Assessing Debris Flow Susceptibility in Deglaciated Alpine Catchments: A Novel Approach Integrating Flow-path Connectivity and Sediment Availability
Bibliographic record
Abstract
Debris flows pose significant natural hazards in mountainous regions globally, with the potential to cause substantial damage to villages and infrastructure in lowland areas. To effectively mitigate these hazards, it is essential to identify catchments prone to delivering debris flows to fan areas. Topographic metrics such as fan slope and catchment Melton number are useful at a regional scale but in recently deglaciated alpine landscapes do not explain high variability in debris flow frequency among neighboring catchments. Here we assess debris flow susceptibility by quantifying the spatial extent of sediment cover across upstream catchment areas and the connectivity of potential debris flow source areas to fans at catchment outlets.The Pitztal Valley in the Austrian Alps serves as the study site, benefiting from elevation data (digital terrain model) and historical debris flow event data since the 1950s for 28 catchments, along with sediment cover maps for 18 catchments. To assess the likelihood that debris flows originating within sediment covered areas could reach the fan, we calculated the minimum mean slope angle along every flow path to the fan apex. Source areas with minimum angles lower than a dynamic friction angle are assumed to be disconnected from the fan apex because potential debris flows would come to rest and form coarse deposits upstream of the fan. This concept is utilized to calculate the fractional connectivity for sediment areas in each catchment as a function of dynamic friction angle. Rather than assume a single friction angle, we compare catchments based on the angle corresponding to a connectivity rate of 50% for the sediment covered areas (referred to as the 50% connectivity angle).We find a highly significant positive exponential correlation between the 50% connectivity angle and the historical debris flow frequency (in units of debris flow events per square kilometer of catchment area) using data from the 18 catchments with sediment cover maps. Validation is performed in 10 additional catchments where we identified sediment covered areas using a new algorithm that can distinguish bare bedrock from sediment deposits from the local topographic roughness. The observed debris flow event frequencies align closely with or fall within the confidence bounds predicted by the 50% connectivity angles, confirming that the combined evaluation of catchment connectivity and sediment availability successfully explains debris flow frequency in this landscape.Lastly, the results are compared to models employing previously published metrics of connectivity and debris flow susceptibility, providing insights into the contribution and efficacy of this new approach.
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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 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| 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".