A Multi-Faceted Debris-Flood Hazard Assessment for Cougar Creek, Alberta, Canada
Bibliographic record
Abstract
A destructive debris flood occurred between 19 and 21 June 2013 on Cougar Creek, located in Canmore, Alberta. Cougar Creek fan is likely the most densely developed alluvial fan in Canada. While no lives were lost, the event resulted in approximately $40 M of damage and closed both the Trans-Canada Highway (Highway 1) and the Canadian Pacific Railway line for a period of several days. The debris flood triggered a comprehensive hazard assessment which is the focus of this paper. Debris-flood frequencies and magnitudes are determined by combining several quantitative methods including photogrammetry, dendrochronology, radiometric dating, test pit logging, empirical relationships between rainfall volumes and sediment volumes, and landslide dam outburst flood modeling. The data analysis suggests that three distinct process types act in the watershed. The most frequent process is normal or “clearwater” floods. Less frequent but more damaging are debris floods during which excessive amounts of bedload are transported on the fan, typically associated with rapid and extensive bank erosion and channel infilling and widening. The third and most destructive process is interpreted to be landslide dam outbreak floods. This event type is estimated to occur at return periods exceeding 300 years. Using a cumulative magnitude frequency technique, the data for conventional debris floods were plotted up to the 100–300s year return period. A peak-over-threshold approach was used for landslide dam outbreak floods occurring at return periods exceeding 300 years, as not all such events were identified during test trenching. Hydrographs for 6 return period classes were approximated by using the estimated peak discharges and fitting the hydrograph shape to integrate to the debris flood volumes as determined from the frequency-magnitude relationship. The fan volume was calculated and compared with the integrated frequency-magnitude curve to check of the validity of the latter. A reasonable match was accomplished, verifying the overall relationship. The findings from this work were later used as input to a risk assessment seeking to quantify risk to loss of life and economic losses. The risk assessment then formed the basis for design of debris-flood mitigation structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".