Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping

• Published in: Water resources research Vol. 58; no. 8
• Main Authors: Bonotto, Giancarlo, Peterson, Tim J., Fowler, Keirnan, Western, Andrew W.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01-08-2022
• Subjects: Aquifers; Catchment area; Catchments; Causality; causality analysis; Climatic conditions; Computer models; Convergence; convergent cross‐mapping; Data sampling; Drought; Dynamical systems; Ecological effects; Ecology; Environmental monitoring; Financial resources; Groundwater; Groundwater data; Groundwater levels; groundwater/surface water interactions; Hydrogeology; Hydrologic models; Hydrologic processes; hydrological processes; Hydrology; Identification methods; Links; Mapping; Mathematical models; Methods; Nonlinear dynamics; Nonlinear systems; Rivers; Seasonal variations; Seasonality; Stream discharge; Stream flow; Tidal bores; Variability; Water management; Australia; Victoria Australia
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2021WR030231

Groundwater (GW) is commonly conceptualized as causally linked to streamflow (SF). However, confirming where and how it occurs is challenging given the expense of experimental field monitoring. Therefore, hydrological modeling and water management often rely on expert knowledge to draw causality between SF and GW. This paper investigates the potential of convergent cross‐mapping (CCM) to identify causal interactions between SF and GW head. Widely used in ecology, CCM is a nonparametric method to identify causality in nonlinear dynamic systems. To apply CCM between variables the only required inputs are time‐series data (stream gauge and bore), so it may be an attractive alternative or complement to expensive field‐based studies of causality. Three upland catchments across different hydrogeologic settings and climatic conditions in Victoria, Australia, are adopted as case studies. The outputs of the method seem to largely agree with a priori perceptual understanding of the study areas and offered additional insights about hydrological processes. For instance, it suggested weaker SF‐GW interactions during and after the Millennium Drought than in the previous wet periods. However, we show that CCM limitations around seasonality, data sampling frequency, and long‐term trends could impact the variability and significance of causal links. Hence, care must be taken while physically interpreting the causal links suggested by CCM. Overall, this study shows that CCM can provide valuable causal information from common hydrological time‐series, which is relevant to a wide range of applications, but it should be used and interpreted with care and future research is needed.
Plain Language Summary
It is common in hydrology to assume that the water below the ground (in aquifers) interacts with the rivers. However, it is challenging to identify where and how it occurs with the available data and financial resources. Therefore, we often use computer models that represent these interactions according to the knowledge of experts. This paper investigates the potential of convergent cross‐mapping (CCM), a method that is widely used in ecology, to identify these interactions. This method only requires time‐series data (e.g., streamflow and groundwater level data) and represents an attractive alternative or complement to expensive field‐based studies. The results of the analysis appear to agree with an initial understanding of the aquifer‐river interactions in three study areas in Victoria, Australia, and offered additional insights. For instance, it suggests weaker interactions during and after the Millennium Drought than in the previous wet periods. However, variability and significance of the causal links inferred by CCM can be due to some limitations of the method. Hence, care must be taken while physically interpreting the results. Overall, CCM can unlock valuable information from common data, which is relevant to hydrology, but it should be used and interpreted with care and future research is needed.
Key Points
Convergent cross‐mapping (CCM) is a nonparametric method to identify causality in nonlinear dynamic systems that is widely used in ecology
CCM results suggest heterogeneous causal links that are largely consistent with perceptual understanding of processes
CCM seems widely applicable in hydrology, but also has limitations and thus should be used and interpreted with care