4 and pCH 4a with high values during nighttime (FCH 4 ≈ 300 nmol m À2 s À1 , pCH 4a ≈ 2.5 μatm) and low values during day (FCH 4 ≈ 0 nmol m À2 s À1 , pCH 4a ≈ 2.0 μatm) for a large part of the data set. This diurnal cycle persist in all open water season; however, the magnitude of the diurnal cycle is largest in the spring months. Estimations of buoyancy in the water show that high nighttime fluxes coincide with convective periods. Our interpretation of these results is that the convective mixing enhances the diffusive flux, in analogy to previous studies. We also suggest that the convection may bring methane-rich water from the bottom to the surface and trigger bubble release from the sediment. A diurnal cycle is not observed for all convective occasions, indicating that the presence of convection is not sufficient for enhanced nighttime flux; other factors are also necessary. The observed diurnal cycle of pCH 4a is explained with the variation of FCH 4 and a changing internal boundary layer above the lake. The presence of a diurnal cycle of FCH 4 stresses the importance of making long-term continuous flux measurements. A lack of FCH 4 measurements during night may significantly bias estimations of total CH 4 emissions from lakes to the atmosphere.

Comparison of floating chamber and eddy covariance measurements of lake greenhouse gas fluxes

“…For a more detailed description see Podgrajsek et al (2014) and . The EC data fulfilling the following criteria were used: wind direction from the lake, RSSI (received signal strength indicator, measure of the LI-7700 signal strength) > 30 % when logged, wind speed > 1 m s −1 , no precipitation and high quality power spectra.…”

“…2a and b), FCH 4EC1 frequently displayed a diurnal cycle with higher values during night-time than during day. The diurnal cycle of FCH 4 is presented in detail by Podgrajsek et al (2014) where it was suggested that the onset of a diurnal cycle of FCH 4 was controlled by water-side convection and formation of methane in the sediment. Such a pattern with convective driven high night-time fluxes was previously observed using flux chambers (Crill et al, 1988;Godwin et al, 2013), while studies from other lakes have found higher daytime CH 4 emissions (e.g.…”

High net CO<sub>2</sub> and CH<sub>4</sub> release at a eutrophic shallow lake on a formerly drained fen

“…We assume the process of convective mixing of the water column (e.g. Godwin et al, 2013;Poindexter and Variano, 2013;Podgrajsek et al, 2014;Sahlée et al, 2014;Koebsch et al, 2015) to be crucial for the diurnal pattern of CH 4 emissions at our study site. This is indicated by the concurrent timing of convective mixing and daily peak CH 4 emissions and a generally high fractional source area coverage of the open water, which shows higher rates of CH 4 release than emergent vegetation.…”

“…Due to the heat release of the surface water to the atmosphere in the night the surface water cools down, initiating convective mixing of the water column down to the bottom. Diffusion is enhanced due to the buoyancy-induced turbulence, the associated increased gas transfer velocity at the air-water interface (Eugster et al, 2003;MacIntyre et al, 2010;Podgrajsek et al, 2014) as well as the transport of CH 4 enriched bottom water to the surface (Godwin et al, 2013;Podgrajsek et al, 2014). In addition, ebullition can be triggered by turbulence due to convective mixing Read et al, 2012).…”

Spatial and Temporal Correlates of Greenhouse Gas Diffusion from a Hydropower Reservoir in the Southern United States

“…First, we used TBL estimates of surface diffusion as the response variable, yet surface diffusion rates based on such estimates are inherently less accurate than direct measurements with floating chambers [21]. Second, diurnal cycling of CO2 and CH4 can influence rates of surface diffusion [5,38,39]; however, we did not include predictor variables to account for such diel variation. Third, although we included a variety of environmental variables as predictors, including other unmeasured environmental factors in statistical models would likely improve model performance.…”