MétaCan
Menu
← Back to cohort

Climate change alters post-surge recovery of coastal aquifers

2025· preprint· en· W4414120575 on OpenAlexafffund
Satoshi Tajima, René Therrien, Philip Brunner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversité Laval
FundersGovernment of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsClimate changeOverwashAquiferEffects of global warmingHydrology (agriculture)Global warming

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.242
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same topicMethane Hydrates and Related Phenomena→French-language works237,207→