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Methane Emissions from a Eutrophic Reservoir: Using Non-Linear Models to Gap Fill and Assess Biophysical Drivers
Citation:
Waldo, S., J. Beaulieu, W. Barnett, AND Johnt Walker. Methane Emissions from a Eutrophic Reservoir: Using Non-Linear Models to Gap Fill and Assess Biophysical Drivers. American Meteorological Society Fourth Conference on Biogeosciences, Boise,ID, May 13 - 18, 2018.
Impact/Purpose:
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 of the various emission pathways, including ebullition and diffusion. We used the eddy covariance method to measure fluxes of CH4 over a mid-sized (2.4 km2), eutrophic reservoir in southeast Ohio, US from winter 2017 thru spring of 2018. In addition to the eddy covariance system, we deployed inverted funnels and floating chambers to partition ebullitive from diffusive fluxes, and a thermistor chain to relate underwater convective mixing to the air-water gas exchange. Measuring reservoir fluxes using the eddy covariance method poses logistical challenges that result in data gaps. Power loss resulted in missing 32% of the 30-minute CH4 flux measurement periods. Furthermore, our instrumentation was sited ca. 24 m from the reservoir-land transition on a dock pylon. Filtering the dataset to exclude periods when the flux footprint was over land resulted in < 27% data coverage. We used wavelet analysis and neural network modeling to gap fill the dataset and to assess biophysical controls on CH4 fluxes. There was a clear seasonal trend in the methane fluxes. Emissions from the reservoir increased from winter to summer, with average fluxes of 14.1 ± 2.2 mg CH4 m-2 d-1 from February – April, increasing to a maximum of 580 ± 31 mg CH4 m-2 d-1 in August. By November, emissions had decreased back to an average of 19.3 ± 4.1 mg CH4 m-2 d-1. Ebullition accounted for >80% of total CH4 emissions. We found that temperature was positively correlated with CH4 emissions, while hydrostatic pressure had an inverse relationship.
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 of the various emission pathways, including ebullition and diffusion. We used the eddy covariance method to measure fluxes of CH4 over a mid-sized (2.4 km2), eutrophic reservoir in southeast Ohio, US from winter 2017 thru spring of 2018. In addition to the eddy covariance system, we deployed inverted funnels and floating chambers to partition ebullitive from diffusive fluxes, and a thermistor chain to relate underwater convective mixing to the air-water gas exchange. Measuring reservoir fluxes using the eddy covariance method poses logistical challenges that result in data gaps. Power loss resulted in missing 32% of the 30-minute CH4 flux measurement periods. Furthermore, our instrumentation was sited ca. 24 m from the reservoir-land transition on a dock pylon. Filtering the dataset to exclude periods when the flux footprint was over land resulted in < 27% data coverage. We used wavelet analysis and neural network modeling to gap fill the dataset and to assess biophysical controls on CH4 fluxes. There was a clear seasonal trend in the methane fluxes. Emissions from the reservoir increased from winter to summer, with average fluxes of 14.1 ± 2.2 mg CH4 m-2 d-1 from February – April, increasing to a maximum of 580 ± 31 mg CH4 m-2 d-1 in August. By November, emissions had decreased back to an average of 19.3 ± 4.1 mg CH4 m-2 d-1. Ebullition accounted for >80% of total CH4 emissions. We found that temperature was positively correlated with CH4 emissions, while hydrostatic pressure had an inverse relationship.