Groundwater nitrate concentration evolution under climate change and agricultural adaptation scenarios: Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract. Nitrate (N-NO3) concentration in groundwater, the sole source of potable water in Prince Edward Island (PEI, Canada), currently exceeds the 10 mg L−1 (N-NO3) health threshold for drinking water in 6 % of domestic wells. Increasing climatic and socio-economic pressures on PEI agriculture may further deteriorate groundwater quality. This study assesses how groundwater nitrate concentration could evolve due to the forecasted climate change and its related potential changes in agricultural practices. For this purpose, a tridimensional numerical groundwater flow and mass transport model was developed for the aquifer system of the entire Island (5660 km2). A number of different groundwater flow and mass transport simulations were made to evaluate the potential impact of the projected climate change and agricultural adaptation. According to the simulations for year 2050, N-NO3 concentration would increase due to two main causes: (1) the progressive attainment of steady-state conditions related to present-day nitrogen loadings, and (2) the increase in nitrogen loadings due to changes in agricultural practices provoked by future climatic conditions. The combined effects of equilibration with loadings, climate and agricultural adaptation would lead to a 25 to 32 % increase in N-NO3 concentration over the Island aquifer system. The change in groundwater recharge regime induced by climate change (with current agricultural practices) would only contribute 0 to 6 % of that increase for the various climate scenarios. Moreover, simulated trends in groundwater N-NO3 concentration suggest that an increased number of domestic wells (more than doubling) would exceed the nitrate drinking water criteria. This study underlines the need to develop and apply better agricultural management practices to ensure sustainability of long-term groundwater resources. The simulations also show that observable benefits from positive changes in agricultural practices would be delayed in time due to the slow dynamics of nitrate transport within the aquifer system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".