Extreme Mediterranean rainfall impact on sedimentary routing systems: what can we learn from Storm Alex using in situ detrital 10Be?
Bibliographic record
Abstract
Understanding how extreme meteorological events influence sediment transport is critical for predicting landscape evolution under a changing climate. Detrital cosmogenic 10 Be can provide insights into sediment dynamics following extreme rainfall, but high-resolution datasets tracking 10 Be variations before and after a storm, alongside long-term records, remain rare. The Var catchment (French Southern Alps) presents a unique case study, as its 10 Be signal was well-documented before the October 2020 Storm Alex (>500 mm of rainfall/24 h), which triggered flash floods, mobilized large sediment volumes, and formed a 10 km-long sediment plume in the Mediterranean Sea. We compare 10 Be concentrations in river sediments collected pre-storm (2016–2018), and at +7 days, +21 days, +4 months, and +7 months post-storm. We also use a historical offshore sample and contextualize these results with a 75 ka-long 10 Be record from deep-sea sediment cores. At the Var outlet, 10 Be concentrations initially increased by ∼25 % at +7 and +21 days, attributed to the mobilization of 10 Be-rich sediments from the upstream Var and Tinée sub-catchments. Concentrations returned to pre-storm levels within four months, primarily due to dilution with 10 Be-poor sediments from the Vésubie sub-catchment fluvioglacial terraces. While short-term 10 Be fluctuations at the Var outlet reflect complex sediment sourcing, our comparison with the 0–75 ka record confirms that major glaciation events and potential anthropic influences remain distinguishable, demonstrating that 10 Be is a robust proxy of denudation changes, even when extreme events are involved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".