Seasonal variability of CO2, CH4, and N2O content and fluxes in small agricultural reservoirs of the northern Great Plains - Jensen, Sydney A.; Webb, Jackie R.; Simpson, Gavin L.; Baulch, Helen M.; Leavitt, Peter R.; Finlay, Kerri (Frontiers in Environmental Science, 2022)
Inland waters are important global sources, and occasional sinks, of CO2, CH4, and N2O to the atmosphere. In contrast, relatively little is known about the contribution of GHGs of constructed waterbodies, particularly small sites in agricultural regions which receive large amounts of nutrients (carbon, nitrogen, phosphorus). Here, we quantify the magnitude and controls of CO2, CH4, and N2O emissions from 20 agricultural reservoirs on seasonal and diel timescales. All gases exhibited consistent seasonal trends, with CO2 concentrations highest in spring and fall, and lowest in mid-summer, CH4 highest in mid-summer, and N2O elevated in spring following ice-off. No discernible diel trends were observed for GHG content. Analyses of GHG covariance with potential regulatory factors were conducted using generalized additive models (GAMs) that revealed CO2 concentrations were affected primarily by factors related to benthic respiration, including dissolved oxygen (DO), dissolved inorganic nitrogen (DIN), dissolved organic carbon (DOC), stratification strength, and water source (as 18Owater). In contrast, variation in CH4 content was correlated positively with factors that favoured methanogenesis, and so varied inversely with DO, soluble reactive phosphorus, and conductivity (a proxy for sulfate content), and positively with DIN, DOC, and temperature. Finally, N2O concentrations were driven mainly by variation in reservoir mixing (as buoyancy frequency), and were correlated positively with DO, soluble reactive phosphorus (SRP), and DIN levels and negatively with pH and stratification strength. Estimates of integrated CO2-eq flux over the entire open-water period revealed that small agricultural reservoirs were small net CO2 sources when estimated directly using monthly measurements and interpolated using generalized additive model smooths, but that emissions could be overestimated by over 10-fold if modeled single mid-summer samples, particularly if there was high levels of temporal variability.