SENSITIVITY OF TERRAIN ATTRIBUTES, WATERSHED ATTRIBUTES, AND SWAT DERIVED HYDROLOGICAL OUTPUTS TO LIDAR DERIVED DEM UNCERTAINTY
Bibliographic record
Abstract
This research analyzes the sensitivity of watershed attributes, and hydrological outputs to LiDAR derived DEM uncertainty introduced through spatial resolution, and LiDAR measurement errors. Sensitivity of watershed attributes to spatial resolution was determined through a scaling analysis at three sites; Mosquito Creek, Scotty Creek and Thomas Brook, with DEMs ranging from 1 to 50 m. Results at Scotty Creek showed the highest sensitivity of watershed area to spatial resolution, due to subtle changes in elevation which were below DEM uncertainty. Validation of the stream length at Thomas Brook showed discrepancies of 3.7 to 24.1% for the 1 to 50 m DEMs, compared to independent field observations. Sensitivity of SWAT derived hydrological outputs to DEM spatial resolution were determined through a scaling analysis of DEMs (1 - 50 m) at Thomas Brook watershed, over a five year simulation period. Results indicated monthly water yield was insensitive to DEM resolution, unless a change in area was also present. Sediment yield from the 50 m DEM showed a 24% reduction compared to the 1 m DEM. The 5 - 50 m DEMs also showed a reduction in channel deposition of 45 - 90 t, compared to the 1 m DEM. Sensitivity of terrain attributes, watershed attributes and hydrological outputs to LiDAR measurement errors were determined at the Thomas Brook watershed through the propagation of LiDAR sensor measurement errors with Monte Carlo simulations. Results showed that the uncertainty in the DEM, slope, and aspect were below 0.06 cm, 1.5° and 24.1° in 97.5% of grid cells, respectively. Watershed area and stream length resulted in relative standard deviations of <1% and 1.5%, respectively. However, sensitivity of watershed area increased in regions with elevation changes below DEM uncertainty and stream length uncertainty increased with decreasing stream length. SWAT simulated flow and sediment showed minor sensitivity to LiDAR measurement error in high flow months, and increased as flow decreased. Simulated sediment showed higher sensitivity to LiDAR measurement errors than flow, due to changes in the HRU slope class, which can shift the dominant HRU (Hydrological Response Unit) if a minimum HRU threshold area is implemented.
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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 | 0.002 | 0.011 |
| 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 | 0.000 | 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 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".