A reservoir-based approach of the SWAT hydrological model in the Napanee River and Wilton Creek agricultural watersheds, Bay of Quinte
Bibliographic record
Abstract
In this study, we employ the Soil and Water Assessment Tool (SWAT), to simulate the hydrological cycle in the Napanee River and Wilton Creek watersheds located in the Bay of Quinte Area of Concern. The geomorphological attributes of the two sites presented challenges in successfully reproducing the observed streamflow with a conceptual model, like SWAT. After adjusting the model inputs and revising the boundary conditions, SWAT performance for the Napanee River model was found to be “very good” within both calibration (Nash-Sutcliffe efficiency, NSE, =0.83 and NSE = 0.87 for the daily and monthly streamflow rates, respectively) and validation (NSE = 0.87 and NSE = 0.91) domains. In contrast, despite the small size of Wilton Creek (∼233 km2) and the lack of permanent or human-regulated dams, the observed streamflow patterns displayed considerably delayed response to extreme precipitation events which posed challenges in achieving satisfactory model performance. Field evidence suggests that model residual variability is driven by excessive surface storage of water, the presence of beaver dams and narrowing of the stream channels under the bridges, which could potentially delay the flow of water for many days. After delineating the areas of flooding and treating them as reservoirs in Wilton Creek, we were able to overcome the modelling challenges with minor modifications in the original SWAT configuration and achieve satisfactory fit for both the calibration (NSE = 0.55 and NSE = 0.73) and validation (NSE = 0.59 and NSE = 0.76) periods. Our study suggests that the reservoirs play a significant role in buffering the impact of extreme flow conditions, especially during the spring freshet
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".