The Florida Everglades is a highly managed subtropical wetland with significant potential to influence regional climate via greenhouse gas (GHG) emissions of carbon dioxide (CO2) and methane (CH4). Past studies have shown dramatically different GHG emissions schemes between long and short hydroperiod marshes based on seasonality, in terms of CO2 source-sink potential and also total methane release. Despite ongoing research for individual GHGs in wetlands, we still have a limited understanding of the degree to which combined GHGs offset or augment each other in terms of net radiative forcing in different wetland ecosystems. Additionally, relatively little is known about the environmental controls on CH4 fluxes and we have yet to identify the drivers most likely to affect the net impact of wetland GHG emissions in terms of total global warming potential (GWP).
The objectives of this research are to describe differences in net radiative forcing between long and short hydroperiod marshes and determine the biotic and abiotic drivers of GHG emissions that alter the GWP of the ecosystem. We use carbon flux data (both CO2 and CH4) collected using eddy covariance methods from two sites within the Everglades, long hydroperiod marsh Shark River Slough and short hydroperiod marsh Taylor Slough.
Preliminary data comparing CO2 and CH4 carbon emissions shows strong patterns of seasonal variability for radiative forcing in short hydroperiod marshes as opposed to long hydroperiod marshes, which exhibited a much more stable emissions scheme over the course of a year. Additionally, the short hydroperiod marshes exhibited a much wider range of net GHG fluxes than did the long hydroperiod marshes despite experiencing very similar environmental conditions, indicating different environmental variables acting as main drivers of radiative forcing, and ultimately the GWP, in either ecosystem. The next step in our project will be the application of time series analyses to quantify relationships between the flux data and environmental variables, including their correlation structures, in order to identify key drivers at each site. Results from this study have important implications for both predictive models of wetland GHG emissions as well as wetland management, especially in the face of both climate change and the Comprehensive Everglades Restoration Plan.