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

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.

Automated summary

This study investigates the potential of convergent cross-mapping (CCM) to identify causal interactions between streamflow and groundwater head. The results from case studies in three different upland catchments in Victoria, Australia, suggest that CCM can provide valuable insights about hydrological processes. However, limitations around seasonality, data sampling frequency, and long-term trends should be considered when interpreting the causal links suggested by CCM.