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| Created: | Mar 11, 2026 at 6:28 p.m. (UTC) | |
| Last updated: | Mar 11, 2026 at 6:50 p.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Public |
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Abstract
This resource contains the code for Seybold et al. (currently in review at Geophysical Research Letters).
Step 1 is the geospatial analysis and extraction of watershed attributes for 82,213 headwater watersheds across the continental United States.
Step 2 takes the extracted data from Step 1 and performs a k-means clustering of headwater watershed attributes. The final results of this clustering (linking HUC12 IDs and cluster assignments) is published as a standalone HydroShare resource and can be found here: Seybold, E. (2025). Headwater HUC12 IDs and associated Clusters, HydroShare, http://www.hydroshare.org/resource/4ba6663de66a42be81422d39ae349b66.
Step 3 takes the transformed headwater and downstream attributes generated in Step 1 (and used in Step 2) to train a random forest classification model that predicts headwater vs. downstream class membership.
Subject Keywords
Coverage
Spatial
Content
Related Resources
| This resource has a related resource in another format | Seybold, E. (2025). Headwater HUC12 IDs and associated Clusters, HydroShare, http://www.hydroshare.org/resource/4ba6663de66a42be81422d39ae349b66 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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