Application of CANWET and HSPF for TMDL Evaluation under Southern Ontario Conditions
Bibliographic record
Abstract
The CANWET (Canadian ArcView Nutrient and Water Evaluation Tool) and HSPF (Hydrologic Simulation Program - FORTRAN) models were applied to Upper Canagagigue Creek watershed of the Grand River basin in southern Ontario, Canada, for hydrology and sediment evaluations. Both the models have similarity in structure where CANWET is simpler, both in algorithms and use, than HSPF. The outputs of both the models for water budgeting components were compared on annual, seasonal, and monthly basis. The water budget components, evapotranspiration, surface runoff, and subsurface runoff produced by both the models were comparable on annual and seasonal time steps; however, there were some discrepancies in monthly and daily simulations. The seasonal, monthly, and daily Nash-Sutcliffe efficiency coefficient with observed stream flows were 0.83, 0.81, and 0.48 for HSPF, respectively, and 0.80, 0.67, and 0.24 for CANWET, respectively. The monthly and daily simulations by HSPF model were better since HSPF algorithm has more control on temporal variation in parameters sensitive for hydrologic simulations. The sediment simulations by both the models were consistently close for erosion and sediment yield on annual basis. However, superiority in predictions for total suspended sediment yield of one model over the other could not be concluded because of lack of observed data. The daily load of sediment modeled by HSPF followed flow peaks and available observed sediment data points.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".