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- Title
Detecting hydrological connectivity using causal inference from time-series: synthetic and real karstic study cases.
- Authors
Delforge, Damien; de Viron, Olivier; Vanclooster, Marnik; Van Camp, Michel; Watlet, Arnaud
- Abstract
We investigate the potential of causal inference methods (CIMs) to reveal hydrological connections from time-series. Four CIMs are selected from two criteria, linear or nonlinear, and bivariate or multivariate. A priori, multivariate and nonlinear CIMs are best suited for revealing hydrological connections because they suit nonlinear processes and deal with confounding factors such as rainfall, evapotranspiration, or seasonality. The four methods are applied to a synthetic case and a real karstic study case. The synthetic experiment indicates that, unlike the other methods, the multivariate nonlinear framework has a low false-positive rate and allows for ruling out a connection between two disconnected reservoirs forced with similar effective precipitation. However, the multivariate nonlinear method appears unstable when it comes to real cases, making the overall meaning of the causal links uncertain. Nevertheless, all CIMs bring valuable insights into the system's dynamics, making them a cost-effective and recommendable tool for exploring data. Still, causal inference remains attached to subjective choices and operational constraints while building the dataset or constraining the analysis. As a result, the robustness of the conclusions that the CIMs can draw deserves to be questioned, especially with real and imperfect data. Therefore, alongside research perspectives, we encourage a flexible, informed, and limit-aware use of CIMs, without omitting any other approach that aims at the causal understanding of a system.
- Subjects
CAUSAL inference; SYSTEM dynamics; EVAPOTRANSPIRATION
- Publication
Hydrology & Earth System Sciences Discussions, 2021, p1
- ISSN
1812-2108
- Publication type
Article
- DOI
10.5194/hess-2021-445