Decades of Change: Monthly and Seasonal Perspective of Sediment and Phosphorus Loads in Agricultural Watersheds
Bibliographic record
Abstract
ABSTRACT Agricultural land use significantly contributes to the deterioration of water quality in the Great Lakes basin. The Pollution from Land Use Activities Reference Group (PLUARG) analysed diverse agricultural watersheds for nonpoint source (NPS) pollution during 1975–1977. The Multi‐Watershed Nutrient Study (MWNS), conducted from 2015 to 2020, revisited some of these watersheds to assess long‐term changes in NPS pollution. For direct comparison with the PLUARG study, a subset of MWNS data from 2017 to 2019 was selected to match the two‐year duration of the PLUARG dataset. A comparative analysis of the two studies reveals notable temporal and spatial variations in runoff, sediment, and total phosphorus (TP) loads. Season I (January–April) exhibited the highest sediment and TP loads. During the PLUARG period, sediment and TP loads were primarily concentrated in February and March. In contrast, MWNS data show an extended loading period spanning February, March, April, and May indicating a shift in seasonal and monthly load distribution patterns. For five of the six watersheds, annual average sediment and TP load magnitudes showed only minor changes between the two study periods. However, North Creek showed a marked increase in both sediment and TP loads, suggesting a shift in load generation behaviour (Sediment loads increased from 0.82 to 1.95 kg/ha/day and TP loads increased from 3.84 × 10 −3 to 6.03 × 10 −3 kg/ha/day). The transportation of sediment and TP loads is highly event‐oriented, with 80%–90% of annual loads occurring during the top 5%–10% of high‐flow events in all watersheds, emphasising the role of hydrological extremes in nutrient mobilisation. These findings highlight critical periods for sediment and phosphorus transport and emphasise the importance of targeted watershed management practices to mitigate NPS pollution effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".