Linking sediment flux to river migration in arid landscapes through mass balance
Bibliographic record
Abstract
ABSTRACT Lateral migration is a key process shaping sinuous rivers and controlling sediment exchange with floodplains. The rate at which channels migrate is affected by bend curvature, bank erodibility, and sediment supply. The relationship between migration rate and sediment supply is poorly understood in dryland regions, where direct measurements are scarce. Here we propose a simple mass-balance model to estimate the sediment flux of ephemeral streams in North America's Great Basin and establish a comparison with timelapse photogrammetric data of lateral migration. The model takes into consideration variables such as long-term hillslope erosion, transient sediment storage in intra-catchment lowlands, and sediment bypass to depocenters. Our results point to first-order similarities in how sediment supply drives channel migration across diverse hydro-climatic regimes. However, we find that, for a given sediment supply and channel width, and despite their ephemeral discharge, dryland streams with minimal bank vegetation migrate about three times faster than humid-climate, vegetated ones. This difference in migration pace likely results from the compound effect of bank erodibility and bend geometry. Our model sheds new light on the driving mechanisms of channel mobility in dryland streams and may find application in assessing the sediment budgets of ungauged streams, reservoir trapping, and morphodynamic adjustments in stressed watersheds.
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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 | 0.000 |
| 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) | 0.001 | 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".