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

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 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 OPEN ACCESSWater 2015, 7 5911 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.

CO2 is Dominant Greenhouse Gas Emitted from Six Hydropower Reservoirs in Southeastern United States during Peak Summer Emissions

“…Coves were chosen for sampling over the main channel for logistic reasons (less boat traffic, shallower water depth, and slower currents) and because earlier sampling revealed that coves typically have greater ebullition rates than the main channel [29,30]. Inverted funnels of 1 m diameter opening (0.79 m 2 opening area) at approximately 1.5 m below the water surface collected gases overnight (15 to 28 h in 73 cases; but in 13 cases, funnels were deployed for up to 48 h due to storms that interfered with scheduled retrieval).…”

“…In an earlier investigation of Douglas Lake (Tennessee), we reported that emissions of both CO 2 and CH 4 were seasonal, and compared to reservoirs in other parts of the world, moderate (for CO 2 ) to low (for CH 4 ) in GHG emissions [29,30]. Based on these results and on unpublished data from quarterly sampling of three additional reservoirs in the southeast U.S., we chose a synoptic survey sampling strategy to characterize emissions from a variety of hydropower reservoirs within a geographically similar area during the period of peak emissions, August-September.…”

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

“…Unlike these models with the inputs of environmental factors, a mathematical model with the theory of Self-Organized Criticality (SOC) was employed to extrapolate values from one reservoir to another directly without any other features [22]. These pioneering models showed a low degree of precision and regional limitations; therefore, developing and optimizing models of reservoir CO 2 emissions is still one of the future research directions [6,12].…”

“…Since the problem of greenhouse gases (GHGs) emissions from hydroelectric reservoirs was first addressed in the publication in 1993 [1], it has been the focus of research around the world [2], especially in Canada [3,4], Brazil [5], and the United States [6,7]. Over the past two decades, a growing amount of work has documented reservoirs' roles as GHG sources [8,9], after extensive research was carried out in various reservoirs.…”

“…One of the most common models is the statistical regression model, which can demonstrate the relationship between CO 2 emissions and one [9] or several [11,16] factors by the regression equations. Other models that can identify more complex non-linear responses, such as Random Forests [6] and Monte Carlo simulation [21], were also used to elucidate the relationship between CO 2 emissions and the factors of environment or dams, and also to predict CO 2 emissions from other under-sampled reservoirs in the nearby geographic region. Unlike these models with the inputs of environmental factors, a mathematical model with the theory of Self-Organized Criticality (SOC) was employed to extrapolate values from one reservoir to another directly without any other features [22].…”