<?xml version="1.0" encoding="UTF-8" standalone="no"?><metadata xml:lang="en">
<Esri>
<CreaDate>20180305</CreaDate>
<CreaTime>14540200</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idAbs>This project seeks to understand and map the senstivity of montane meadow and riparian to incision and downcutting. To achieve that goal we've assembled a large number of geomorphic, geologic, and hydrologic metrics for entire watersheds across the Great Basin. Only watersheds with mapped perennial water were included in this dataset. This dataset features 1479 watersheds form across the Great Basin.</idAbs>
<searchKeys>
<keyword>watershed</keyword>
<keyword>mountain</keyword>
<keyword>montane</keyword>
<keyword>Great Basin</keyword>
<keyword>hydrology</keyword>
<keyword>geomorphology</keyword>
<keyword>geology</keyword>
</searchKeys>
<idPurp>GIS database of geologic, geomorphic, and shape characteristics for 1,479 watersheds in the Great Basin with perennial water.</idPurp>
<idCredit>Dilts,T., Board, D., Knight, A., Wesely, N., Wedge, L., Lord, M., Miller, J., Carroll, R., Snyder, K., Weisberg, P., and Chambers, J. (2018) GIS database of geologic, geomorphic, and shape characteristics for 1,479 watersheds in the Great Basin with perennial water. As part of: A multi-scale resilience-based framework for restoring and conserving Great Basin wet meadows and riparian ecosystems. U.S. Forest Service, University of Nevada Reno, Desert Research Institute, and Western Carolina University. Digital spatial data.</idCredit>
<resConst>
<Consts>
<useLimit>Please cite if you use these data in a publication</useLimit>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
</Thumbnail>
</Binary>
</metadata>