Predicting the Impact of Drainage Ditches upon Hydrology and Sediment Loads Using KINEROS 2 Model: A Case Study in Ontario.
Bibliographic record
Abstract
Hydrologic models are calibrated and validated with an existing drainage network/drainage pattern (DNDP). However, in present times water could be routed through alternative DNDPs. The main objective of thispaper was to explore the performance of KINEROS 2 model in predicting streamflow and sediment yield in response to alterations in DNDP. Adopting the existing DNDP as an input, the model was calibrated for three events (18 April 2013, 12 June 2012, and 12 June 2013) and validated for two events (12 April 2014, and 30 August 2013) for flow at the watershed outlet. Further, the model was calibrated for eight events and validated for seven events for sediment content at the watershed outlet. Thereafter, the model was driven with a modified DNDP, and its response upon peak flow, direct runoff and sediment yield were investigated for two events (12 April 2014 and 18 April 2013) and a synthetic design storm (2-year-24 hour) at a sub-basin outlet (GUL_RSD). Three DNDPs: DNDP_M (road-side ditches with the same Manning's n), DNDP_MV (road-side ditches lined with medium vegetation), and DNDP_HV (road-side ditches lined with thick vegetation) were considered. KINEROS 2 results revealed that peak flow, direct runoff, and sediment yield increased by 47.36 %, 31.39 %, and 26.96 % respectively for 12 April 2014 event for DNDP_M. Similar results were obtained for 18 April 2013 and synthetic design storm events. However, when road-side ditches were lined with a thicker vegetation (DNDP_MV and DNDP_HV), a reduction in peak flow, direct runoff, and sediment yield was observed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".