Debris‐Flood Hazard Assessments in Steep Streams
Bibliographic record
Abstract
Abstract Debris floods most commonly occur in steep mountain channels and on their alluvial fans but can also occur on small gravel bed rivers with watershed areas up to several hundred square kilometers. This became obvious during July 2021 and November 2021 debris floods in northwestern Germany and southwestern British Columbia, Canada. We subdivide debris floods into three categories: those triggered by a supra‐critical bed shear stress ratio, form by dilution from debris flows, and resulting from outbreak floods. This trichotomy challenges traditional hazard assessments; debris floods classify as a fluvial process, yet their destructive mechanics are difficult to characterize. Key hazards interact spatially and temporally in debris floods: inundation, scour, sediment transport and deposition and bank erosion. We describe approaches to quantify hazards by systematically accounting for these processes and introduce a novel approach for hazard quantification and mapping in which flow velocity, depth, and presumed fluid density are combined with the annual event frequency for all event scenarios. The derivative “composite hazard maps” are equally valid for debris floods and debris flows. Isolines of bank erosion based on a probabilistic analysis of a physically based model are added to capture the potential of debris floods to abruptly widen their channels. Substantial challenges remain, specifically in the reliable prediction of sediment transport and progressive bank erosion. Our intention is to homogenize debris‐flood hazard assessments and especially mapping methodologies. This could allow for systematic integration with landuse policies assuring consistency among approaches executed by practitioners acting in this field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".