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The Stream-Catchment (StreamCat) Dataset
Citation:
Weber, M., R. Hill, S. Leibowitz, AND D. Thornbrugh. The Stream-Catchment (StreamCat) Dataset. AWRA Summer Specialty Conference: GIS and Water Resources IX, Sacramento, CA, July 11 - 13, 2016.
Impact/Purpose:
Stream environments reflect, in part, the hydrologic integration of upstream landscapes. Characterizing upstream features is critical for effectively understanding, managing, and conserving riverine ecosystems but represents a major challenge. We developed and will present our database of >160 landscape metrics for ~2.7 million watersheds within the conterminous USA: The Stream-Catchment (StreamCat) Dataset. This dataset can be linked to an existing geospatial framework to provide rapid extraction of upstream characteristics for stream-based studies. This presentation will examine how the StreamCat dataset can be combined and used with other frameworks and applications such as the National Stream Internet (NSI). The presentation should provide useful information to users of the StreamCat dataset and facilitate interoperability of StreamCat with other frameworks and applications. It contributes to an FY16 deliverable under SSWR 3.01B
Description:
Stream environments reflect, in part, the hydrologic integration of upstream landscapes. Characterizing upstream landscape features is critical for effectively understanding, managing, and conserving riverine ecosystems. However, watershed delineation is a major challenge if hundreds to thousands of watersheds must be delineated or if a study spans a large geographic extent. Further, site-specific watershed delineations do not provide a framework for easily and quickly applying analytical results to new, un-sampled locations. We developed a database of >160 landscape metrics for ~2.7 million watersheds within the conterminous USA: The Stream-Catchment (StreamCat) Dataset. The framework uses topological flow information contained within the National Hydrography Dataset Plus v.2 to summarize upstream characteristics for each stream segment. StreamCat’s construction within and link to the NHDPlusV2 allows for rapid extraction of upstream landscape metrics for stream-based studies. These landscape metrics include both natural (e.g., climate, soils, geology) and anthropogenic (e.g., dams, agriculture, urbanization) factors.. Locations of field samples can be integrated with the framework of upstream landscape metrics to derive site-based watershed metrics . Variation in landscape features or model results can be presented visually and provide a unique tool for assessing analyses. We provide examples of how StreamCat can be used with NHDPlusV2 to visualize variation in key landscape indicators of water quality, such as urbanization, and provide input for a national model of biological stream condition as well as developing a national index of watershed integrity. Further, we look at how landscape metrics from StreamCat can be incorporated with frameworks such as the National Stream Internet and other applications such as regional SSN modeling. . In addition to streams, we show how we are applying the process to derive a similar framework for lakes.