Probabilistic Numerical Modeling of Compound Flooding Caused by Tropical Storm Matthew Over a Data‐Scarce Coastal Environment
Bibliographic record
Abstract
Abstract The passage of a tropical storm, as the main driver of storm surge and high waves in many coastal regions, can also generate heavy rainfall and cause river overflow. The resulting combination of riverine, pluvial, and coastal flood hazard can result in catastrophic losses particularly in densely populated coastal environments. In this study, we characterize compound flooding caused by Tropical Storm Matthew and assess the significance and associated uncertainties of multiple contributing factors over a data‐scarce coastal region. A hydrological model combined with a simplified two‐dimensional hydrodynamic model are set up and validated to investigate the compounding effects of storm tide, wave runup, rainfall, and river overflow at the southern coast of Saint Lucia in the Caribbean Sea. Pléiades‐1 and Sentinel‐1 satellite imageries are used to determine the flood‐impacted areas. The analyses are performed based on deterministic and probabilistic approaches and the effects of uncertain boundary conditions and model parameters are investigated. Results show that the individual analysis of flood hazards, in isolation, can lead to substantial underestimation of flood risks. Heavy rainfall and wave runup are the most significant contributors to compound flooding in Saint Lucia. In addition, the interactions between seawater and streamflow can exacerbate riverine flood hazards particularly upstream of the river mouth. Communities in western Vieux Fort, and the Hewanorra International Airport, have high exposure to compound flooding, which is projected to intensify under climate change.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".