Improved calibration scheme of SWAT by separating wet and dry seasons
Bibliographic record
Abstract
Simulation of low flow process is critical to water quality, water supply, and aquatic habitat. However, the poor performance of Soil and Water Assessment Tool (SWAT) in dry seasons has impeded its application to watersheds characterized largely by low-flows. Aiming at overcoming this shortage, a seasonal calibration scheme was proposed, in which SWAT was calibrated separately for the dry and wet periods and the “optimal” simulation results of these two periods were combined into a complete runoff series. An extended SWAT model incorporating with the proposed seasonal calibration scheme, named SWAT-SC was constructed and compared with the original SWAT to simulate daily runoff in the Jinjiang watershed dominated by a typical subtropical monsoon climate in southeastern China. The study reveals that when Nash-Sutcliffe efficiency (ENS) of the original SWAT model indicated a satisfied model performance in a wet season or a whole year, it may not guaranty acceptable performance for the dry period. A significant improvement was achieved by using SWAT-SC for simulating runoffs in the dry period, and although not as notably as the dry period, improvements for runoff simulation of the wet and overall periods were observed as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".