Climate change alters post-surge recovery of coastal aquifers
Bibliographic record
Abstract
Climate change is projected to increase storm-surge intensity while reducing frequency, posing complex challenges for the recovery of coastal aquifers subject to recurrent wave overwash events. This study quantifies the combined effects of these opposing trends using surface-subsurface integrated numerical simulations across 12 scenarios with varying storm-surge frequency and intensity. Here, we show that two distinct long-term regimes emerge: (i) full recovery, where the aquifer returns to pre-surge conditions if storm intensity and frequency remain below critical thresholds, and (ii) shifted equilibrium, characterized by persistent salt accumulation and depleted fresh groundwater availability if these thresholds are exceeded. Higher hydraulic conductivity and smaller island width exacerbate the salt accumulation by enhancing the salt load introduced during each storm-surge event and decelerating the subsequent flushing processes. The transition between the regimes is captured by a single dimensionless number, E, which integrates recurrent storm-surge characteristics affecting salt load (intensity and frequency). In the second regime, the excess salt load and freshwater volume at new dynamic equilibria are effectively approximated by linear functions of E. This framework offers a straightforward tool for the preliminary assessment of climate-change impacts on coastal groundwater systems, particularly for small islands with limited and vulnerable freshwater resources, thereby supporting proactive water security strategies against climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".