Groundwater level projections for aquifers affected by annual to decadal hydroclimate variations DOI
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani

et al.

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

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 change necessary. Aquifers with dominated by seasonal (marked annual cycle) or low-frequency (interannual decadal driven large-scale dynamics) may encounter different sensitivities change. We investigated this hypothesis generating groundwater level projections using deep learning models for annual, inertial (low-frequency dominated) mixed annual/low-frequency northern France from 16 CMIP6 model inputs an ensemble approach. Generated were then analysed changes variability. Generally, levels tended decrease all scenarios across the 2030-2100. The showed slightly increasing but decreasing types. As severity scenario increased, more inertial-type stations appeared be affected Focusing on confirmed observation: while significant amount less severe SSP 2-4.5 scenario, eventually slight yet statistically as increased. For almost Finally, seemed, instances, higher than historical period, without any differences between emission scenarios.

Language: Английский

Groundwater level projections for aquifers affected by annual to decadal hydroclimate variations DOI
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani

et al.

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

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 change necessary. Aquifers with dominated by seasonal (marked annual cycle) or low-frequency (interannual decadal driven large-scale dynamics) may encounter different sensitivities change. We investigated this hypothesis generating groundwater level projections using deep learning models for annual, inertial (low-frequency dominated) mixed annual/low-frequency northern France from 16 CMIP6 model inputs an ensemble approach. Generated were then analysed changes variability. Generally, levels tended decrease all scenarios across the 2030-2100. The showed slightly increasing but decreasing types. As severity scenario increased, more inertial-type stations appeared be affected Focusing on confirmed observation: while significant amount less severe SSP 2-4.5 scenario, eventually slight yet statistically as increased. For almost Finally, seemed, instances, higher than historical period, without any differences between emission scenarios.

Language: Английский

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