Influence of precipitation event magnitude on baseflow and coastal nitrate export for Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract The export of anthropogenic nitrate to coastal waters, which depends on the interplay between many factors such as land use and meteorological forcing, is a rising concern in many regions of the world. The present study investigates the effect of precipitation event magnitude on baseflow and associated groundwater‐driven nitrate export in Prince Edward Island, Canada. Twenty‐year time‐series of precipitation, stream flow, and groundwater levels across the island were analysed to establish a three‐way relationship between precipitation, groundwater level rise, and baseflow increase along a hydrological response pathway in this island setting. The analysis was performed by extracting hydrological responses for groundwater level and baseflow for a selected subset of relatively isolated precipitation events. The results reveal a non‐linear relationship between precipitation event magnitude and baseflow change that is also observed for precipitation events associated with hurricanes and post tropical storms. A time‐series of streamflow nitrate concentrations during Hurricane Dorian's passage (September 2019) was used to evaluate the relevance of these findings for nitrate export. The data show that baseflow increases after a heavy precipitation event have limited impact on streamflow concentrations, but result in substantial and sustained increase of nitrate export (calculated by multiplying concentration by flow). These observations are consistent with a recharge‐induced water table rise leading to increased hydraulic gradients that drive discharge of shallow nitrate‐containing groundwater. Due to the similarity of the processes governing groundwater flow to the sea and to streams, the results can be used to gain insights into the impacts of precipitation event magnitude on direct groundwater discharge to coastal waters. On Prince Edward Island, this applies to at least the 13% of the island's surface area that is closer to the ocean than to any stream and likely feeds submarine groundwater discharge pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".