Modeling surface runoff and subsurface tile drainage under regular drainage and controlled drainage with sub- irrigation in Southern Ontario
Bibliographic record
Abstract
Controlled drainage with subirrigation has been applied as a strategy in southern Ontario to mitigate nutrient loss from subsurface drained cropland to surface water bodies. The Root Zone Water Quality Model (RZWQM2) has been widely used for simulating management effects on crop production and soil and water quality, and a subirrigation component was recently developed. The objective of this study is to model surface runoff, subsurface tile drainage, and crop yield under two water management practices: regular drainage (DR) and controlled drainage with subirrigation (CDS) in southern Ontario. Field observed hydrological and yield data under those two water management practices near Harrow, ON from June 2008 to December 2011 were used to evaluate RZWQM2. The measured surface and subsurface water discharges were monitored continuously year round in a corn-soybean rotation field. Subirrigation was not measured but was estimated assuming it met the daily crop ET computed by the model. RZWQM2 was calibrated and validated against tile drainage and yield data from regular drainage and controlled drainage with subirrigation, respectively. For the calibration against runoff and tile drainage data under regular drainage, percent bias (PBIAS) was within ±15%, Nash-Sutcliffe efficiency (NSE) > 0.50, and index of agreement (IoA) > 0.80; however, for the validation under the controlled drainage with subirrigation, PBIAS >±15%, NSE < 0.22, and IoA < 0.78. This RZWQM2 was capable of predicting tile drainage and surface runoff under the regular drainage, but was not as precise for the controlled drainage with subirrigation treatment. This may be attributable to a poor estimation of sub-irrigation amount as model input.
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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 | 0.000 |
| 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 | 0.000 | 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".