Simulating Greenhouse Gas Fluxes Across the Terrestrial–Aquatic Interface: A Spatially Explicit Approach


Debjani Sihi1* (, Alexandra Cory1, Jianqiu Zheng2, Taylor Cyle1, Eric Davidson3, Patrick Megonigal4, Michael Weintraub5, Zoe Cardon6, Suzanne Thomas6


1Emory University, Atlanta, GA; 2Pacific Northwest National Laboratory, Richland, WA; 3Center for Environmental Science Appalachian Laboratory, University of Maryland–Frostburg, MD; 4Smithsonian Environmental Research Center, Edgewater, MD; 5University of Toledo, Toledo, OH; 6Marine Biological Laboratory, Woods Hole, MA



Terrestrial–aquatic interfaces (TAIs) undergo frequent variations in soil saturation that result in heterogeneous distribution of greenhouse gas (GHG)–regulating components—such as dissolved organic carbon content, terminal electron acceptor (TEA) availability, pH, and nutrient abundance. The formation of soil microsites (i.e., microscale regions with unique biogeochemical characteristics compared to the surrounding area) enable seemingly disparate processes—such as oxic respiration and methanogenesis—to occur simultaneously within one soil column. Most GHG flux models fail to predict such co-occurrences as they assume a homogeneous distribution of GHG-regulating components across a given soil column. Here, the project addresses this discrepancy by developing a spatially explicit model of GHG fluxes in TAIs. To mimic in situ conditions, researchers conduct incubations of intact soil cores collected from a hydrology gradient in Old Woman Creek, OH, near Lake Eerie, which the team subjects to hydrological fluctuation. High-resolution two-dimensional maps of dioxygen concentration are obtained using colorimetric planar oxygen optode imaging. Spatially specific measurements of pH and redox state are obtained using microelectrodes, and dissolved nutrient/TEA abundances are obtained from extracted porewater samples. GHG fluxes are measured continuously using a Cavity Ring-Down Spectrophotometer (Picarro G2508). These measurements are used to generate probability distribution (or density) functions of GHG-regulating components. By employing a model-experiment framework and constraining a spatially explicit GHG flux model (DAMM-GHG) coupled with a redox-reactive network module (AquaMEND), the project assesses whether capturing redox heterogeneity in soil microsites aids with improved predictions of GHG fluxes from TAIs.