Groundwater Level Projections for Aquifers Affected by Annual to Decadal Hydroclimate Variations: Example of Northern France

Abstract In a context where anticipating future trends and long‐term variations in water resources is crucial, improving our knowledge about most types of aquifer responses to climate variability and change is necessary. Aquifers with variability dominated by seasonal (marked annual cycle) or low‐frequency variations (interannual to decadal variations driven by large‐scale climate dynamics) may encounter different sensitivities to climate change. We investigated this hypothesis by generating groundwater level projections using deep learning models for annual, inertial (low‐frequency dominated) or mixed annual/low‐frequency aquifer types in northern France from 16 CMIP6 climate model inputs in an ensemble approach. Generated projections were then analyzed for trends and changes in variability. Generally, groundwater levels tended to decrease for all types and scenarios across 2030–2100 without any significant differences between emission scenarios. However, when comparing future projections to historical data, groundwater levels appeared slightly higher in the near future (2030–2050), with decreasing intensities in later periods. The variability of projections showed slightly increasing variability for annual types for all scenarios but decreasing variability for mixed and inertial types. As the severity of the scenario increased, more mixed and inertial‐type stations appeared to be affected by decreasing variability. Focusing on low‐frequency confirmed this observation: while a significant amount of stations showed increasing variability for the less severe SSP2‐4.5 scenario, low‐frequency variability eventually showed slight yet statistically significant decreasing trends as the severity of the scenario increased. For the most severe scenario, almost all stations were affected by decreasing low‐frequency variability.