HR: 1340h
AN: B53B-1181    [Abstracts]
TI: Empirically Modeling Carbon Fluxes over the Northern Great Plains Grasslands
AU: * Zhang, L
EM: lizhang@usgs.gov
AF: SAIC, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), 47914 252nd Street, Sioux Falls, SD 57198,
AU: Wylie, B k
EM: wylie@usgs.gov
AF: SAIC, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), 47914 252nd Street, Sioux Falls, SD 57198,
AU: Ji, L
EM: lji@usgs.gov
AF: SAIC, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), 47914 252nd Street, Sioux Falls, SD 57198,
AU: Gilmanov, T
EM: tagir.gilmanov@sdstate.edu
AF: South Dakota State University, South Dakota State University, Brookings, SD 57007,
AU: Tieszen, L L
EM: tieszen@usgs.gov
AF: USGS/EROS, 47914 252nd Street, Sioux Falls, SD 57198,
AB: Grasslands cover nearly one-fifth of the global terrestrial surface and store most of their carbon below ground. The grassland ecosystem in the Great Plains occupies over 1.5 million km² of land area and is the primary resource for livestock production in North America. However, the contributions of grasslands to local and regional carbon budgets remain uncertain due to the lack of carbon flux data for the expansive grassland ecosystems under various managements, land uses, and climate variability. A quantitative understanding of carbon fluxes across these systems is essential for developing regional, national, and global carbon budgets and providing guidance to policy makers and managers when substantial conversion to biofuels are implemented. Additionally, these estimates will provide insights into how the grassland ecosystem will respond to future climate and what systems are sustainable and offer net carbon sinks. This knowledge base and decisions support tools are needed for developing land management strategies for the region under a variety of environmental conditions and land use options. In the past, we used a remote sensing-based piecewise regression (PWR) model to estimate the grassland carbon fluxes in the northern Great Plains using the 1-km SPOT VEGETATION normalized difference vegetation index (NDVI) data. We estimated the carbon fluxes through integrated spatial databases and remotely sensed extrapolations of flux tower data to regional scales. The PWR model was applied to derive an empirical relationship between environmental variables and tower-based measurements. The PWR equations were then applied through time and space to estimate carbon fluxes across the study area at 1-km resolution. We now improve this modeling approach by 1) using Moderate Resolution Imaging Spectroradiometer (MODIS) data with higher temporal, spatial, and spectral resolutions (8-day, 500-m, and 7-band) as input; 2) incorporating the actual vegetation evapotranspiration data derived from the VegET model, which takes into account soil moisture and land surface phenology; 3) adding an additional flux tower from Brookings, SD, and additional years at other flux towers to the training data sets; and 4) considering the lag response of vegetation production to precipitation. We modeled and mapped 8-day and 500-m carbon fluxes for the years 2000–2006 in the northern Great Plains grasslands. These maps were then used to assess the regional and temporal trends of carbon fluxes in this region, identify carbon sink and source areas, and determine important transitions and environmental drivers of carbon sinks/sources. Cross-validation at sites showed that the improved model increases the estimation accuracies and reflects the variations in water stress that may not be monitored by vegetation indices alone because of the lag-response of vegetation indices to water deficits.
DE: 0428 Carbon cycling (4806)
DE: 4806 Carbon cycling (0428)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting