Impacts of climate change and best management practices on nitrate loading to a eutrophic coastal lagoon
Bibliographic record
Abstract
Anthropogenic climate change and associated increasing nutrient loading to coasts will worsen coastal eutrophication on a global scale. Basin Head is a coastal lagoon located in northeastern Prince Edward Island, Canada, with a federally protected ecosystem. Nitrate-nitrogen (NO3-N) is conveyed from agricultural fields in the watershed to the eutrophic lagoon via intertidal groundwater springs and groundwater-dominated tributaries. A field program focused on four main tributaries that discharge into the lagoon was conducted to measure year-round NO3-N loading. These measurements were used to calibrate a SWAT+ hydrologic model capable of simulating hydrologic and NO3-N loads to the lagoon. Several climate change scenarios incorporating different agricultural best management practices (BMPs) were simulated to better understand potential future NO3-N loading dynamics. Results indicate that all climate change scenarios produced increased annual NO3-N loading to the lagoon when comparing historical (1990–2020) to end of century time periods (2070–2100); however, only one climate scenario (MRI-ESM2-0 SSP5-8.5) resulted in a statistically significant (p-value <0.05) increase. Enlarged buffer strips and delayed tillage BMP simulations produced small (0%–8%) effects on loading, while changing the crop rotation from potato-barley-clover to potato-soybean-barley yielded a small reduction in NO3-N loading between the historical period and the end of the century (26%–33%). Modeling revealed changes in seasonal loading dynamics under climate change where NO3-N loads remained more consistent throughout the year as opposed to current conditions where the dominant load is in the spring. An increase in baseflow contributions to streamflow was also noted under climate change, with the largest change occurring in the winter (e.g., up to a five-fold increase in February). These findings have direct implications for coastal management in groundwater-dominated agricultural watersheds in a changing climate.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".