Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5791-2025
https://doi.org/10.5194/hess-29-5791-2025
Research article
 | 
28 Oct 2025
Research article |  | 28 Oct 2025

Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America

Wouter J. M. Knoben, Cyril Thébault, Kasra Keshavarz, Laura Torres-Rojas, Nathaniel W. Chaney, Alain Pietroniro, and Martyn P. Clark

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-893', Anonymous Referee #1, 14 Apr 2025
    • AC1: 'Reply on RC1', Wouter Knoben, 08 May 2025
  • RC2: 'Comment on egusphere-2025-893', Anonymous Referee #2, 15 Apr 2025
    • AC2: 'Reply on RC2', Wouter Knoben, 08 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (17 May 2025) by Nunzio Romano
AR by Wouter Knoben on behalf of the Authors (09 Jun 2025)
EF by Anna Glados (01 Jul 2025)  Manuscript   Author's response   Author's tracked changes   Supplement 
ED: Referee Nomination & Report Request started (01 Jul 2025) by Nunzio Romano
RR by Brandi Gaertner (02 Jul 2025)
RR by Anonymous Referee #2 (29 Jul 2025)
ED: Publish as is (05 Aug 2025) by Nunzio Romano
AR by Wouter Knoben on behalf of the Authors (06 Aug 2025)
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Short summary
Many existing datasets for hydrologic analysis tend to treat catchments as single spatially homogeneous units focusing on daily data and typically do not support more complex models. This paper introduces a dataset that goes beyond this set-up by (1) providing data at a higher spatial and temporal resolution, (2) specifically considering the data requirements of all common hydrologic model types, and (3) using statistical summaries of the data aimed at quantifying spatial and temporal heterogeneity.
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