Evapotranspiration from extensive green roofs: influence of climatological conditions, vegetation type, and substrate depth
Bibliographic record
Abstract
Green roofs are gaining popularity worldwide as a low impact development tool to mitigate increasing stormwater runoff within dense urban areas. Evapotranspiration (ET) is the key hydrologic process governing the capacity of a green roof to retain rainfall as it regenerates available water storage space in the green roof substrate (soil) between rainfall events. To date, there are limited data on how the interaction between different climatological conditions and design parameters (e.g., vegetation type, substrate depth) affect ET rates. This currently limits the ability to optimize green roof design for stormwater management. In this field study, the impact of climatological conditions, vegetation type, and substrate depth on ET rates were evaluated from experimental, modular extensive green roofs installed in three climate regions in Canada: Calgary AB (Prairies), London ON (Great Lakes/ St. Lawrence), and Halifax NS (Atlantic/ Maritime). Daily ET rates and cumulative ET over the field season were calculated from daily (n = 40) and continuous (n = 2 to n = 4) module weight data recorded from May to September in 2013 and 2014. The modular set-up of the green roof at all sites consisted of two module depth treatments (10 cm and 15 cm substrate depth), substrate only treatments (no vegetation), and four vegetation treatments (monoculture treatments of Sedum spurium, Sporobolus heterolepis, and Aquilegia canadensis, and a mixed species treatment consisting of the three aforementioned species). The plant coverage and root mass distribution were characterized for all vegetation treatments. The percentage of cumulative rainfall returned to the atmosphere by ET over the 2013 and 2014 field seasons was greater for Calgary (73%) and London (67%) compared with Halifax (33%). ET rates in Calgary and London were found to be limited by the available moisture in the substrate, whereas the results suggested that the other climatological variables or atmospheric forcing rather than available moisture may have been the limiting factor controlling ET rates in Halifax. Data revealed that green roofs with only Sedum spurium or a mixture of Sedum spurium, Sporobolus heterolepis, and Aquielgia canadensis had higher ET rates, and thus will be able to restore the retention capacity of the green roof substrate faster than a green roof with no vegetation, Sporobolus heterolepis or Aquilegia canadensis. The optimum substrate depth differed among vegetation types and study site. To optimize the hydrologic performance of green roofs (i.e., retention capacity), this study found that plant characteristics, such as plant coverage and root mass distribution, should be considered when selecting vegetation type and substrate depth. This study provided valuable insight on the sensitivity of ET rates to climatological conditions and green roof design parameters (i.e., vegetation type and substrate depth), with the study findings needed to make informed decisions on the design and optimization of the hydrologic benefits for green roofs installed in different climatological conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".