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Empirical modeling of carbon fluxes in the northern Great Plains grasslands

Li Zhang, SAIC, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), lzhang@usgs.gov (Presenting)
Bruce Wylie, ARTS, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), wylie@usgs.gov
Lei Ji, ARTS, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), lji@usgs.gov
Tagir Gilmanov, South Dakota State University, tagir.gilmanov@sdstate.edu
Larry Tieszen, U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), tieszen@usgs.gov

The grassland ecosystem in the Great Plains is the primary resource for livestock production in North America. The contributions of grasslands to local and regional carbon budgets remain uncertain, largely due to the lack of carbon flux data for grassland ecosystem. The quantitative estimates of carbon fluxes across the grasslands are essential for developing regional, national, and global carbon budgets, and providing insight into how the grassland ecosystem will respond to future climate. A remote sensing-based piecewise regression model has been developed to estimate the grassland and shrubland carbon fluxes in this region. This approach estimated the carbon fluxes through integrated spatial databases and remotely sensed extrapolations of in situ flux tower data to regional ecosystem distributions. In our recent study, this modeling approach has been improved with: 1) used MODIS data as input, 2) incorporated the actual vegetation evapotranspiration data which works as the surrogate of soil moisture, and 3) added one additional flux tower data in the training data sets. Based on the improved modeling method, we mapped 8-day and 500-m carbon fluxes for years 2000-2006 in this region, which could help to monitor and assess the regional and temporal trends of carbon fluxes, identify carbon sink and source areas, and determine important transitions and environmental drivers of carbon sink/source. Cross-validation at sites showed that the improved modeling approach have increased the estimation accuracies and can reflect the variations in water stress that may not be monitored by vegetation indices because of the lag response to water deficits.

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