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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- Watershed sediment cascades across multiple timescales: Causal relationships with hydroclimate and underlying surface attributes Z. Yue et al. https://doi.org/10.1016/j.jhydrol.2025.134234
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- Spatial heterogeneity of agricultural drought drivers in irrigation district: A causal inference framework bridging covariation and structural equation modeling X. Shi et al. https://doi.org/10.1016/j.agwat.2025.109978
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- Explainable and causal machine learning to investigate the spatiotemporal dynamics patterns of coastal water quality in Hong Kong H. Zhang et al. https://doi.org/10.1016/j.watres.2025.125026
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- Unraveling drought propagation dynamics using causal inference from time series M. Pang & S. Yang https://doi.org/10.1088/1748-9326/ae151e
28 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. https://doi.org/10.1038/s43247-024-01891-w
- Climate shapes baseflows, influencing drought severity M. Zaerpour et al. https://doi.org/10.1088/1748-9326/ad975a
- Agriculture’s impact on water–energy balance varies across climates M. Zaerpour et al. https://doi.org/10.1073/pnas.2410521122
- Scour depth prediction using machine learning and explainable AI: assessment of bridge vulnerability P. Kanishkavardhan et al. https://doi.org/10.3389/fbuil.2026.1790274
- Long-term spatiotemporal assessment of wetland degradation and ecological resilience in Indian Ramsar sites spanning various agroecological zones M. Rawat et al. https://doi.org/10.1016/j.rsase.2026.101979
- Inferring causal associations in hydrological systems: a comparison of methods H. Liang et al. https://doi.org/10.1007/s00477-025-02977-3
- Trends and causal structures of rain-on-snow flooding N. Kumar et al. https://doi.org/10.1016/j.jhydrol.2025.133938
- When Geoscience Meets Foundation Models: Toward a general geoscience artificial intelligence system H. Zhang et al. https://doi.org/10.1109/MGRS.2024.3496478
- DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment H. Wang et al. https://doi.org/10.5194/hess-30-1543-2026
- 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. https://doi.org/10.3390/w14121836
- Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping G. Bonotto et al. https://doi.org/10.1029/2021WR030231
- Can causal discovery lead to a more robust prediction model for runoff signatures? H. Abbasizadeh et al. https://doi.org/10.5194/hess-29-4761-2025
- An increasing mutual promotion of economic growth between China and the world observed from nighttime light remote sensing Z. Chen et al. https://doi.org/10.1080/17538947.2024.2426521
- Climate change and human activities amplify runoff variability risks in lower reaches of large rivers J. Gao et al. https://doi.org/10.1038/s43247-025-02759-3
- Watershed sediment cascades across multiple timescales: Causal relationships with hydroclimate and underlying surface attributes Z. Yue et al. https://doi.org/10.1016/j.jhydrol.2025.134234
- From Monitoring Data to Management Decisions: Causal Network Analysis of Water Quality Dynamics Using CEcBaN S. Hilau et al. https://doi.org/10.3390/w18060764
- Nonlinear Riparian Interactions Drive Changes in Headwater Streamflow S. Newcomb & S. Godsey https://doi.org/10.1029/2023WR034870
- Framework for extracting multi-objective operation rules for cascade reservoirs based on causal features and physical mechanisms D. Gu et al. https://doi.org/10.1016/j.ejrh.2025.102522
- A causal-aware artificial intelligence framework for mineral prospectivity mapping Z. Zhang et al. https://doi.org/10.1016/j.gsf.2026.102248
- Detecting causal relationship of non-floodplain wetland hydrologic connectivity using convergent cross mapping S. Lee et al. https://doi.org/10.1038/s41598-023-44071-0
- Spatial heterogeneity of agricultural drought drivers in irrigation district: A causal inference framework bridging covariation and structural equation modeling X. Shi et al. https://doi.org/10.1016/j.agwat.2025.109978
- 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. https://doi.org/10.3390/atmos14010079
- Revealing joint evolutions and causal interactions in complex ecohydrological systems by a network-based framework L. Wang et al. https://doi.org/10.5194/hess-29-361-2025
- Unravelling the spatiotemporal causality chain between meteorological and agricultural drought propagation in the China–Pakistan Economic Corridor M. Ismail et al. https://doi.org/10.1016/j.atmosres.2025.108532
- Explainable and causal machine learning to investigate the spatiotemporal dynamics patterns of coastal water quality in Hong Kong H. Zhang et al. https://doi.org/10.1016/j.watres.2025.125026
- Ecological responses to hydrological connectivity in grassland riparian zones: Insights from vegetation and ground-dwelling arthropods M. Ye et al. https://doi.org/10.1016/j.scitotenv.2024.171196
- From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
- Unraveling drought propagation dynamics using causal inference from time series M. Pang & S. Yang https://doi.org/10.1088/1748-9326/ae151e
Saved (final revised paper)
Latest update: 05 Jun 2026
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...
