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Water 2015, 7(11), 5910-5927; doi:10.3390/w7115910

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

1
Oak Ridge National Laboratory Environmental Sciences Division, P.O. Box 2008, Oak Ridge, TN 37831-6351, USA
2
Xcel Engineering Inc., 1066 Commerce Park Drive, Oak Ridge, TN 37830-0117, USA
Present address: Department of Biological Sciences, Marshall University, 1 John Marshall Drive, Huntington, WV 25755-2510, USA.
Present address: Science Education Program, Oak Ridge Associated Universities, P.O. Box 117, MS 36, Oak Ridge, TN 37831-0117, USA.
*
Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Received: 25 August 2015 / Revised: 1 October 2015 / Accepted: 21 October 2015 / Published: 29 October 2015
View Full-Text   |   Download PDF [561 KB, uploaded 29 October 2015]   |  

Abstract

Emissions of CO2 and CH4 from freshwater reservoirs constitute a globally significant source of atmospheric greenhouse gases (GHGs), but knowledge gaps remain with regard to spatiotemporal drivers of emissions. We document the spatial and seasonal variation in surface diffusion of CO2 and CH4 from Douglas Lake, a hydropower reservoir in Tennessee, USA. Monthly estimates across 13 reservoir sites from January to November 2010 indicated that surface diffusions ranged from 236 to 18,806 mg·m−2·day−1 for CO2 and 0 to 0.95 mg·m−2·day−1 for CH4. Next, we developed statistical models using spatial and physicochemical variables to predict surface diffusions of CO2 and CH4. Models explained 22.7% and 20.9% of the variation in CO2 and CH4 diffusions respectively, and identified pH, temperature, dissolved oxygen, and Julian day as the most informative predictors. These findings provide baseline estimates of GHG emissions from a reservoir in eastern temperate North America, a region for which estimates of reservoir GHGs emissions are limited. Our statistical models effectively characterized non-linear and threshold relationships between physicochemical predictors and GHG emissions. Further refinement of such modeling approaches will aid in predicting current GHG emissions from unsampled reservoirs and forecasting future GHG emissions. View Full-Text
Keywords: climate change; CH4; CO2; hydropower; random forests model; reservoir climate change; CH4; CO2; hydropower; random forests model; reservoir
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mosher, J.J.; Fortner, A.M.; Phillips, J.R.; Bevelhimer, M.S.; Stewart, A.J.; Troia, M.J. Spatial and Temporal Correlates of Greenhouse Gas Diffusion from a Hydropower Reservoir in the Southern United States. Water 2015, 7, 5910-5927.

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