Field Performance and Water Balance Predictions of Evapotranspirative Landfill Biocovers
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».