HR: 09:45h
AN: B21B-08 INVITED    [Abstracts]
TI: Multi-Sensor Model-Data Assimilation for Improved Modeling of Savanna Carbon and Water Budgets
AU: * Barrett, D J
EM: Damian.Barrett@csiro.au
AF: CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia
AU: Renzullo, L J
EM: Luigi.Renzullo@csiro.au
AF: CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia
AU: Guerschman, J
EM: Juan.Guerschman@csiro.au
AF: CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia
AU: Hill, M J
EM: hillmj@aero.und.edu
AF: University of North Dakota, Clifford Hall, Stop 9011, Grand Forks, ND 58202, United States
AB: Model-data assimilation methods are increasingly being used to improve model predictions of carbon pools and fluxes, soil profile moisture contents, and evapotranspiration at catchment to regional scales. In this talk, I will discuss the development of model-data assimilation methods for application to parameter and state estimation problems in the context of savanna carbon and water cycles. A particular focus of this talk will be on the integration of in situ datasets and multiple types of satellite observations with radiative transfer, surface energy balance, and carbon budget models. An example will be drawn from existing work demonstrating regional estimation of soil profile moisture content based on multiple satellite sensors. The data assimilation scheme comprised a forward model, observation operators, multiple observation datasets and an optimization scheme. The forward model propagates model state variables in time based on climate forcing, initial conditions and model parameters and includes processes governing evapotranspiration, water budget and carbon cycle processes. The observation operators calculate modeled land surface temperature and microwave brightness temperatures based on the state variables of profile soil moisture and soil surface layer soil moisture at less than 2.5 cm depth. Satellite observations used in the assimilation scheme are surface brightness temperatures from AMSR-E (passive microwave at 6.9GHz at horizontal polarization) and from AVHRR (thermal channels 4 & 5 from NOAA-18), and land surface reflectances from MODIS Terra (channels 1 and 2 at 250m resolution). These three satellite sensors overpass at approximately the same time of day and provide independent observations of the land surface at different wavelengths. The observed brightness temperatures are used as constraints on the coupled energy balance/microwave radiative transfer model, and a canopy optical model was inverted to retrieve leaf area indices from observed reflectances in optical wavebands. Results show that the multiple constraints approach is effective in identifying and reducing the influence of bias on the resultant analysis that occurs when only single observation data sets are used. Reductions in error and bias lead to improved prognoses of soil profile water store and forecasts of rainfall runoff. The development and routine application of model-data assimilation methods in savanna biophysical modeling will improve performance of ecosystem biophysical models, assist with the design of filed campaigns to maximize uncertainty reduction, fill gaps in knowledge of the carbon and water dynamics of savannas and provide better information on which to base decision making to solve natural resource management problems in this biome.
DE: 0416 Biogeophysics
DE: 0428 Carbon cycling (4806)
DE: 0466 Modeling
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting