Evaluating a Dissolved Organic Carbon Model For Daycent Within a Bayesian Framework.
Poster Number 2817
Monday, November 4, 2013
Tampa Convention Center, East Hall, Third Floor
Eleanor Campbell1, William J. Parton2, Jennifer Soong1, M. Francesca Cotrufo3 and Keith Paustian4, (1)Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO (2)Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO (3)Soil and Crop Sciences, Colorado State University, Fort Collins, CO (4)Dept. Soil and Crop Sciences, Colorado State University, Fort Collins, CO
The water-driven movement of dissolved organic carbon (DOC) through soils is an important component of vertical carbon (C) dynamics, acting as a C transport mechanism down the soil profile. DAYCENT- a widely used process-based plant/soil ecosystem model- is typically used to simulate plant/soil C and nitrogen dynamics as well as trace gas fluxes and other ecosystem variables in surface soil layers, generally from 0 – 20 or 30cm. Currently DAYCENT does not explicitly model DOC movement with water flow, which limits its ability to simulate vertical soil C dynamics and deep soil C storage. This affects the use of DAYCENT to evaluate bioenergy feedstock production systems, where deep-rooted and perennial species are considered in part for their potential contributions to soil C storage in deeper soil layers. We propose adding a DOC pool to the conceptual 3-pool DAYCENT soil C submodel, allowing us to use DAYCENT to simulate vertical C and deep soil C dynamics. We evaluated a proposed DOC model for the surface litter layer, testing model performance against experimental data for several types of litter using a Bayesian approach. We propose incorporating this soil DOC model into DAYCENT’s soil C submodel, to allow for the simulation of vertical soil C transfer by DOC. Combined with other vertical C movement mechanisms in soils, such as root inputs and animal mixing, this will allow us to develop the simulation of deep soil C dynamics in DAYCENT and improve the comparative evaluation of bioenergy feedstock production systems.