Impacts of groundwater dynamics around a macro-tidal river on agricultural soil salinity
Bibliographic record
Abstract
Estuaries are vulnerable to oceanic and atmospheric climate change. Much of the research investigating climate change impacts on estuaries is focused on saltwater intrusion within surface water due to drought and rising sea levels, with implications for ecosystems and humans. Groundwater and soil near estuaries may also be influenced, as estuary salinity and hydraulic head changes can impact soils and aquifers not previously at risk of salinization. This study was conducted to address knowledge gaps related to present and future groundwater salinity distribution in a groundwater system connected to a macro-tidal estuary. The studied estuary experiences a tidal bore due to its hydraulic connection to the Bay of Fundy in Nova Scotia, Canada. A parcel of agricultural land adjacent to the estuary was selected to assess the groundwater response to episodic fluctuations in estuary water levels and salinity. Groundwater monitoring and electromagnetic surveys were conducted to map soil and groundwater salinity patterns. A numerical model of groundwater flow and solute transport informed by field data was used to investigate how varying estuary salinity due to droughts and sea-level rise could impact groundwater salinity. Results showed that, in contrast to salt wedges observed along marine coasts, the saline groundwater existed as a plume immediately around the estuary. Model simulations showed that short-term droughts had an insignificant impact on the adjacent groundwater salinity. However, permanent increases in salinity caused by sea-level rise increased the plume volume by 86 %, or an additional ∼11 m horizontally and ∼ 4.5 m vertically. Our results suggest that increased river salinity in this setting would not result in widespread salinization of porewater and agricultural soils, but more extensive salinization may be experienced in permeable aquifers or along more saline estuarine zones. Findings may inform land management decisions in regions exposed to increased salinity in the future.
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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 | 0.000 |
| 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 | 0.000 |
| 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".