Articles | Volume 30, issue 11
https://doi.org/10.5194/hess-30-3455-2026
https://doi.org/10.5194/hess-30-3455-2026
Research article
 | 
05 Jun 2026
Research article |  | 05 Jun 2026

Cause-effect discovery in hydrometeorological systems: evaluation of causal discovery methods

Vivek Kumar Yadav, Murray C. Peel, Keirnan Fowler, Dongryeol Ryu, and Bramha Dutt Vishwakarma

Viewed

Total article views: 3,866 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,356 1,317 193 3,866 114 126
  • HTML: 2,356
  • PDF: 1,317
  • XML: 193
  • Total: 3,866
  • BibTeX: 114
  • EndNote: 126
Views and downloads (calculated since 05 Nov 2025)
Cumulative views and downloads (calculated since 05 Nov 2025)

Viewed (geographical distribution)

Total article views: 3,866 (including HTML, PDF, and XML) Thereof 3,860 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Jun 2026
Download
Short summary
Identifying drivers is crucial for process understanding and predictions. In Hydrometeorological systems, many variables are closely related, and common methods often rely on correlation. We describe theoretically distinct methods of discovering cause-effect relations from data. We evaluate them in a large simulated environment. Results show that finding cause-effect relations provides a parsimonious picture and to obtain robust predictions, especially under changing environmental conditions.
Share