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Sediment Temperature Control on Methane Ebullition in a Small Eutrophic Reservoir
Citation:
Waldo, S., J. Beaulieu, W. Barnett, AND Johnt Walker. Sediment Temperature Control on Methane Ebullition in a Small Eutrophic Reservoir. Presented at American Geophysical Union Fall Metting, Washington, D.C., D.C, December 10 - 14, 2018.
Impact/Purpose:
The AGU Fall meeting is an opportunity to share results and get feedback from scientists conducting similar research that has not yet been published.
Description:
Reservoirs are a globally important source of methane (CH4) to the atmosphere, but measuring CH4 emission rates from reservoirs is difficult due to the spatial and temporal variability in emissions via the emission pathways of ebullition (bubbling) and diffusion. The dominant source of CH4 in reservoirs is production by methanogens in the reservoir sediment, a process that has been widely shown to have a positive correlation with temperature. However, oxidation of CH4 to carbon dioxide by methanotrophs, an important sink for CH4 within lakes, also scales with temperature. Understanding the relationship between reservoir CH4 emission (i.e. production – consumption) and temperature is made more complex by this dual feedback. This study presents results from multiple in-situ monitoring efforts at a small eutrophic reservoir in the Midwest US that look at how CH4 emissions vary with temperature across space and time. Using data sets from eddy covariance monitoring as well as inverted funnels, we found strong log relationships between daily average CH4 fluxes and daily average sediment temperature, with R^2 values of 0.58, 0.45, and 0.7 for the eddy covariance data, the inverted funnel deployed at the 1.3-m site (“shallow”), and the inverted funnel deployed at the 8-m site (“deep”), respectively. The Q10 values for the shallow and deep site were 32 and 20, respectively, indicating a stronger dependence on temperature at the shallow site. However, both the shallow and deep sites had similar emission rates, scaling with relative maximum sediment temperature at each respective site. Sediment temperature was also found to be the second most important variable input to the artificial neural network used for gap-filling the eddy covariance CH4 fluxes (after wind speed). Improving our understanding of the temperature – methane emission feedback in freshwaters will enhance our ability to predict future global methane emissions.