The significance of channel recharge rates for estimating debris‐flow magnitude and frequency
Bibliographic record
Abstract
Abstract The quantification of debris‐flow hazard requires estimates of debris‐flow frequency and magnitude. Several methods have been proposed to determine the probable volume of future debris flows from a given basin, but most have neglected to account for debris recharge rates over time, which may lead to underestimation of debris‐flow volumes in basins with rare debris flows. This paper deals with the determination of debris recharge rates in debris‐flow channels based on knowledge of debris storage and the elapsed time since the last debris flow. Data are obtained from coastal British Columbia and a relation is obtained across a sample of basins with similar terrain and climatic conditions. For Rennell Sound on the west coast of the Queen Charlotte Islands, the power‐law relation for area‐normalized recharge rate, Rt, versus elapsed time, te was Rt = 0·23te−0·58 with an explained variance of 75 per cent. A difference in recharge rates may exist between creeks in logged and unlogged forested terrain. The power function for undisturbed terrain was Rt = 0·20te−0·49, while the function for logged areas was Rt = 0·30te−0·77. This result suggests that for the same elapsed time since the last debris flow, clearcut gullies tend to recharge at a slower rate than creeks in old growth forest. This finding requires verification, particularly for longer elapsed times since debris flow, but would have important implications for forest resource management in steep coastal terrain. This study demonstrates that commonly used encounter probability equations are inappropriate for recharge‐limited debris flow channels. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".