Understanding the risk of unpaved roads on drinking water treatability by assessing sediment erosion across Canada
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".