2006 Fall Meeting          
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Cite abstracts as Author(s) (2006), Title, Eos Trans. AGU,
(52), Fall Meet. Suppl., Abstract xxxxx-xx

HR: 08:15h
TI: Multivariate Geographic Clustering as a Basis for Ecoregionalization in the Environmental Sciences
AU: * Hargrove, W W
EM: hnbw@fire.esd.ornl.gov
AF: Oak Ridge National Laboratory, Environmental Sciences Division P.O. Box 2008, M.S. 6407, Oak Ridge, TN 37830-6407 United States
AU: Hoffman, F M
EM: forrest@climate.ornl.gov
AF: Oak Ridge National Laboratory, Environmental Sciences Division P.O. Box 2008, M.S. 6106, Oak Ridge, TN 37830-6106 United States
AB: Multivariate clustering based on fine spatial resolution maps of elevation, temperature, precipitation, soil characteristics, and solar inputs has been used to produce sets of quantitative ecoregion maps for the conterminous United States and the world at several levels of division. The coarse ecoregion divisions accurately capture intuitively-understood regional environmental differences, whereas the finer divisions highlight local condition gradients and ecotones. Such statistically-generated ecoregions can be produced based on user-selected continuous variables, allowing customized regions to be delineated for any specific problem. For example, 20 geographic domains having a similar climate were identified for the National Ecological Observatory Network (NEON), based on nine ecologically relevant climatic variables, including temperature, precipitation, solar radiation, and plant-available soil moisture at 1 sq km resolution. Because the ecoregion classification is quantitative, it can provide a basis for additional types of analyses. A red-green-blue visualization based on the first three Principal Component axes of ecoregion centroids indicates with color the relative combination of environmental conditions found within each ecoregion. Colors show the similarity of environmental conditions across regions. Multiple geographic areas can be classified into a single common set of quantitative ecoregions to provide a basis for comparison, or maps of a single area through time can be classified to portray climatic or environmental changes geographically in terms of current conditions. Quantified representativeness can characterize borders between ecoregions as gradual, sharp, or of changing character along their length. Similarity of any ecoregion to all other ecoregions can be quantified and displayed as a "representativeness" map. The representativeness of an existing spatial array of sample locations or study sites can be mapped relative to a set of quantitative ecoregions, suggesting locations for additional samples or sites.
UR: http://research.esd.ornl.gov/~hnw/EMecoregions.pdf
DE: 0520 Data analysis: algorithms and implementation
DE: 1632 Land cover change
DE: 1637 Regional climate change
DE: 1640 Remote sensing (1855)
DE: 4815 Ecosystems, structure, dynamics, and modeling (0439)
SC: Earth and Space Science Informatics [IN]
MN: 2006 Fall Meeting

Research partially sponsored by the 1) Climate Change Research Division (CCRD) of the Office of Biological and Environmental Research (OBER), and 2) Mathematical, Information, and Computational Sciences (MICS) Division of the Office of Advanced Scientific Computing Research (OASCR) within the U.S. Department of Energy's Office of Science (SC). This research used resources of the National Center for Computational Science (NCCS) at Oak Ridge National Laboratory (ORNL) which is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
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