Hydrological and water quality modeling of agricultural fields in Quebec
Bibliographic record
Abstract
Two tile-drained agricultural fields in the Pike River watershed of Southern Quebec were instrumented in October 2000 to monitor phosphorus and nitrate concentrations in surface runoff and tile drainage. Data collected from these sites were used as the primary input to test a GIS-based hydrological and water quality simulation model (ArcView SWAT2000) at the field scale. Surface runoff, subsurface flow, sediment yield, nitrate loads and phosphorus loads were the principal parameters evaluated by the model. The SWAT model was calibrated using data collected in the year 2002 while 2003 data was used for validating the model. Particulate phosphorus and total dissolved phosphorus loads in streamflow were also simulated using SWAT and compared with field measurements. A sensitivity analysis showed that curve number, available soil water content and soil evaporation factors significantly influenced water yield simulations while model performance for water quality parameters was governed mainly by the accuracy of simulating field operations such as fertilization and tillage. The monthly coefficients of performance after calibration ranged from being very good for some parameters (0.27 to 0.66 for total water yield; 0.38 to 0.67 for total phosphorus; and 0.23 to 0.89 for sediments) to being inconsistent for others (0.44 to 2.28 for subsurface flow; 0.63 to 4.36 for surface runoff; and 0.66 to 1.35 for total nitrate loads). Overall, it was found that SWAT results on a seasonal scale were generally more reliable whereas daily or monthly simulations could be improved by using a longer calibration period or incorporating model changes. Short-term impacts of implementing different best management practices for tillage, crop rotation and fertilization were also evaluated using the validated SWAT model. It was found that conservation tillage of corn coupled with pasture or soybean rotations can reduce total phosphorus loads in the range of 25-50% over conventional tillage with corn.
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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) | 0.000 | 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) | 0.002 | 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".