Temporal patterns and biophysical controls on methane emissions from a small eutrophic reservoir: insights from two years of eddy covariance monitoring
- 1Center for Environmental Measurements and Modeling, United States Environmental Protection Agency, Office of Research and Development, Cincinnati, 45268, USA
- 2Neptune and Company, Inc., Lakewood, 80215, USA
- 3Pegasus Technical Services, Cincinnati, 45268, USA
- 4Miami University, Department of Biology, Oxford, 45056, USA
- 5Center for Environmental Measurements and Modeling, United States Environmental Protection Agency, Office of Research and Development, Durham, 27709, USA
Abstract. Waters impounded behind dams (i.e. reservoirs) are important sources of greenhouses gases, especially methane (CH4), but their contribution is not well constrained due to high spatial and temporal variability, limitations in monitoring methods to characterize hot spot and hot moment emissions, and the limited number of studies that investigate diurnal, seasonal, and interannual patterns in emissions. In this study, we investigate the temporal patterns and biophysical drivers of CH4 emissions from Acton Lake, a small eutrophic reservoir, using a combination of methods: eddy covariance monitoring, continuous warm-season ebullition measurements, spatial emission surveys, and measurements of key drivers of CH4 production and emission. We used an artificial neural network to gap-fill the eddy covariance time series and to explore the relative importance of biophysical drivers on the inter-annual timescale. Acton Lake had cumulative areal emission rates of 40.6 ± 5.9 and 71.4 ± 4.2 g CH4 m−2 in 2017 and 2018, respectively, or 97.4 ± 14 and 171 ± 10 Mg CH4 in 2017 and 2018 across the whole 2.4 km2 area of the lake. The main difference between years was a period of elevated emissions lasting less than two weeks in the spring of 2018, which contributed 17 % of the total annual emissions, and was likely due to favourable sediment temperature and algal carbon substrate availability in 2018 compared to 2017. CH4 emissions only displayed diurnal patterns 18.5 % of the monitoring period, suggesting that factors that do not follow a diurnal pattern (e.g. substrate availability) may be driving emissions. Combining spatially extensive measurements with temporally continuous monitoring enabled us to quantify aspects of the spatial and temporal variability in CH4 emission. We found that the relationships between CH4 emissions and sediment T depended on location within the reservoir and observed a clear spatio-temporal offset in maximum CH4 emissions as a function of reservoir depth. These findings suggest a strong spatial pattern in CH4 biogeochemistry within this relatively small (2.4 km2) reservoir. In addressing the need for a better understanding of GHG emissions from reservoirs, there is a trade-off in intensive measurement of one water body versus short-term and/or spatially limited measurements in many water bodies. The insights from multi-year, continuous, spatially extensive studies like this one can be used to inform both the study design and emission upscaling from spatially or temporally limited results, specifically the importance of trophic status and intra-lake variability in assumptions about upscaling CH4 emissions.
Sarah Waldo et al.
Sarah Waldo et al.
Artificial Neural Network (ANN) Resampling Results for Gap Filling Eddy Covariance Data https://doi.org/10.5281/zenodo.3995098
Model code and software
R Code for: Temporal patterns and biophysical controls on methane emissions from a small eutrophic reservoir: insights from two years of eddy covariance monitoring https://doi.org/10.5281/zenodo.4540271
Sarah Waldo et al.
Viewed (geographical distribution)