HR: 0800h
AN: B21B-1031    [Abstracts]
TI: Estimation of Diurnal to Seasonal Ecosystem Parameters Using an Ensemble Kalman Filter
AU: chen, m
EM: mchen@usgs.gov
AF: U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), 47914 252nd street, sioux falls, SD 57198 United States
AB: An important part of the creating complex numerical ecosystem models is to determine parameter values. Parameter estimation is typically carried out with non-sequential strategies such as least-squares fitting. However, a potential advantage of sequential methods such as ensemble Kalman Filter (EnKF) is that parameter values can drift through time in response to observations. This research explores how to use an EnKF to generate posterior distributions and seasonality of the model parameter values of a simple carbon cycle model using observed fluxes of carbon (C), weather, hydrology, energy, and remote sensing data at three forest sites: Howland (Maine, USA), Boreas (Alberta, Canada) and Niwot Ridge Forest (Colorado, USA). The analyses demonstrate that the model parameters, such as light use efficiency, respiration rates, minimum and optimum temperature and so on, are most highly constrained by eddy flux data at daily to seasonal time scales. Light use efficiency of the ecosystems demonstrates a strong seasonality and better constrained by C fluxes in the growing season. Results show that, via data assimilation and simultaneous estimation of parameter values, the prediction of GPP, respiration and NEE improved significantly compared with those predicted by the original model without data assimilation. However, a significant portion of the variances cannot be explained by the model (in forecasting mode) probably due to the simplicity of the model structure and errors in daily flux data (caused by measurements and estimation). Nevertheless, EnKF can be very useful in evaluating and developing ecosystem models, improving understanding and quantification of the C cycle parameters and processes, and aiding selection of eddy flux tower sites and measurement frequency.
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0430 Computational methods and data processing
DE: 0434 Data sets
DE: 0439 Ecosystems, structure and dynamics (4815)
SC: Biogeosciences [B]
MN: Fall Meeting 2005