Combining Measurements and Models to Estimate Carbon Sequestration. (A03-bostick464867-poster)
Authors:
W.M. Bostick* - Univ. of Florida J. Koo - Univ. of Florida
J.W. Jones - Univ. of Florida
A.J. Gijsman - CIAT/Univ. of Florida
Abstract:
One challenge to implementing carbon (C) trading is verifying C sequestration over large areas. In this study, a data assimilation technique, the Ensemble Kalman Filter (EnKF), was used to improve C sequestration estimates. The EnKF combines measurements, model estimates, and the uncertainties thereof to optimally estimate system state variables and parameters. The EnKF provides a measure of confidence in these estimates and can be used to model phenomena with both temporal and spatial variability. Our EnKF was implemented with a C model containing fresh, humic, and stable soil C pools and a plant biomass pool. The latter can be estimated using a cropping system model, remote
sensing-based model or direct measurements. Our EnKF was used to update the status of the soil C pools and selected rate parameters for a 50-year simulation. A sensitivity analysis was conducted to evaluate the effects of uncertainties in measurements, model predictions, and selected rate parameters on EnKF estimates. The EnKF provides a framework for handling the uncertainty involved with verifying C sequestration.
Speaker Information: W. McNair Bostick, Univ. of Florida, Agricultural and Biological Engineering Department, Gainesville, FL 32611; Phone: 352-392-1864 ext. 292; E-mail: [email protected]
Session Information: Tuesday, November 4, 2003, 4:00 PM-6:00 PM Presentation Start: 4:00 PM (Poster Board Number: 118)
Keywords: kalman filter; data assimilation; crop model; carbon sequestration
