Groundwater level projections for aquifers affected by annual to decadal
hydroclimate variations
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 analysed for trends
and changes in variability. Generally, groundwater levels tended to
decrease for all types and scenarios across the 2030-2100. 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 SSP 2-4.5 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.
Finally, groundwater levels seemed, in most instances, slightly higher
in the future than in the historical period, without any significant
differences between emission scenarios.