Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1617-2024
https://doi.org/10.5194/hess-28-1617-2024
Research article
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11 Apr 2024
Research article | Highlight paper |  | 11 Apr 2024

A network approach for multiscale catchment classification using traits

Fabio Ciulla and Charuleka Varadharajan

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This paper introduces recent methods to cluster catchments that is based on traits with an application on a very important dataset (over 9000 catchments using 274 traits). The method proposed open many research perspectives in the fields of hydrology, environmental sciences and other disciplines.
Short summary
We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.