Identifying threshold storm events and quantifying potential impacts of climate change on sediment yield in a small upland agricultural watershed of <scp>Ontario</scp>
Bibliographic record
Abstract
Abstract Agricultural zones are significant sediment sources, but it is crucial to identify critical source areas (CSAs) of sediment yield within these zones where best management practices (BMPs) can be applied to the best effect in reducing sediment delivery to receiving water bodies rather than the economically nonviable alternative of randomly or sweepingly implementing BMPs. A storm event of a specific magnitude and hyetograph profile may, at different times, generate a greater or lesser sediment yield. The widely used agricultural nonpoint source (AGNPS) model was used to identify CSAs for sediment losses in Southwestern Ontario's agriculture‐dominated 374‐ha Holtby watershed. A storm threshold approach was adopted to identify critical periods for higher sediment losses. An AGNPS model for the Holtby watershed was set up, calibrated, and validated for run‐off volume, peak flow rate, and sediment yield for several storms. The calibrated and validated model was run for storms of increasing return periods to identify threshold storm events that would generate sediment yield greater than an acceptable value for early and late spring, summer, and fall seasons. Finally, to evaluate the potential impacts of climate change, we shifted shorter duration summer storms into spring conditions and quantified the changes in sediment yield dynamics. A 6‐hr, 7.5‐year early spring storm would generate sediment losses exceeding the acceptable limit of 0.34 t ha−1 for the season. However, summer storms (2 hr, up to 100 years) tended to generate sediment yields below those of an identifiable threshold storm. If such shorter duration summer storms occurred in spring, the sediment yield would increase by more than fivefold. A 5‐year future storm would generate an equivalent effect of a 100‐year current spring event. The high sediment delivery to be expected will have significant implications regarding the future management of water quality of receiving waters. Appropriate placement of BMPs at CSAs will thus be needed to reduce such high sediment delivery to receiving waters.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".