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.
This paper introduces recent methods to cluster catchments that is based on traits with an...
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.
We present a new method based on network science for unsupervised classification of large...