COS 13-9
Improving process-based modeling of CO2 and CH4 exchange from managed wetlands in the Sacramento-San Joaquin River Delta

Monday, August 10, 2015: 4:20 PM
336, Baltimore Convention Center
Patricia Y. Oikawa, Environmental Science Policy and Management, UC Berkeley, Berkeley, CA
Sara Knox, Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA
Cove Sturtevant, Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA
Joseph Verfaillie, Environmental Science, Policy, and Management, University of California - Berkeley, Berkeley, CA
Iryna Dronova, College of Environmental Design, University of California at Berkeley, Berkeley, CA
Christina Poindexter, Lawrence Berkeley National Lab, CA
G. Darrel Jenerette, Department of Botany and Plant Sciences, University of California, Riverside, CA
Dennis Baldocchi, Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA

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 we are using a data assimilation approach to parameterize and validate a new process-based biogeochemical model: the Peatland Arrhenius Michaelis-Menten (PAMM) model. 


To predict gross primary productivity (GPP), we employ a simple light use efficiency model which can explain 90% of the observed variation in GPP in a mature wetland. To predict ecosystem respiration (Reco) we employ two Michaelis-Menten (MM) equations paired with Arrhenius equations to describe temperature-sensitive soil carbon pools: recently-fixed photosynthetic carbon and soil organic matter. By modeling both GPP and Reco, the PAMM model accurately simulates half-hourly net ecosystem exchange of CO2 (NEE) in a mature wetland (r2=0.85). To predict CH4 exchange, we simulate methanogenesis using two MM equations paired with Arrhenius equations, where most methane is derived from recently-fixed carbon. We also simulate methane oxidation using one MM equation, with methane as the only substrate. As the wetlands in the Delta are densely vegetated, only plant-mediated and hydrodynamic transport of CH4 are modeled, ignoring ebullition. By modeling CH4 production, oxidation, and transport, the PAMM model accurately simulates daily ecosystem exchange of CH4 in a mature wetland (r2=0.62). A second year of data were used to validate the model and found the model to accurately predict both annual CO2 (observed = -495g C m-2 yr-1; modeled = -450 ±51g C m-2 yr-1) and CH4 exchange (observed = 27g C m-2 yr-1; modeled = 31 ±2g C m-2 yr-1). Our work advances biogeochemical modeling in managed peatland systems and aids the development of a GHG protocol that can be employed under California’s Cap and Trade program.