Watershed modeling using arc hydro based on DEMs: a case study in Jackpine watershed
Why this work is in the frame
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Bibliographic record
Abstract
Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). In this study, DEMs and stream network data were used to model the Jackpine Watershed in Ontario, Canada, using Arc Hydro Tools. The modeling results include stream network and catchment delineation. The effects of the DEM reconditioning process and the stream threshold value on the modeling accuracy were examined through three simulations. The accuracy was discussed by overlying the actual and simulated maps, as well as by comparing stream densities, network lengths and numbers of streams, catchment area, and number of catchments. Other possible methods to improve the watershed modeling were also discussed. It is concluded that Arc Hydro is capable of performing watershed modeling with satisfactory performance. It is shown that DEM reconditioning can improve the accuracy of watershed modeling. It is also implied that lower stream threshold value can not only lead to a more detailed stream network but also enhance the accuracy of catchment delineation.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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