The limits of watershed delineation: implications of different DEMs, DEM resolutions, and area threshold values
Bibliographic record
Abstract
Abstract Identifying and demarcating watershed areas provides a basis for designing and planning for water resources. In this study, DEMs-based estimates of watershed characteristics of three rivers of Bangladesh – Halda, Sangu, and Chengi – were derived using eight Digital Elevation Models (DEMs) of 30 m, 90 m, and 225 m resolution in the Soil and Water Assessment Tool (SWAT). We have assessed watershed characteristics concerning DEMs, resolutions, and Area Threshold Values (ATVs). Though the elevation data differed, high correlation values among DEMs and resolutions confirm the negligible effect of elevation in the watershed delineation. However, the slope and watershed delineation vary for different DEMs and resolutions. The 90 m DEMs estimated larger areas for Halda and Chengi and lower perimeter values for all three rivers. In watershed delineation, the area near the mouth and flat terrain did not coincide with DEMs. The common intersected area by DEMs can be used as the focal area of watershed management. ATV ≤ 40 km2 significantly influences sub-basin counts and stream network extraction for these watershed areas. Though watershed size and shape were independent of the different ATVs, the DEM-based watershed delineation process in SWAT needs optimum ATV values to represent the stream network precisely.
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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 | 0.000 | 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 | 0.000 |
| 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".