Runoff simulation in the Canagagigue Creek watershed using the MIKE SHE model
Bibliographic record
Abstract
The Canagagigue Creek watershed, located in the Grand River Basin, is one of the fastest developing areas in Ontario. The watershed hydrology has changed considerably due to the increased anthropogenic activities, producing frequent floods and droughts as well as water quality problems. MIKE SHE, a watershed-scale model, was used to simulate surface runoff from the Canagagigue Creek watershed. Various management scenarios affecting the surface hydrology were also evaluated. The model was calibrated for four years (1994-95 to 1997-98) and validated for another four years (1990-91 to 19931-94). For the calibration period, the correlation between the observed and simulated daily runoff was satisfactory, as shown by the coefficient of determination value of 0.59. The coefficient of determination was 0.44 for the validation period. The Nash-Sutcliffe coefficients obtained were 59% and 40% for the calibration and validation period, respectively. Use of daily input data together with a simplistic snowmelt routine, was found to affect model performance during the winter/spring period. Henceforth, model performance can be greatly improved by adopting a more comprehensive method for simulating snowmelt and incorporating the frozen soil conditions. Overall, the model was able to simulate surface runoff reasonably well on annual, seasonal, monthly, and daily intervals, representing all the hydrological components adequately. With the various management scenarios simulated, it was found that the deforestation scenario considerably increased the total flow (11%). On the other hand, the high runoff peaks were decreased and low flows were increased considerably in the application of the tile drainage scenario. It was also observed that surface flow increased in wet years and decreased in normal and dry years in the climate change scenario. Though impacts of certain scenarios were almost negligible, their effects were significant when associated with the percentage area under transformation. Hence, it was concluded that the model can be used to simulate various management scenarios to solve hydrologic problems in the Southern Ontario climatic condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".