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Activity Number:
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287
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
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Sponsor:
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Environmental and Ecological Statistics
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| Abstract - #300129 |
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Title:
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Mid-Atlantic Watersheds Classification and Prioritization for Protection and Restoration
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Author(s):
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Wayne Myers*+ and Mary McKenney-Easterling and Kristen Hychka and Bronson Griscom and Joseph Bishop and Gian Rocco and Robert Brooks and George Constantz and Ganapati P. Patil and Charles Taillie
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Companies:
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Pennsylvania State University and Pennsylvania State University and Pennsylvania State University and Canaan Valley Institute and Pennsylvania State University and Pennsylvania State University and Pennsylvania State University and Canaan Valley Institute and Pennsylvania State University and Pennsylvania State University
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Address:
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124 Land & Water Research Building, University Park, PA, 16802,
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Keywords:
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watersheds ; classification ; clustering ; geographic information systems ; environment
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Abstract:
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The purpose is to characterize watersheds in a region regarding vulnerability and degradation of water quality due to human impact based on available spatial information. Available information is of five general types: (1) physical and physiographic conformation, (2) soil factors, (3) climatic factors, (4) land-cover/land-use, and (5) prior records of sampling at selected locations for water quality and biological indicators. The strategy is to develop cluster-based classes of watersheds that are expected to have similar responses to anthropogenic stressors, without using metrics that are directly influenced by local human activity. Watersheds in these classes are then analyzed for degree of human influence as indicated by land-cover/land-use and demographics. More sparse data on water quality and biological indicators at stream sampling locations provides a basis for determining the degradation response to human-induced stressors in each class along with potential for remediation. Statistical innovation takes the form of clustering by stages and coupling the clustering process with visualization and expert interaction through the medium of geographic information systems (GIS).
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