Field Performance and Water Balance Predictions of Evapotranspirative Landfill Biocovers
Bibliographic record
Abstract
The present research aims to extend the application of Evapotranspirative (ET) covers to Canadian landfill biocovers and assess their performance under climatic conditions present in Canada. Seven large-scale lysimeters were constructed simulating a capillary barrier landfill biocover and monitored for water balance from May 2018 to May 2019. Two soil types (Topsoil and Compost mixture) and three types of vegetation (Native grass species, Alfalfa, and Japanese Millet) were used to investigate the most effective design. Rainfall simulations were carried out to assess the performance of vegetated and non-vegetated covers. Water balance predictions made using two codes (SEEP/W and HYDRUS) were compared to water balance data from lysimeters over the growing season. The rainfall simulation results suggested that the compost mixture was able to hold 40 % more moisture than topsoil, on average. Percolation as a percentage of rainfall (percolation percentage) was significantly lower for vegetated media compared to bare or poorly vegetated media. During the growing season, Alfalfa had the highest average ET rate, followed by Japanese Millet and Native grass species. Among soil, plant and meteorological factors, solar radiation, surface cover fraction, rooting depth and plant height had a significant effect on ET rates. The results suggested that as plants became established, the average percolation percentage decreased for all crop types. Annual percolation percentage was 13-14 % for lysimeters which were not subjected to rainfall simulations. Among lysimeters subjected to rainfall simulations, lysimeters with Japanese Millet transmitted the lowest amount of percolation (10 %-17 %), followed by Native Grass species and Alfalfa (23 %-28 %). Under the same vegetation coverage, lysimeters with compost mixture generally transmitted lower or equal percolation compared to lysimeters with topsoil. Modelling results from June 2018 to September 2018 (110 days) showed that predicted evapotranspiration was in better agreement with field results when the Penman-Monteith (PM) method was used instead of Penman-Wilson (PW). In general, soil water storage and percolation were overpredicted by both codes using the PM method and underpredicted using the PW method. Model limitations included predictions under high-intensity rainfall events, estimating canopy interception and considering preferential pathways associated with plant roots.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".