Improving overland flow routing by incorporating ancillary road data into Digital Elevation Models
Bibliographic record
Abstract
Roads, ditches, and culverts influence hydrological and geomorphological processes significantly. However, most hydrological models continue to rely solely on regional digital elevation models (DEMs) to derive overland flow directions even though these DEMs have been shown to contain inadequate topographical information to effectively represent linear landscape features. This paper introduces a methodology that improves the accuracy of grid-based overland flow routing through the use of ancillary road, ditch, and culvert data. The road enforcement algorithm (REA) that was developed re-routes overland flow on either side of a road independently, thereby enforcing linear landscape features within the flow direction matrix. The overland flow patterns resulting from the implementation of the REA differ significantly from flow patterns derived using conventional GIS routing algorithms. A test application in the prairies of southern Alberta, Canada shows the REA affects intra-watershed runoff transport as well as the size and shape of DEM-derived watersheds. The flow direction matrices created with the REA can be incorporated into any grid-based hydrological model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".