Drinking water availability from scattered crystalline aquifers: Climatic risks evaluated from production data in France

Brittany, France. Climate change and population growth are increasing pressure on exploited drinking water resources networks. Even in temperate climates, this pressure can become significant, particularly in areas where groundwater resources and storage capacities are limited, such as regions underlain by crystalline rocks. The challenge is further compounded in contexts involving small, scattered, and interconnected production units. Assessing failure risks and supply vulnerability is often challenging due to limited monitoring, insufficient data, and poor coordination among the many stakeholders involved. To address this gap, we developed a framework to quantify the vulnerability of drinking water resources by exploitation type (shallow wells, deep boreholes, surface water) using production data in contexts where resource monitoring is minimal or absent. We demonstrate that variations in water production can serve as a proxy for evaluating the state of exploited resources in contexts where direct resource monitoring is lacking — a common limitation for small-scale water resource assessments. The framework allows for the identification of compensation mechanisms between surface and groundwater resources and highlights the risks of shortages related to climatic variations. The study highlights the vulnerability of the area and provides insights for improving the security of drinking water supplies. It underscores the importance of considering productivity variations at monthly time steps, rather than limiting projections to an annual basis. • Framework to assess risks to drinking water availability from water production data. • Unveiling of the vulnerability of drinking water supply in a context of data-scarce and scattered resources. • Long-term relations between climatic variations and water production in low-storage aquifers. • Quantification of climatic impact on water resources productivity from production data.