EFFECT OF DIGITAL ELEVATION MODEL RESOLUTION ON EMPIRICAL ESTIMATION OF SOIL LOSS AND SEDIMENT TRANSPORT WITH GIS
Bibliographic record
Abstract
The horizontal accuracy of topographic data represented by digital elevation model (DEM) resolution brings about uncertainties in landscape process modeling with raster GIS. This paper presents a study on the effect of topographic variability on cell-based empirical estimation of soil loss and sediment transport. An original DEM of 10m resolution for a case watershed was re-sampled to three realizations of higher grid sizes for a comparative examination. Equations based on the USLE are applied to the watershed to calculate soil loss from each cell and total sediment transport to streams. The study found that the calculated total soil loss from the watershed decreases with the increasing DEM resolution with a linear correlation as spatial variability is reduced by cell aggregation. The USLE topographic factors (LS) extracted from applied DEMs represent spatial variability, and determine the estimations as shown in the modeling results. The commonly used USGS 30m DEM appears to be able to reflect essential spatial variability and suitable for the empirical estimation. The appropriateness of a DEM resolution is dependent upon specific landscape characteristics, applied model and its parameterization. This work attempts to provide a general framework for the research in the DEM-based empirical modeling.
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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 | 0.006 | 0.037 |
| 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 | 0.000 | 0.001 |
| 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 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".