Channel types predictions for the Sacramento River basin

Hervé Guillon, Colin F. Byrne, Belize Arela Albin Lane, Samuel Sandoval Solis & Gregory Brian Pasternack
Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, we leveraged machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data.

Registration Year

  • 2020
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Resource Types

  • Dataset
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Affiliations

  • Utah State University
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  • University of California, Davis
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