Preprints
https://doi.org/10.5194/hess-2021-445
https://doi.org/10.5194/hess-2021-445

  06 Sep 2021

06 Sep 2021

Review status: this preprint is currently under review for the journal HESS.

Detecting hydrological connectivity using causal inference from time-series: synthetic and real karstic study cases

Damien Delforge1,2, Olivier de Viron3, Marnik Vanclooster1, Michel Van Camp2, and Arnaud Watlet4 Damien Delforge et al.
  • 1Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
  • 2Royal Observatory of Belgium, Brussels, Belgium
  • 3Littoral, Environnement et Sociétés, Université de La Rochelle and CNRS (UMR7266), La Rochelle, France
  • 4British Geological Survey, Nottingham, UK

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.

Damien Delforge et al.

Status: open (until 01 Nov 2021)

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Damien Delforge et al.

Damien Delforge et al.

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Short summary
Causal inference methods aim at detecting causal interactions between variables in a dataset. We study four of these generic methods and ask whether they can detect connected flow paths below the ground. The task is complex because connections seem ubiquitous as the variables react in the same way to meteorological variables. Our results show that, although not entirely reliable, the recent methods reduce the number of potential connections, with some being less prone to errors.