HR: 0800h
AN: B21B-1032    [Abstracts]
TI: Identifying critical parameters in a terrestrial ecosystem model for accuracy improvement through integration of data and/or assimilation
AU: * Baruah, P J
EM: pjbaruah@iis.u-tokyo.ac.jp
AF: Institute of Industrial Science (IIS) The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 1538505 Japan
AU: Yasuoka, Y
EM: yyasuka@iis.u-tokyo.ac.jp
AF: Institute of Industrial Science (IIS) The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 1538505 Japan
AU: Itoh, A
EM: itoh@jamstec.go.jp
AF: Frontier Research Center for Global Change Ecosystem Change Research Program, 3173-25 Showamachi, Kanazawa-ku, Yokohama, 2360001 Japan
AU: Dye, D
EM: dye@jamstec.go.jp
AF: Frontier Research Center for Global Change Ecosystem Change Research Program, 3173-25 Showamachi, Kanazawa-ku, Yokohama, 2360001 Japan
AB: Previous studies on model inter-comparison among existing terrestrial ecosystem models agreed on basic features of the biospheres, but showing considerable differences in total estimates due to differences in model assumptions about vegetation structure, model parameterization and input datasets. In order to enhance the confidence in the estimates, the first step can be assimilation of critical parameters or integration of satellite-derived datasets in the ecosystem model employing most recent parameterizations and reliable vegetation structures. This research aimed at identifying such critical parameters for the purpose of integration of satellite derived data products and/or assimilation in the terrestrial ecosystem model SimCYCLE. SimCYCLE is a process-based model having five compartments and estimates monthly and yearly global terrestrial NPP (Net Primary Productivity) within reasonable limits. The process of identifying the critical parameters as well the integration of data and assimilation in essentially involved three steps; first, sensitivity analysis of selected internal and input parameters at different steps of a simulation; second, selection of input parameters based on the sensitivity analysis, integration of validated satellite derived datasets and validation of estimated NPP with ground truth GPPDI (Global Primary Productivity Data Initiative); third, selection of parameters for assimilation based on sensitivity analysis and assimilation based on validated NPP in the second step. With the constraint on the availability of reliable satellite derived datasets, Leaf Area Index (LAI) and fractional Absorbed Photosynthetically Active Radiation (fAPAR) were selected as the input parameters for integration of satellite-derived datasets. Specific Leaf Area (SLA) and the extinction coefficient (Kd) were the two internal parameters selected for assimilation.
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0466 Modeling
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: Fall Meeting 2005