HR: 1340h
AN: B53B-1173    [Abstracts]
TI: Objective refinements to a diagnostic terrestrial biosphere model using satellite data: North America carbon and water cycle simulations
AU: * Ichii, K
EM: kazuhito.ichii@gmail.com
AF: Faculty of Symbiotic Systems Science, Fukushima University, 1 Kanayagawa, Fukushima, 960-1296, Japan
AU: * Ichii, K
EM: kazuhito.ichii@gmail.com
AF: Department of Geography, San Jose State University, One Washington Square, San Jose, CA 95192, United States
AU: * Ichii, K
EM: kazuhito.ichii@gmail.com
AF: NASA Ames Research Center, MS242-5, Moffett Field, CA 94035, United States
AU: Wang, W
EM: weile.wang@gmail.com
AF: NASA Ames Research Center, MS242-5, Moffett Field, CA 94035, United States
AU: Wang, W
EM: weile.wang@gmail.com
AF: California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
AU: Hashimoto, H
EM: hirofumi.hashimoto@gmail.com
AF: California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
AU: Yang, F
EM: feihuayang@wisc.edu
AF: Department of Geography, University of Wisconsin-Madison, 426 Science Hall, 550 North Park Street, Madison, WI 53706, United States
AU: Votava, P
EM: pvotava@mail.arc.nasa.gov
AF: NASA Ames Research Center, MS242-5, Moffett Field, CA 94035, United States
AU: Votava, P
EM: pvotava@mail.arc.nasa.gov
AF: California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
AU: Michaelis, A R
EM: amac@hyperplane.org
AF: NASA Ames Research Center, MS242-5, Moffett Field, CA 94035, United States
AU: Michaelis, A R
EM: amac@hyperplane.org
AF: California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, United States
AU: Nemani, R R
EM: rama.nemani@nasa.gov
AF: NASA Ames Research Center, MS242-5, Moffett Field, CA 94035, United States
AB: We established a framework for objective improvement of a diagnostic terrestrial biosphere model (Terrestrial Observation and Prediction System; TOPS) using satellite-derived products including snow cover, evapotranspiration (ET), and gross primary productivity (GPP). Based on the TOPS model structure, we established an objective improvement process by first optimizing snow submodel, then soil water submodel, and finally gross primary production submodel. We used MODIS snow cover products (MOD10A2) for snow submodel improvements, Support Vector Machine (SVM) based ET estimation for soil water submodel improvements, and SVM-based GPP estimation for GPP model improvements as satellite-derived products. Snow submodel refinement has shown an improvement on snow dynamics, streamflow, ET, and GPP over high latitude areas. Soil water cycle submodel refinement has shown an improvement on seasonal ET and GPP variations for seasonally-dry regions. GPP submodel refinement has shown an improvement on seasonal and annual GPP over the vegetated regions. Our analysis shows that the objective improvement of terrestrial ecosystem model is an effective way for improving model performance. Carbon and water cycle simulation over North America were greatly improved as a result of the model improvements.
UR: http://ecocast.arc.nasa.gov
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0480 Remote sensing
DE: 1655 Water cycles (1836)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting