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

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

doi:https://doi.org/10.5194/hess-26-2181-2022

Articles | Volume 26, issue 8

https://doi.org/10.5194/hess-26-2181-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-2181-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Supplement

https://doi.org/10.5194/hess-26-2181-2022-supplement

Data sets

“Data for: Time-series clustering approaches for subsurface zonation and hydrofacies detection using a real timelapse electrical resistivity dataset” D. Delforge, A. Watlet, O. Kaufmann, M. Van Camp, and M. Vanclooster https://doi.org/10.17632/zh5b88vn78.2

Time-series clustering approaches for subsurface zonation and hydrofacies detection using a real time-lapse electrical resistivity dataset D. Delforge, A. Watlet, O. Kaufmann, M. Van Camp, and M. Vanclooster https://doi.org/10.1016/j.jappgeo.2020.104203

Data and results for manuscript “Imaging groundwater infiltration dynamics in karst vadose zone with long-term ERT monitoring” A. Watlet, O. Kaufmann, A. Triantafyllou, A. Poulain, J. E. Chambers, P. I. Meldrum, P. B. Wilkinson, V. Hallet, Y. Quinif, M. Van Ruymbeke, and M. Van Camp https://doi.org/10.5281/zenodo.1158631

Imaging groundwater infiltration dynamics in the karst vadose zone with longterm ERT monitoring A. Watlet, O. Kaufmann, A. Triantafyllou, A. Poulain, J. E. Chambers, P. I. Meldrum, P. B. Wilkinson, V. Hallet, Y. Quinif, M. Van Ruymbeke, and M. Van Camp https://doi.org/10.5194/hess-22-1563-2018

A Parsimonious Empirical Approach to Streamflow Recession Analysis and Forecasting D. Delforge, R. Muñoz-Carpena, M. Van Camp, and M. Vanclooster https://doi.org/10.1029/2019WR025771

Agrometeorological data for the Jemelle station CRA-W https://agromet.be/

Model code and software

Detecting and quantifying causal associations in large nonlinear time series datasets (https://jakobrunge.github.io/tigramite/) J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic https://doi.org/10.1126/sciadv.aau4996

SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R, Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors https://doi.org/10.1038/s41592-019-0686-2

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.