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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Apr 2022
Research article |
| 29 Apr 2022
| 29 Apr 2022
Detecting hydrological connectivity using causal inference from time series: synthetic and real karstic case studies
Damien Delforge
CORRESPONDING AUTHOR
Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Royal Observatory of Belgium, Brussels, Belgium
Olivier de Viron
Littoral, Environnement et Sociétés, Université de La Rochelle and CNRS (UMR7266), La Rochelle, France
Marnik Vanclooster
Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Michel Van Camp
Royal Observatory of Belgium, Brussels, Belgium
Arnaud Watlet
British Geological Survey, Nottingham, UK
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Cited
14 citations as recorded by crossref.
- Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al. 10.1038/s43247-024-01891-w
- Climate shapes baseflows, influencing drought severity M. Zaerpour et al. 10.1088/1748-9326/ad975a
- Nonlinear Riparian Interactions Drive Changes in Headwater Streamflow S. Newcomb & S. Godsey 10.1029/2023WR034870
- Agriculture’s impact on water–energy balance varies across climates M. Zaerpour et al. 10.1073/pnas.2410521122
- Framework for extracting multi-objective operation rules for cascade reservoirs based on causal features and physical mechanisms D. Gu et al. 10.1016/j.ejrh.2025.102522
- Detecting causal relationship of non-floodplain wetland hydrologic connectivity using convergent cross mapping S. Lee et al. 10.1038/s41598-023-44071-0
- Inferring causal associations in hydrological systems: a comparison of methods H. Liang et al. 10.1007/s00477-025-02977-3
- Assessing the Impact of Long-Term ENSO, SST, and IOD Dynamics on Extreme Hydrological Events (EHEs) in the Kelani River Basin (KRB), Sri Lanka V. Wijeratne et al. 10.3390/atmos14010079
- Trends and causal structures of rain-on-snow flooding N. Kumar et al. 10.1016/j.jhydrol.2025.133938
- Revealing joint evolutions and causal interactions in complex ecohydrological systems by a network-based framework L. Wang et al. 10.5194/hess-29-361-2025
- The Impacts of Hydrology and Climate on Hydrological Connectivity in a Complex River–Lake Floodplain System Based on High Spatiotemporal Resolution Images S. Yang et al. 10.3390/w14121836
- Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping G. Bonotto et al. 10.1029/2021WR030231
- Ecological responses to hydrological connectivity in grassland riparian zones: Insights from vegetation and ground-dwelling arthropods M. Ye et al. 10.1016/j.scitotenv.2024.171196
- An increasing mutual promotion of economic growth between China and the world observed from nighttime light remote sensing Z. Chen et al. 10.1080/17538947.2024.2426521
14 citations as recorded by crossref.
- Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al. 10.1038/s43247-024-01891-w
- Climate shapes baseflows, influencing drought severity M. Zaerpour et al. 10.1088/1748-9326/ad975a
- Nonlinear Riparian Interactions Drive Changes in Headwater Streamflow S. Newcomb & S. Godsey 10.1029/2023WR034870
- Agriculture’s impact on water–energy balance varies across climates M. Zaerpour et al. 10.1073/pnas.2410521122
- Framework for extracting multi-objective operation rules for cascade reservoirs based on causal features and physical mechanisms D. Gu et al. 10.1016/j.ejrh.2025.102522
- Detecting causal relationship of non-floodplain wetland hydrologic connectivity using convergent cross mapping S. Lee et al. 10.1038/s41598-023-44071-0
- Inferring causal associations in hydrological systems: a comparison of methods H. Liang et al. 10.1007/s00477-025-02977-3
- Assessing the Impact of Long-Term ENSO, SST, and IOD Dynamics on Extreme Hydrological Events (EHEs) in the Kelani River Basin (KRB), Sri Lanka V. Wijeratne et al. 10.3390/atmos14010079
- Trends and causal structures of rain-on-snow flooding N. Kumar et al. 10.1016/j.jhydrol.2025.133938
- Revealing joint evolutions and causal interactions in complex ecohydrological systems by a network-based framework L. Wang et al. 10.5194/hess-29-361-2025
- The Impacts of Hydrology and Climate on Hydrological Connectivity in a Complex River–Lake Floodplain System Based on High Spatiotemporal Resolution Images S. Yang et al. 10.3390/w14121836
- Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping G. Bonotto et al. 10.1029/2021WR030231
- Ecological responses to hydrological connectivity in grassland riparian zones: Insights from vegetation and ground-dwelling arthropods M. Ye et al. 10.1016/j.scitotenv.2024.171196
- An increasing mutual promotion of economic growth between China and the world observed from nighttime light remote sensing Z. Chen et al. 10.1080/17538947.2024.2426521
Latest update: 15 Aug 2025
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.
Causal inference methods (CIMs) aim at identifying causal links from temporal dependencies found...
