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 ( SY sp ) and drainage basin attributes in a geographical information system (GIS) environment. This study examines SY sp 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 SY sp ranges from 194 to 1302 t km −2 yr −1 for sub‐basins ranging from 567 to 212 447 km 2 . The high degree of scatter in SY sp 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 ( LC s ) emerges as the single major land cover control for SY sp in the high mountainous upper Indus River basin. A regression model with percentage snow/ice cover ( LC s ) as the single independent variable explains 73·4% of the variance in SY sp for the whole Indus basin. A combination of percentage snow/ice cover ( LC s ), 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 SY sp . Based on the R 2 adj 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".