Articles | Volume 26, issue 8
https://doi.org/10.5194/hess-26-2181-2022
https://doi.org/10.5194/hess-26-2181-2022
Research article
 | 
29 Apr 2022
Research article |  | 29 Apr 2022

Detecting hydrological connectivity using causal inference from time series: synthetic and real karstic case studies

Damien Delforge, Olivier de Viron, Marnik Vanclooster, Michel Van Camp, and Arnaud Watlet

Viewed

Total article views: 3,601 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,571 934 96 3,601 384 56 60
  • HTML: 2,571
  • PDF: 934
  • XML: 96
  • Total: 3,601
  • Supplement: 384
  • BibTeX: 56
  • EndNote: 60
Views and downloads (calculated since 06 Sep 2021)
Cumulative views and downloads (calculated since 06 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,601 (including HTML, PDF, and XML) Thereof 3,384 with geography defined and 217 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Jan 2025
Download
Short summary
Causal inference methods (CIMs) aim at identifying causal links from temporal dependencies found in time-series data. Using both synthetic data and real-time series from a karst system, we study and discuss the potential of four CIMs to reveal hydrological connections between variables in hydrological systems. Despite the ever-present risk of spurious hydrological connections, our results highlight that the nonlinear and multivariate CIM has a substantially lower false-positive rate.