Estimating bed material transport in Himalayan streams using the virtual velocity approach
Bibliographic record
Abstract
This study investigates sediment mobility and transport dynamics in two Himalayan rivers, the Aglar and Paligad Rivers, during both monsoon and non-monsoon flows. Employing the virtual velocity approach, key parameters such as bed proportional mobility (Y), active layer depth (ds), and displacement length were measured to estimate the virtual velocity of mobilized grains. Local parameters (0.5 m sub-sections) and wetted cross-sectional averages were utilized. Using local parameters, the total annual bed material transport was determined as 67,100 t (±20,400 t) and 18,400 t (±6,000 t) for the Aglar and Paligad Rivers, respectively, with nearly 60% occurring during the monsoon. The significant contribution of non-monsoonal flows (~ 40 %) could be ascertained to higher enough flows in specific sub-sections inducing partial or full mobility. Still, the contribution of partial transport (PT) remained lower (< 6%). In contrast, based on cross-section average parameters, total transport was estimated at 42,300 t (±15,800 t) and 12,200 t (±4,700 t) for the Aglar and Paligad Rivers, respectively, with approximately 79% and 68% occurring during the monsoon. The contribution of PT increased to nearly 18% and 29% for the Aglar and Paligad Rivers, respectively, attributed to the averaging effects of shallower sections. Furthermore, the interdependence of partial transport on Y and full transport on ds leads to discontinuities in transport curves, prompting the proposal of a unified function to represent transport extent for both partial and full transport conditions. The unified function ensured the generation of continuous transport curves, yielding similar transport patterns concerning the contribution of PT, FT, monsoonal, and non-monsoonal flows. The findings are particularly relevant for efficient river management as the region houses several hydropower plants and is highly susceptible to climate change.Keywords:Painted tracers, partial transport, full transport, active layer, monsoonal flows
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".