Extreme river flood exposes latent erosion risk
Bibliographic record
Abstract
Abstract Climate change is expected to increase the frequency and magnitude of river floods 1 . Floods not only cause damage by inundation and loss of life 2,3 but also jeopardize infrastructure because of bank failure and riverbed erosion processes that are poorly understood. Common flood safety programmes include dyke reinforcement and river widening 4–9 . The 2021 flood in the Meuse Basin caused 43 fatalities and billions of dollars of damage to infrastructure 10 . Here, on the basis of analysis of the Meuse flood, we show how uneven widening of the river and heterogeneity of sediment deposits under the river can cause massive erosion. A recent flood safety programme widened the river 11 , but created bottlenecks where widening was either prevented by infrastructure or not yet implemented. Riverbed erosion was exacerbated by tectonic uplift that had produced a thin top gravel layer above fine-grained sediment. Greatly enhanced flow velocities produced underwater dunes with troughs that broke through the gravel armour in the bottlenecks, exposing easily erodible sands, resulting in extreme scour holes, one more than 15 m deep. Our investigation highlights the challenges of re-engineering rivers in the face of climate change, increased flood risks and competition for river widening space, and calls for a better understanding of the subsurface.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".