Effects of digital elevation model resolution on derived stream network positions
Bibliographic record
Abstract
This study examines the effect of digital elevation model (DEM) resolution on the positional accuracy of derived hydrologic networks and quantitatively confirms that DEM resolution should be greater than the average hillslope length when used for hydrologic modeling. Seven hundred kilometers of mapped stream networks are compared to stream networks derived from 17 DEMs with resolutions ranging from 30 m to 3 km. Comparison between predicted and mapped streams reveals that accuracy of predicted stream locations decays quickly beyond a DEM resolution of 180 m. A new application of Gyasi‐Agyei et al.'s [1995] DEM resolution suitability test based on average slope and vertical resolution indicates an average hillslope length of between 150 and 180 m. Tarboton et al.'s [1991] method of determining average hillslope length based on slope and accumulation areas reconfirms the length to be 150 m and verifies the link between network accuracy and hillslope scale. Two algorithms are used to derive stream networks: the D∞ algorithm [ Tarboton, 1997 ], which allows for flow dispersion, and the D8 algorithm [ O'Callaghan and Mark, 1984 ], which does not. A comparison between the D8 and D∞ algorithms shows that modeling flow dispersion is not necessary in steep terrain, as both algorithms performed equally well. However, the D∞ algorithm is found to be less susceptible to modeling erroneous hillside flow convergence, making it preferable to the D8 algorithm when used by Tarboton et al.'s [1991] method of determining the average hillslope lengths. Finally, field observation indicates that predicted first‐ and second‐order streams tend to exist when predicted; however, mapped streams have a higher positional accuracy.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".