Measuring and modeling sediment dispersion in small streams
Bibliographic record
Abstract
Turbidity-based events in multiple-use watersheds can potentially lead to negative impacts on water quality. This is of particular concern in small streams that serve as drinking water sources in the Province of British Columbia. The one-dimensional advection-dispersion equation (1D ADE) is the most common approach to modeling the transport of substances in flowing water. However, relatively little is known about its applicability to suspended sediment, especially regarding the sink term that accounts for sediment settling. The aim of this study was to assess the degree to which the 1D ADE accurately predicts suspended sediment dispersion in small channels. In addition, an evaluation of the applicability of predictive formulas for the longitudinal dispersion coefficient to small channels was undertaken. Tracer experiments were conducted in three different channels: (1) a concrete channel; (2) a semi-natural channel; and (3) a natural channel. Sodium chloride and suspended sediment were injected simultaneously in the channels. The sediment particle sizes ranged from <0.075 mm to 1 mm. Sodium chloride was treated as a conservative tracer (i.e., no losses or gains during transport), and the sodium chloride plumes were modeled first to obtain best-fit estimates for the longitudinal dispersion coefficients. Suspended sediment plumes were modeled subsequently using the best-fit longitudinal dispersion coefficients from the sodium chloride plumes with an additional settling rate parameter to account for the sediment loss. The 1D ADE was capable of reproducing the observed curves with ±50% relative error. The settling rate term was found to be essential to properly simulate the suspended sediment plumes. The commonly used formula for settling rate (settling velocity/depth) overestimated the loss of particles and it was not applicable to the observed data. Twenty-six predictive formulas for the longitudinal dispersion coefficient were evaluated on their ability to reproduce the observed plumes. None of the predictive formulas were able to predict the dispersion process in the small channels with less than ±50% error. The formulas from Sattar and Gharabaghi (2015) had the best performance overall. Findings from this thesis can serve as a guideline for engineers and scientists working with tracer data and water quality models in small streams.
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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 | 0.000 | 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".