Understanding the risk of unpaved roads on drinking water treatability by assessing sediment erosion across Canada
Notice bibliographique
Résumé
Forest harvesting, wildfire suppression, energy resource exploration, and recreation all require unpaved roads. As a result, many roads in drinking watersheds are potential sources of fine (<63 µm) sediment. Erosion of fine sediment threatens drinking water treatability as can be a source of increased nutrients and sediment, creating issues in reservoirs and treatment infrastructure. However, there is a lack of research regarding sediment yields and erosion models on unpaved roads in Canada. The objectives of this study were 1) to understand the variability and predominant factors that contribute to increased road erosion risk in Canadian drinking watersheds, 2) evaluate the Forest and Range Evaluation Program (FREP) used to categorize risk, 3) evaluate erosion and runoff dynamics from representative unpaved roads and trails within the Ghost and Elbow River Watershed, near Calgary, Alberta, and 4) to validate the runoff and sediment production predictions of the Road Erosion and Delivery Index (READI) model using UAV data and rainfall simulations on the representative road segments. The first two objectives were addressed in chapter 2 with 107 site surveys and 22 small-plot (1.5m 2 ) rainfall simulations. Unpaved road erosion risk was influenced by road surface conditions, road slope, and traffic. Furthermore, traffic and poor road surface conditions increased fine sediment yields. Risk was found to be highest in the Montane Cordillera which had poor road surfacing conditions, steep slopes, and high traffic. Rainfall simulations and the FREP model could not be directly compared because of sediment yield units, but when plot sediment yields were ranked from lest to highest sediment there was a positive linear relationship. The final 2 objectives were addressed in chapter 3 with 6 site surveys, 6 large (60-150m 2 ) and small rainfall (1.5m 2 ) simulations, and 5 UAV flights. Site surveys gave insight to road 3 construction, road sweep samples provided details on available sediment, runoff and erosion rates were assessed from the rainfall simulations, and UAV flights produced digital surface models that gave an understanding to road roughness. Rainfall simulations showed that large and small plots preferentially (>70%) eroded fine particles. The rainfall simulations erosion rates followed three patterns: a steady-state of erosion, an increasing rate of erosion, and a decreasing rate of erosion. Similarly, runoff followed three patterns; a gradual increase to a steady state, increase with no steady state, and a steady state throughout the simulation. Runoff started at different time intervals based on scale; large-scale experiments started between 8-16 minutes and small simulations started between 2-4 minutes. Lastly, the READI model did not accurately predict time-to-concentrations or sediment yields compared to rainfall simulations, expect when site specific roughness values from digital surface models and erosivity (K) values were applied. The assessment of road erosion is valuable for understanding the impacts of road management practices on water quality. In Canada, sediment yields predominantly consists of particles ≤ 63 µm which poses a larger threat to drinking water treatability. Results showed fine sediment yields are related to traffic volume and road surface conditions. The Forest and Range Evaluation Program (FREP) and Road Erosion and Drainage Index (READI) models are tools to understand road erosion risk. These models assist in assessing sediment yields, providing useful insights for road managers. The appropriate method depends on the objectives and the available information to effectively address road erosion challenges.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».