Modelling Legacy Nitrogen Dynamics in the Transboundary Lake Erie Watershed
Bibliographic record
Abstract
Lake Erie is a source of drinking water, recreation, and commercial opportunity for both the United States and Canada, making the protection of its water quality essential. In the past decades, Lake Erie's ecosystems have been adversely impacted by recurring toxic algal blooms. These algal blooms are attributed to nitrogen (N) and phosphorus pollution from agricultural runoff. Despite recent efforts to reduce N application in the Lake Erie basin, high levels of N concentration persist in surface and groundwater systems. One of the reasons for this apparent stasis in N concentrations is legacy stores of N in landscapes that contribute to lag times in water quality response, even after inputs have ceased. Legacy N is stored in the soil and slow-moving groundwater and makes up a large portion of current N contamination. Here, we aim to quantify N legacies across the entire Lake Erie basin to predict time lags in water quality improvements in surface and groundwater. We use a process-based model, ELEMeNT, to quantify legacy N stores and watershed-scale N dynamics over the past century across the basin. Such models inform nutrient management practices across the Lake Erie basin by explicitly incorporating \nlegacy dynamics. Our study shows that N surplus (the difference between N inputs and non-hydrological N outputs) has been rising across most Lake Erie sub-watersheds since 1950 and has only started to plateau or decrease around 2000. Agricultural inputs from manure, fertilizer, and biological fixation were the lead contributors to N surplus in agricultural sub-watersheds, and domestic N was the lead N contributor in urban sub-watersheds. Since 1950, between 4% and 44% of N has been stored as legacy N (23% median). On average, 92% of this N legacy is retained in the soil and 8% is in the groundwater. Through \ncorrelation analysis, we have found that higher fractions of groundwater N and SON legacy accumulation are correlated with slower travel times and lower tile drainage, while wastewater denitrification emerged as the dominant component in urban sub-watersheds. These results provide \ninsight into drivers of legacy N and N release in sub-watersheds, which could aid in targeted nutrient management across the watershed.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".