Evaluation of streamflow simulation by SWAT model for two small watersheds under snowmelt and rainfall
Bibliographic record
Abstract
The degradation of the river water quality in Canadian rural catchments is of concern. In these catchments, the Soil Water Assessment Tool (SWAT) model can help better understand the problems related to diffuse pollution. The numerous documented applications of SWAT have been dominated by areas uniquely driven by rainfall. Given that Canadian hydroclimatic conditions differ due to the presence of a seasonal snowpack of long duration, evaluation of the hydrological performance needs to be performed prior to attempting any water quality simulations. The objective of the present work is to evaluate the hydrological behaviour of the SWAT model under snowmelt and rainfall for two small watersheds located in southeastern Canada. Different calibration schemes are evaluated including seasonal effects. One-year calibration gave satisfactory daily performances measured with Nash-Sutcliffe efficiency (NS) ranging between 61 and 83% and deviations of volume (Dv ) between −10 and 1%, while in validation, NS was 40–73% and Dv between −20 and −3%. The SWAT model has difficulties in reconciling both seasons. When winter and summer data are used separately to calibrate the model, the model performance is still much better for the winter season than for the summer one. However, the latter is considerably improved when only summer observations are provided for calibration. Conversely, calibration based strictly on the winter observations provides no real advantage over that based on all available data. A two-step composite calibration, which optimizes the SWAT snow accumulation and melt-related parameters on the winter data, after all other model parameters have been optimized on the summer data, provides a compromise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".