Exploring Global Water Scarcity Dynamics Through Causal Discovery and Structural Causal Modelling

Water scarcity represents a critical global challenge driven by diverse complex interactions between natural and anthropogenic factors. Long-term water scarcity often results in depletion of water resources in so-called water scarcity hotspots. To understand the interactions among social, ecological, and hydrological components within these water scarcity hotspots, we applied causal discovery to observational time series of socio-economic, meteorological, and ecological variables. This resulted in a network representing the causal relations between these variables and Terrestrial Water Storage (TWS). Recognizing the limitations of causal discovery, we supplemented the network with expert knowledge. From this we derived Structural Causal Models (SCMs) that simulate the causal mechanisms influencing TWS trends at the water scarcity hotspots. The resulting SCMs have a variable performance with a median $r^2$ of 0.67 compared to TWS observations. The SCMs allowed us to estimate the impact of anthropogenic and natural changes on TWS variability at water scarcity hotspots. Our analysis confirms population growth as the most significant cause of TWS change in hotspots. As such, this study demonstrates how causal discovery and SCMs can enhance modelling of human-water system dynamics affected by water scarcity, improving the understanding of these systems and potential impacts of future changes on water storage and availability. For future research, more detailed data on human-water use is needed to improve the robustness of these models. This is essential for developing effective water management strategies to mitigate water scarcity at hotspots.