Articles | Volume 28, issue 7
Research article
 | Highlight paper
11 Apr 2024
Research article | Highlight paper |  | 11 Apr 2024

A network approach for multiscale catchment classification using traits

Fabio Ciulla and Charuleka Varadharajan


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1675', Anonymous Referee #1, 05 Sep 2023
    • AC1: 'Reply on RC1', Fabio Ciulla, 26 Oct 2023
  • RC2: 'Comment on egusphere-2023-1675', Anonymous Referee #2, 07 Sep 2023
    • AC2: 'Reply on RC2', Fabio Ciulla, 26 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (17 Nov 2023) by Roger Moussa
AR by Fabio Ciulla on behalf of the Authors (26 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Jan 2024) by Roger Moussa
RR by Anonymous Referee #2 (15 Jan 2024)
RR by Anonymous Referee #1 (09 Feb 2024)
ED: Publish as is (09 Feb 2024) by Roger Moussa
AR by Fabio Ciulla on behalf of the Authors (23 Feb 2024)  Manuscript 
Executive editor
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.