Articles | Volume 26, issue 8
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

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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.