HR: 1340h
AN: B53B-1186    [Abstracts]
TI: A Landscape Investigation of Carbon Dioxide Flux Drivers Using a Geostatistical Regression at Various Temporal Scales
AU: * Mueller, K
EM: kimlm@umich.edu
AF: The University of Michigan, Department of Civil & Environmental Engineering, 2350 Hayward, Ann Arbor, MI 48109-2125, United States
AU: Curtis, P
EM: curtis.7@osu.edu
AF: The Ohio State University, Department of Evolution, Ecology, and Organismal Biology, 318 W. 12th Avenue, Columbus, OH 43210-1293, United States
AU: Michalak, A M
EM: amichala@umich.edu
AF: The University of Michigan, Department of Civil & Environmental Engineering, 2350 Hayward, Ann Arbor, MI 48109-2125, United States
AU: Michalak, A M
EM: amichala@umich.edu
AF: The University of Michigan, Department of Atmopsheric, Oceanic, and Space Sciences, 2455 Hayward St., Ann Arbor, MI 48109-2143, United States
AB: Methods used to quantify regional sources and sinks of atmospheric carbon dioxide (CO₂) typically involve the modeling of biophysical and ecological processes using relationships derived from mechanistic studies performed at smaller spatial scales. However, estimates from biospheric models coupled with atmospheric transport models have difficulty reproducing measured atmospheric CO₂ concentrations. Although there may be several reasons for these inconsistencies, they point towards a need to study the relationships between critical biophysical and ecological processes and CO₂ flux at the spatio-temporal resolution of biospheric models. In addition, the most influential flux drivers identified at diurnal scales may differ from those at larger timeframes. As such, this research aims to investigate parameters driving landscape scale CO₂ flux at various temporal scales. The study focuses on a mixed northern hardwood forest site (~1km²) at the University of Michigan Biological Station (UMBS), where eddy-covariance net ecosystem exchange (NEE) measurements from an AmeriFlux tower have been collected since 1999 together with other site-specific environmental datasets such as air temperature, friction velocity, and leaf area index (LAI). This study employs a geostatistical regression at monthly, daily and hourly time scales which, unlike classical statistical techniques, has the ability to account for temporal correlation in the NEE residuals. The regression analysis characterizes the relationships between NEE and the available environmental datasets by estimating regression coefficients which provide insight into the process-based mechanisms controlling biospheric CO₂ flux variability at the three examined temporal scales. Preliminary results show that at the monthly scale, LAI and the fraction of canopy intercepted photosynthetically active radiation (fPAR) have the strongest associations with monthly averaged NEE, with LAI being associated with a sink and fPAR being associated with a source of atmospheric CO₂. These findings at the landscape scale are consistent with previous results from a global geostatistical inversion study [Gourdji et al, in prep] that suggest that, at a monthly temporal resolution, LAI is better able to explain the strong seasonality expected for photosynthesis while the weaker seasonal cycle of fPAR captures variability expected for total ecosystem respiration. At the daily time resolution, during the early growing season, LAI and fPAR show similar contributions as those at the monthly resolution. Forest biophysical theory supported by mechanistic studies argues that fPAR is a better proxy for photosynthesis than LAI, which as a partial measure of above ground biomass, is more indicative of autotrophic and heterotrophic respiration. However, results from this study and the global work suggest that representations of these vegetative parameters may have a different spatio-temporal correlation to flux at aggregated scales than those inferred at smaller resolutions. Overall, results indicate that it is the combination of the LAI and fPAR datasets that is able captures the largest part of the photosynthesis and respiration signal at the monthly and daily resolutions during the growing season.
DE: 0428 Carbon cycling (4806)
DE: 0430 Computational methods and data processing
DE: 0438 Diel, seasonal, and annual cycles (4227)
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting