B51B-0432
Using soil oxygen sensors to inform understanding of soil greenhouse gas dynamics
Friday, 18 December 2015
Poster Hall (Moscone South)
Terry Loecke1, Karla M Jarecke1, Amy J Burgin1, Trenton E Franz1 and Simonetta Rubol2, (1)University of Nebraska Lincoln, Lincoln, NE, United States, (2)University of Southern California, Los Angeles, CA, United States
Abstract:
Hot spots and hot moments of greenhouse gas (GHG) fluxes can contribute significantly to overall GHG budgets. Hot spots and hot moments occur when dynamic soil hydrology triggers important shifts in soil biogeochemical and physical processes that control GHG emissions. Soil oxygen (O2), a direct control on biogenic GHG production (i.e., nitrous oxide-N2O, carbon dioxide-CO2 and methane-CH4), may serve as both an important proxy for determining sudden shifts in subsurface biogenic GHG production, as well as the physical transport of soil GHG to the atmosphere. Recent technological advancements offer opportunities to link in-situ, near-continuous measurements of soil O2 concentration to soil biogeochemical processes and soil gas transport. Using high frequency data, this study asked: Do soil O2 dynamics correspond to changes in soil GHG concentrations and GHG surface fluxes? We addressed this question using precipitation event-based and weekly sampling (19 months in duration) data sets from a restored riparian wetland in Ohio, USA. During and after precipitation events, changes in subsurface (10 and 20 cm) CO2 and N2O concentrations were inversely related to short-term (< 48 h) changes in soil O2 concentrations. Subsurface CH4 concentrations changes during precipitation events, however, did not change in response to soil O2 dynamics. Changing subsurface GHG concentrations did not necessarily translate into altered surface (soil to atmosphere) GHG fluxes; soil O2 dynamics at 10 cm did not correspond with changes in surface N2O and CH4 fluxes. However, changes in soil O2 concentration at 10 cm had a significant positive linear relationship with change in surface CO2 flux. We used a random forest approach to identify the soil sensor data (O2, temperature, moisture) which contribute the most to predicting weekly GHG fluxes. Our study suggests that monitoring near-continuous soil O2 concentration under dynamic soil hydrology may lead to greater understanding of GHG emissions and incorporation of hot spots and hot moments into GHG modeling efforts.
