HR: 1340h
AN: GC13A-0945    [Abstracts]
TI: Influence of Spatial Components of Uncertainty on the Prediction of Carbon Disturbed by Deforestation in the Brazilian Amazon
AU: * Gutierrez-Velez, V H
EM: vgutier@umd.edu
AF: Clark University Department of Geography, 950 Main St., Worcester, MA 01610,
AU: * Gutierrez-Velez, V H
EM: vgutier@umd.edu
AF: University of Maryland Department of Geography, 2181 LeFrak Hall, College Park, MD 20742,
AU: Pontius, R G
EM: rpontius@clarku.edu
AF: Clark University Department of Geography, 950 Main St., Worcester, MA 01610,
AB: The prediction of carbon emissions from tropical deforestation is critical to anticipate the magnitude and consequences of global warming. This prediction involves several sources of uncertainty that need to be identified and measured. In this work, we evaluate how spatial components of uncertainty influence the prediction of carbon disturbed by deforestation in the Brazilian Amazon forest. The specific components of uncertainty are the accuracy on the prediction of quantity and location of deforestation and the variability and uncertainty in the spatial distribution of carbon. Five maps representing the spatial distribution of carbon in the study area are used to calculate the absolute minimum, maximum and mean carbon disturbed under different simulated intensities of deforestation. These results are also compared with the estimation of carbon disturbed by deforestation in areas where actual deforestation occurred between 2000 and 2005. Results indicate that the most influential component of uncertainty is the accuracy on the prediction of quantity of deforestation followed by the uncertainty on the spatial distribution of carbon. The effect of the accuracy on the prediction of location however is trivial compared to the other two components. In order to reduce uncertainty in the prediction of carbon disturbed by deforestation in the Brazilian Amazon, efforts should be focused on improving both the prediction of the total area that will be deforested and the representation of the spatial distribution of carbon rather than on the prediction of where deforestation will occur. This analysis constitutes a basis to set priorities on reducing uncertainty associated to the prediction of carbon emissions from tropical deforestation and may contribute to the design of forest-based mechanisms to mitigate global warming.
DE: 1615 Biogeochemical cycles, processes, and modeling (0412, 0414, 0793, 4805, 4912)
DE: 1630 Impacts of global change (1225)
DE: 1631 Land/atmosphere interactions (1218, 1843, 3322)
DE: 1632 Land cover change
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting