Evaluating Three Hydrological Distributed Watershed Models: MIKE-SHE, APEX, SWAT
Why this work is in the frame
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Bibliographic record
Abstract
Selecting the right model to simulate a specific watershed has always been a challenge, and field testing of watersheds could help researchers to use the proper model for their purposes. The performance of three popular Geographic Information System (GIS)-based watershed simulation models (European Hydrological System Model (MIKE SHE), Agricultural Policy/Environmental Extender (APEX) and Soil and Water Assessment Tool (SWAT)) were evaluated for their ability to simulate the hydrology of the 52.6 km2 Canagagigue Watershed located in the Grand River Basin in southern Ontario, Canada. All three models were calibrated for a four-year period and then validated using an independent four-year period by comparing simulated and observed daily, monthly and annual streamflow. The simulated flows generated by the three models are quite similar and closely match the observed flow, particularly for the calibration results. The mean daily/monthly flow at the outlet of the Canagagigue Watershed simulated by MIKE SHE was more accurate than that simulated by either the SWAT or the APEX model, during both the calibration and validation periods. Moreover, for the validation period, MIKE SHE predicted the overall variation of streamflow slightly better than either SWAT or APEX.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it