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Record W4392583755 · doi:10.5194/egusphere-egu24-7378

Critical channel runoff as direct trigger of debris flows in mountainous terrain.

2024· preprint· en· W4392583755 on OpenAlexaff
Markus Hrachowitz, L. S. Sklar, Roland Kaitna

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsConcordia University
Fundersnot available
KeywordsDebris flowSurface runoffDebrisTerrainChannel (broadcasting)Environmental scienceHydrology (agriculture)GeologyGeographyComputer scienceGeotechnical engineeringCartographyOceanographyTelecommunicationsEcology

Abstract

fetched live from OpenAlex

As a natural hazard in mountainous terrain, debris flows cause considerable disruptions, human casualties and economic damage in many regions world-wide. However, the spatially localized nature of debris flows together with the lack of data at sufficient temporal and spatial resolutions make the triggering processes difficult to describe. As a result, debris flows are problematic to predict. Effective regional and local early warning systems, built on both process-based or statistical models, have therefore so far remained elusive. Even more, common statistical models, such as precipitation-intensity threshold models, rely on precipitation. As debris flows are essentially in-channel processes, precipitation is an indirect predictor and proxy for in-channel processes. As such it is not surprising that precipitation has limited predictive power. In spite of recent progress, general and detailed descriptions of in-channel processes that control debris flow triggering only start to emerge. Most generally, sediment supply and channel flow magnitudes can be considered major direct controls on debris flow occurence. As both are difficult to observe, they have so far not been systematically exploited and quantitatively described for their role as debris flow triggers. Based on 20-year records of hydro-climatic data, several dozens of well documented debris flow events in three contrasting head-water catchments in the Central Alps and a semi-distributed, process-based hydrological model, the objectives of our analysis are to (1) quantify the critical channel runoff magnitudes that have triggered past debris flows and to establish whether characteristic magnitudes can be found as a function of topography, soils, geology and other factors, (2) identify the relevance of snow melt vs. rainfall for the generation of debris flow triggering critical channel runoff, and (3) to test whether modelled critical channel runoff has higher power to predict debris flows than standard precipitation-intensity models.Overall, we have found that indeed, relatively well-defined minimum critical channel flows as lower limits above which debris flows occur feature each of the three study catchments. It was also found that the general magnitudes are highly site specific. In spite of that, no obvious relation between the average critical flow magnitudes and landscape characteristics, such as local terrain or channel slopes, vegetation cover, soil type or geology at the three sites could be identified. In general, it was found that flow peaks, generated by short-duration, high-intensity rainfall events, mostly during summer, dominated the debris flow trigger dynamics at the study sites. In addition, several instances when debris flows were triggered by flow peaks of similar magnitudes but generated by high-intensity snow melt in combination with rain-on-snow were observed, highlighting the importance of quantifying liquid water input dynamics instead of bulk precipitation as system input that causally leads to the occurrence of debris flows. Intrinsically accounting not only for this distinction but also additional effects by evaporation, modelled channel flow magnitudes were found to be better predictors of debris flows, with respect to both, higher rates of true positives (correctly predicted debris flows) and lower rates of false positives (predicted but not occurred in reality), than traditional precipitation thresholds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.257
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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