Bibliographic record
Abstract
Grass swales are vegetated open channels that collect and transport stormwater runoff.They are often used as an alternative to concrete gutters to transport runoff along streets due to their low cost.However, they also offer several advantages in stormwater quality management, especially in their ability to infiltrate runoff.This chapter describes another benefit of grass swales: their ability to trap particulates during low flows.A series of detailed laboratory tests were conducted to describe sediment transport processes for stormwater grass swales.Field verifications of these processes are also described in this chapter.As expected, runoff hydraulics, especially depth of flow, along with swale length, affect the transport of particulates of different sizes.Shallow flows (less than the grass height) provided consistently high removal rates, while deeper flows (and especially along with relatively low sediment concentrations) had poorer sediment trapping abilities.Obviously, long swales and large particle sizes are an effective combination, but the smallest particles are likely to be effectively transported along most swales.There appeared to be equilibrium concentrations for different particle sizes that were not further reduced, irrespective of swale length, likely associated with combinations of scour of Sediment Transport in Grass Swales material from the underlying soil or of previously trapped sediment, and the carrying capacity of the water. MethodologyThe Department of Civil and Environmental Engineering at the University of Alabama has been conducting research investigating the effectiveness of grass swales for stormwater sediment transport for several years.This research was initially supported by the Water Environment Research Foundation (WERF) (Johnson, et al. 2003) and more recently by the University Transportation Center of Alabama (UTCA).The aims of this research are to understand the effects of different variables affecting sediment transport in grass swales, especially considering different particle sizes, swale features, and flow conditions.Controlled tests were conducted using specially constructed indoor grass swales and test solutions having known concentrations of sediment with different particle sizes.The test solutions used particles of sieved sands (locally acquired) and commercially-sized silica (from U.S. Silica Co.).The indoor swales were adjusted for different slopes, and had three different grasses.Two series of indoor experiments were conducted.The first series were exploratory in nature to identify the most important variables affecting sediment transport, while the second series examined these variables in more detail and were used to develop a sediment transport model.The first series of tests examined grass type, slope, swale length, time since the beginning of the flow, flow rate, particle size, and sediment concentration.The second series of tests focused on fewer grass types, had less variability in sediment concentrations, and composited samples over the complete test period.The sediment transport processes described in this chapter were derived during the second series of experiments.The second series of indoor swale experiments included analyzing 108 samples for turbidity, total solids, total suspended solids, total dissolved solids, and particle size distribution.The results of the indoor swale experiments were verified during monitoring at an outdoor grass swale located adjacent to the Tuscaloosa (Alabama, U.S.A.) City Hall, during actual storm events.The outdoor swale tests included collecting and analyzing 69 samples during 13 storm events from August to December 2004.These samples were analyzed for turbidity, total solids, total suspended solids, total dissolved solids, and particle size distribution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".