Combined hazard of typhoon-generated meteorological tsunamis and storm surges along the coast of Japan
Bibliographic record
Abstract
Abstract Two hazardous typhoons, Lionrock (August 2016) and Jebi (September 2018), destructively affected the coast of Japan and produced extreme sea level variations. The results of field surveys in the impacted regions showed that multiple deaths and extensive floods were caused by the combined effect of low-frequency sea level raise (storm surges) and intensive high-frequency (HF) tsunami-like waves (meteotsunamis). The data from ten tide gauges for the 2016 event and eight gauges for the 2018 event were used to examine the properties of the observed sea levels, to estimate the relative contribution of the two sea level components and to evaluate their statistical characteristics (maximum wave heights, amplitudes and periods of individual components, etc.). For the 2016 event, we found that the surge heights were from 12 to 35 cm and that the mean contribution of surges into the total observed sea level heights was ~ 39%; the meteotsunami amplitudes were from 22 to 92 cm, and they contributed 61% of the total height. For the 2018 event, storm surges were significantly stronger, from 46 to 170 cm, while HF amplitudes were from 38 to 130 cm; their relative inputs were 67% and 33%, respectively. Combined, they formed total flood heights of up to 120 cm (2016 event) and 288 cm (2018 event). Previously, the contribution of storm seiches (meteotsunamis) in coastal floods had been underestimated, but results of the present study demonstrate that they can play the principal role. What is even more important, they produce devastating currents: according to our estimates, current speeds were up to 3 knots (1.5 m/s) during the Lionrock event and more than 5 knots (2.6 m/s) during Jebi; these strong currents appear to be the main reason for the resulting damage of coastal infrastructure. The most important characteristic of the recorded meteotsunamis is their trough-to-crest maximum height. During the 2016 event, these heights at three stations were > 1 m: 171 cm at Erimo, 109 cm at Hachijojima and 102 cm at Ayukawa. The 2018 event was stronger; maximum meteotsunami wave heights were 257 cm at Gobo, 138 cm at Kushimoto, 137 cm at Kumano and 128 cm at Murotomisaki. The 2018 Gobo height of 257 cm is much larger than historical non-seismic seiche maxima for the Pacific coast of Japan (140–169 cm) estimated by Nakano and Unoki (1962) for the period of 1930–1956.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".