Mapping Future Coastal Groundwater Flooding and Impacted Infrastructure Using a Hybrid Analytical–Geospatial Approach
Bibliographic record
Abstract
ABSTRACT Surface flooding from rising sea levels is widely recognised as a coastal threat, but coastal subsurface (groundwater) flooding is often overlooked. Coastal water table rise due to sea‐level rise can accelerate contaminant flow from onsite wastewater treatment systems (OWTS), decrease roadway performance, corrode below‐ground infrastructure, and intensify surface flooding. The few related, existing studies were primarily local, leaving important gaps for regional coastal risk assessments. In this study, we employed analytical solutions for coastal water table rise within a new geospatial framework to facilitate risk assessments at larger scales relevant for governance. An ArcGIS toolbox was developed to classify coastal aquifers as recharge or topography limited and perform the analytical calculations along a regional coastline using publicly available geospatial datasets. The analysis was conducted for Nova Scotia (~10 000 km shoreline), an Atlantic Canadian province characterised by high relative sea‐level rise and reliance on private wells and OWTS. Results highlight coastal regions expected to experience shallow groundwater and possessing potentially vulnerable infrastructure (water table depth < 1.5 m) before and after (year 2099) projected sea‐level rise. The model revealed that the most southwestern county (Yarmouth) will experience the highest percent of coastal land with groundwater flooding before (5.7%) and after (7.1%) sea‐level rise. Because of differences in coastal infrastructure distribution, the Bay of Fundy (Annapolis County) coastline had the highest fraction of potentially compromised OWTS and roadways (up to 12.8%), while the highest relative increase in potentially compromised OWTS by 2099 was in the southeast (+115%, Shelburne County). This novel hybrid analytical–geospatial approach serves as a first‐order assessment and can be used to prioritise localities for further site‐specific research and inform targeted coastal development policies.
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 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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".