HR: 1340h
AN: B23C-1490    [Abstracts]
TI: Frameworks, Schemes and Hierarchies for Reducing Uncertainties in Estimation of Carbon Dynamics in Tree-Grass Systems
AU: * Hill, M J
EM: hillmj@aero.und.edu
AF: Department of Earth System Science and Policy, University of North Dakota, Clifford Hall Stop 9011, 4149 Campus Drive, Grand Forks, ND 58201, United States
AU: Hanan, N P
EM: niall.hanan@colostate.edu
AF: Natural Resource Ecology Laboratory, Colorado State University, Campus Mail 1499, Fort Collins, CO 80523-1499, United States
AB: The global area of savannas varies between 15 and 37 M km², depending upon inclusion of grasslands, dry woodlands and temperate tree-grass systems. There are large uncertainties in the carbon budgets of savannas, influenced by several factors: 1) large spatial extent and diversity of vegetation structure and floristics; 2) scarcity of field measured soil and biomass carbon stocks and fluxes; 3) non-linear relationships between vegetation dynamics, carbon stocks, carbon fluxes and climate across the savanna rainfall gradient; and 4) variation from seasonally inundated to semi-arid moisture regimes. Current approaches to assessing carbon dynamics of savannas may be grouped as: a) global scale biogeochemical models (e.g. Century, Biome-BGC) that simulate nutrient and carbon dynamics in response, primarily, to climate; b) dynamic global vegetation models (e.g. LPJ, IBIS) that simulate competitive interactions among plant functional types; c) land use change models in which net fluxes are computed based on estimated carbon stocks in ‘natural' versus transformed landscapes; d) inversion approaches in which regional net terrestrial fluxes are inferred from observed atmospheric carbon dioxide concentrations; and e) simple light use efficiency productivity models. A number of key surface properties can be supplied by the latest quantitative remote sensing, although not all are available with the spatial and temporal coverage required: a) optical remote sensing - fractional cover of soil, non-photosynthetic vegetation, and photosynthetic vegetation; light use efficiency; biochemistry; plant functional types; ground fire area and intensity, canopy water status; 3D canopy properties; and radiation interception probabilities; b) microwave remote sensing (passive, active and interferometry) – inundation and flooding dynamics; vegetation biomass and structure; soil moisture; c) LiDAR remote sensing – tree heights, canopy structure and biomass, synergies with radar and multi-angle optical. The limitations of in situ data for calibration and validation and the need to link process and pattern dynamically through time and across scales suggest that combinations of modelling approaches and data sources will be needed to significantly reduce uncertainties. Process models may be enhanced by specific biochemical and structural remote sensing retrievals. Physical energy and water balance models may be aided by time series of temperature, soil moisture, rainfall and atmospheric data retrieved by remote sensing. Land use change modeling relies upon remotely sensed definition of land cover change and combination with ancillary data to define land use. Model-data assimilation and inverse methods require prior information on surface states and fluxes that may be available from remote sensing products. Models describing savanna carbon dynamics as one biome among many global biomes, often neglect key processes relevant to savannas, including the impacts of fire, herbivory and shifting agriculture on carbon. In modeling the global savanna biome there is a pressing need for new frameworks, schemes and hierarchies that link the diverse approaches with key processes and mechanisms that are unique to tree-grass systems and that combine energy and water balance dynamics, the physiological and population dynamics of vegetation communities, and the spatially explicit patch dynamics of human disturbance.
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0426 Biosphere/atmosphere interactions (0315)
DE: 0428 Carbon cycling (4806)
DE: 0430 Computational methods and data processing
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting