Factors controlling specific sediment yield in the upper Indus River basin, northern Pakistan
Bibliographic record
Abstract
Abstract Estimates of sediment yield are essential in water resources analysis, modelling and engineering, in investigations of continental denudation rates, and in studies of drainage basin response to changes in climate and land use. The availability of high resolution, global environmental datasets offers an opportunity to examine the relationships between specific sediment yield (SYsp) and drainage basin attributes in a geographical information system (GIS) environment. This study examines SYsp at 14 long‐term gauging stations within the upper Indus River basin. Twenty‐nine environmental variables were derived from global datasets, the majority with a 1 × 1 km resolution. The SYsp ranges from 194 to 1302 t km−2 yr−1 for sub‐basins ranging from 567 to 212 447 km2. The high degree of scatter in SYsp is greatly reduced when the stations are divided into three groups: upper, glacierized sub‐basins; lower, monsoon sub‐basins; and the main Indus River. Percentage snow/ice cover (LCs) emerges as the single major land cover control for SYsp in the high mountainous upper Indus River basin. A regression model with percentage snow/ice cover (LCs) as the single independent variable explains 73·4% of the variance in SYsp for the whole Indus basin. A combination of percentage snow/ice cover (LCs), relief and climate variables explains 98·5% of the variance for the upper, glacierized sub‐basins. For the lower monsoon region, a regression model with only mean annual precipitation (P) explains 99·4% of the variance. Along the main Indus River, a regression model including just basin relief (R) explains 92·4% of the variance in SYsp. Based on the R2adj and P‐value statistics, the variables used are capable of explaining the majority of variance in the upper Indus River basin. Copyright © 2007 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".