Efficient coastal inundation early-warning system for low-lying atolls, dealing with lagoon and ocean side inundation in Tarawa, Kiribati
Why this work is in the frame
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Bibliographic record
Abstract
Tarawa is a low-lying atoll in the Gilbert Island group, capital of the Republic of Kiribati and home of nearly 70.000 inhabitants. With limited land area, rapid population growth and urbanization, strong interannual sea level variability induced by ENSO and sea level rise, Tarawa is highly vulnerable to coastal flooding. In this context, Early Warning Systems are a proven cost-effective climate adaptation measure to strengthen community resilience. In virtually enclosed atolls, the water level experienced at the shore is compounded by tides, sea level anomaly, storm surge and the contribution of waves. While wave setup and runup, are the primary components driving inundation along the ocean-facing shorelines, sea level anomaly, wind setup and wave pumping through the atoll rim contributes more inside lagoons. In this paper we present an efficient process-based approach to forecast flooding events along both, the ocean and lagoon coasts of atoll islands. With the intention of being highly scalable to other island countries, the system has been designed as a lightweight and accurate tool, that provides actionable and user-friendly water level predictions 7 days in advance. Publicly available global forecast products are ingested by a high-resolution wave model and tailor-made metamodels to translate ocean forcings to water levels at the shore. In absence of a comprehensive topography dataset, extreme value distributions of 27-year hourly water levels were evaluated every 500 m along the coast to define and communicate different levels of warnings according to the recurrence interval of the forecasted event. The long-term wave climate, nearshore wave transformation, and water levels produced under this work increase Tarawa's risk knowledge and support informed investments and future development strategies. While this system will significantly enhance ocean services in Kiribati, improving baseline data still remains a critical need for government and communities to support informed decision making to better cope with increased coastal hazards.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it