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Potential Grazing Impact to Water Quality in the Western United States

Timothy G. Wade 1, Peter Leinenbach 2, Scott Augustine 2, Anthony R. Selle 3, Abe Calderon 4, J. Max Viger, Daniel T. Heggem 5, Karl A. Hermann 3, Robert K. Hall 6, Andrew Weiss 7, and Valentina Haack 8

1 USEPA ORD Environmental Sciences Division, Landscape Ecology Branch, RTP, NC 27711
2 USEPA Region 10, 1200 Sixth Avenue, Seattle, WA 98101
3 USEPA Region 8, 999 18th Street, Suite 300, Denver, CO 80202-2466
4 SAIC, EPA Region 10, 1200 6th Avenue, Seattle, WA 98101
5 USEPA ORD Environmental Sciences Division, Landscape Ecology Branch, Las Vegas, NV 89119
6 USEPA Region 9, WTR2, 75 Hawthorne St., San Francisco, CA 94105
7 The Nature Conservancy, 217 Pine St. Suite 1100,Seattle, WA 98101
8 Indus Corporation, EPA Region 10,1200 6th Avenue,Seattle, WA 98101

Grazing is a widespread stressor on ecosystems in the western United States. As part of the US EPA's Western Environmental Monitoring and Assessment Program (EMAP), the potential for grazing impacts to surface water quality was modeled using commonly available data in a Geographic Information System (GIS). Inputs to the model are derived from four sources: land cover from the National Land Cover Dataset (NLCD), land ownership from the National Atlas, distance-to-water from the National Hydrography Dataset (NHD; 1:100,000 scale), and topographic position from the National Elevation Dataset (NED). The model uses 30 meter grid cells for input, analysis, and output. The input grids to the model have their values scaled to a range of 0 (not possible to graze) to 10 (most likely to be grazed). The potential grazing impact model operates by multiplying each of the four input grids together to determine a value between 0 and 10,000 for each cell. The areas with the highest potential for water quality impacts due to grazing are flat, non-protected, grasslands that are within 90 meters of a water source. Maps of potential grazing impact for Oregon and the Central Valley of California, along with maps of the model input layers are included on the poster.

Keywords: grazing, grazing impacts, water quality, grazing impact model

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