Memory and Management: Competing Controls on Long-Term Nitrate Trajectories in U.S. Rivers
Bibliographic record
Abstract
Excess nitrogen from intensive agricultural production, atmospheric N deposition, and urban point sources elevates stream nitrate concentrations, leading to problems of eutrophication and ecosystem degradation in coastal waters. A major emphasis of current US-scale analysis of water quality is to better our understanding of the relationship between changes in anthropogenic N inputs within watersheds and subsequent changes in riverine N loads. While most water quality modeling assumes a positive linear correlation between watershed N inputs and riverine N, many efforts to reduce riverine N through improved nutrient management practices result in little or no short-term improvements in water quality. Here, we use nitrate concentration and load data from 478 US watersheds, along with developed N input trajectories for these watersheds, to quantify time-varying relationships between N inputs and riverine N export. Our results show substantial variations in watershed N import-export relationships over time, with quantifiable hysteresis effects. Our results show that more population-dense urban watersheds in the northeastern U.S. more frequently show clockwise hysteresis relationships between N imports and riverine N export, with accelerated improvements in water quality being achieved through the implementation of point-source controls. In contrast, counterclockwise hysteresis dynamics are more common in agricultural watersheds, where time lags occur between the implementation of nutrient management practices and water-quality improvements. Finally, we find higher tile-drainage densities to be associated with more linear relationships between N inputs and riverine N. The empirical analysis in this study is bolstered by modeled simulations to reproduce and further explain drivers behind the hysteretic relationships commonly observed in the monitored watersheds.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".