Combining Microbial Enzyme Kinetics Models with Light Use Efficiency Models to Predict CO2 and CH4 Ecosystem Exchange from Flooded and Drained Peatland Systems

Monday, 15 December 2014
Patricia Y Oikawa1, Darrel Jenerette2, Sara H Knox1, Cove S Sturtevant1, Joseph G Verfaillie1 and Dennis D Baldocchi1, (1)University of California Berkeley, Dept of Environmental Science, Policy, & Management, Berkeley, CA, United States, (2)University of California Riverside, Riverside, CA, United States
Under California’s Cap-and-Trade program, companies are looking to invest in land-use practices that will reduce greenhouse gas (GHG) emissions. The Sacramento-San Joaquin River Delta is a drained cultivated peatland system and a large source of CO2. To slow soil subsidence and reduce CO2 emissions, there is growing interest in converting drained peatlands to wetlands. However, wetlands are large sources of CH4 that could offset CO2-based GHG reductions. The goal of our research is to provide accurate measurements and model predictions of the changes in GHG budgets that occur when drained peatlands are restored to wetland conditions. We have installed a network of eddy covariance towers across multiple land use types in the Delta and have been measuring CO2 and CH4 ecosystem exchange for multiple years. In order to upscale these measurements through space and time we are using these data to parameterize and validate a process-based biogeochemical model. To predict gross primary productivity (GPP), we are using a simple light use efficiency (LUE) model which requires estimates of light, leaf area index and air temperature and can explain 90% of the observed variation in GPP in a mature wetland. To predict ecosystem respiration we have adapted the Dual Arrhenius Michaelis-Menten (DAMM) model. The LUE-DAMM model allows accurate simulation of half-hourly net ecosystem exchange (NEE) in a mature wetland (r2=0.85). We are working to expand the model to pasture, rice and alfalfa systems in the Delta. To predict methanogenesis, we again apply a modified DAMM model, using simple enzyme kinetics. However CH4 exchange is complex and we have thus expanded the model to predict not only microbial CH4 production, but also CH4 oxidation, CH4 storage and the physical processes regulating the release of CH4 to the atmosphere. The CH4-DAMM model allows accurate simulation of daily CH4 ecosystem exchange in a mature wetland (r2=0.55) and robust estimates of annual CH4 budgets. The LUE- and CH4-DAMM models will advance understanding of biogeochemisty and microbial processes in managed peatland systems as well as aid the development of a GHG protocol in the Delta that can provide financial incentive to farmers to reduce GHG emissions under California’s Cap and Trade program.