Anatomy of a Flash Flood in a Hyperarid Environment: From Atmospheric Masses to Sediment Dispersal in the Sea
Bibliographic record
Abstract
Flash floods in rivers near hyper-arid coastlines impact both land and marine environments, from recharging groundwater and supporting desert ecosystems to affecting marine water quality, organisms, and substrates. Few studies, however, have followed these events from atmospheric origins to marine effects. This study tracked a desert flash flood in October 2016 in Eilat, capturing stages from atmospheric conditions to sediment distribution at sea. Observations included satellite data, meteorological reports, floodwater discharge, and sediment levels from the Kinnet Canal outlet, alongside offshore turbidity and salinity data. Our findings indicate that a weakened polar vortex amplified a Rossby wave, triggering convective instability over the Eastern Mediterranean and northern Red Sea. In Eilat (mean annual rainfall = 27 mm, SD = 21 mm), 128 % of the average fell within hours, with the flood reaching the sea approximately 50 h later and lasting 27 h. Around 25 000 t of sediment were discharged, causing offshore salinity drops (up to 1.75 ‰ below the seawater background) and fluctuations of suspended sediment concentrations due to varying flow rates. In turn, particle dispersal in the sea switched several times between hypopycnal (surface) and hyperpycnal (bottom) flows. These findings link the different stages of the flood and their cascading effects from air masses to sedimentary processes in the sea.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".