Impacts of Vegetation and Growing Media on Evapotranspiration in Bioretention Systems
Bibliographic record
Abstract
Although bioretention systems have known benefits in managing urban stormwater, there is still a gap of knowledge about the role of evapotranspiration (ET) in these systems. To date, very few works have studied the effects of different parameters on ET simultaneously over an extended period, especially in the field. Therefore, this work aimed to investigate the role of design variables (including growing media and vegetation) and climatic parameters on ET in bioretention systems. To this end, twenty-four bioretention mesocosms constructed using three media types (i.e., media 40, media 70, and clay-loam mixed with wood chips) and planted with three vegetation types (i.e., herbaceous mixes, woody mixes, and turfgrass as control) were monitored during three growing seasons in 2018 – 2020. The set-up of the mesocosms allowed examining the roles of these variables along with their interactions on ET. The results confirmed the effect of media and vegetation, apart from the climatic variables, on ET at the surface and deep layers of the mesocosms. Still, their impacts were more prominent at the surface layer. Among the investigated media and vegetation types, the media 70 and the woody vegetation appeared to outperform their counterparts in enhancing ET during the study period. The findings also demonstrated that the effects of media and vegetation on ET in the mesocosms varied with time. The influence of design variables, particularly the vegetation, became more prominent over time. In addition, the impact of media-vegetation interactions on ET was identified, and thus its consideration is necessary. These results suggest the need for optimizing bioretention systems with regard to the design variables, whose roles in ET are time- and depth-variant, to promote ET and, in turn, the bioretention performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".