Vertical Saltwater Intrusion in Coastal Aquifers Driven by Episodic Flooding: A Review
Bibliographic record
Abstract
Abstract Low‐elevation coastal areas are increasingly vulnerable to seawater flooding as sea levels rise and the frequency and intensity of large storms increase with climate change. Seawater flooding can lead to the salinization of fresh coastal aquifers by vertical saltwater intrusion (SWI). Vertical SWI is often overlooked in coastal zone threat assessments despite the risk it poses to critical freshwater resources and salt‐intolerant ecosystems that sustain coastal populations. This review synthesizes field and modeling approaches for investigating vertical SWI and the practical and theoretical understanding of salinization and flushing processes obtained from prior studies. The synthesis explores complex vertical SWI dynamics that are influenced by density‐dependent flow and oceanic, hydrologic, geologic, climatic, and anthropogenic forcings acting on coastal aquifers across spatial and temporal scales. Key knowledge gaps, management challenges, and research opportunities are identified to help advance our understanding of the vulnerability of fresh coastal groundwater. Past modeling studies often focus on idealized aquifer systems, and thus future work could consider more diverse geologic, climatic, and topographic environments. Concurrent field and modeling programs should be sustained over time to capture interactions between physical processes, repeated salinization and flushing events, and delayed aquifer responses. Finally, this review highlights the need for improved coordination and knowledge translation across disciplines (e.g., coastal engineering, hydrogeology, oceanography, social science) to gain a more holistic understanding of vertical SWI. There also needs to be more education of communities, policy makers, and managers to motivate societal action to address coastal groundwater vulnerability in a changing climate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".