Effects of area under‐estimations of sloped mountain terrain on simulated hydrological behaviour: a case study using the ACRU model
Bibliographic record
Abstract
Abstract Sloped areas calculated from a GIS raster file, such as a digital elevation model, are smaller than the true surface area, because they are projected to a planimetric plane. In mountainous regions this sloped area under‐estimation (SAUE) can have significant consequences on hydrological calculations. A sensitivity analysis is conducted, using the ACRU agro‐hydrological modelling system in a small watershed in Glacier National Park, Montana, USA, to investigate the sensitivity of the SAUE on key elements of the hydrological cycle, including precipitation depth, April snow depth, August soil moisture deficit, actual evapotranspiration depth, and runoff depth. The sensitivity analysis is based on 224 unique combinations of slope, soil and land cover types, elevation with associated precipitation depths, and north and south facing radiation regimes. Results revealed an increasing influence of the SAUE on all hydrological processes with increasing slope steepness. Distinct differences and magnitudes between different land cover types, different elevations, and, in particular, different exposition were quantified. Actual evapotranspiration increases with SAUE, while runoff decreases. April soil water is simulated to decrease with an increase in SAUE. Finally, a comparison of a streamflow simulation of a small and steep alpine watershed with and without consideration of the SAUE is carried out. The sloped area of the small watershed is under‐estimated by 20·9%, and the difference in simulated runoff is 12·3%. When the SAUE was not considered, runoff was simulated to be higher, the associated coefficient of determination was slightly lower, and the slope of the regression line was flatter. Copyright © 2010 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".