Modeling Streams and Hydrogeomorphic Attributes in Oregon From Digital and Field Data 1

Managers, regulators, and researchers of aquatic ecosystems are increasingly pressed to con- sider large areas. However, accurate stream maps with geo-referenced attributes are uncommon over relevant spatial extents. Field inventories provide high-quality data, particularly for habitat characteristics at fine spatial resolutions (e.g., large wood), but are costly and so cover relatively small areas. Recent avail- ability of regional digital data and Geographic Information Systems software has advanced capabilities to delineate stream networks and estimate coarse-resolution hydrogeomorphic attributes (e.g., gradient). A spa- tially comprehensive coverage results, but types of modeled outputs may be limited and their accuracy is typically unknown. Capitalizing on strengths in both field and regional digital data, we modeled a syn- thetic stream network and a variety of hydrogeomorphic attributes for the Oregon Coastal Province. The synthetic network, encompassing 96,000 km of stream, was derived from digital elevation data. We used high-resolution but spatially restricted data from field inventories and streamflow gauges to evaluate, cali- brate, and interpret hydrogeomorphic attributes modeled from digital elevation and precipitation data. The attributes we chose to model (drainage area, mean annual precipitation, mean annual flow, probability of perennial flow, channel gradient, active-channel width and depth, valley-floor width, valley-width index, and valley constraint) have demonstrated value for stream research and management. For most of these attributes, field-measured, and modeled values were highly correlated, yielding confidence in the mod- eled outputs. The modeled stream network and attributes have been used for a variety of purposes, includ- ing mapping riparian areas, identifying headwater streams likely to transport debris flows, and characterizing the potential of streams to provide high-quality habitat for salmonids. Our framework and models can be adapted and applied to areas where the necessary field and digital data exist or can be obtained.

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