Compounded influence of extreme coastal water level and subsidence on coastal flooding from satellite showcased at Saint-Louis (Senegal, West Africa)
Bibliographic record
Abstract
In the face of rising in sea level due to climate change, the occurrence of extreme events such as storms is increasingly affecting coastal areas, particularly low-lying coasts. Knowledge of these phenomena is an important factor in mitigating the risk of coastal flooding and protecting coastal communities. The main objective of this study is to contribute to the understanding of the joint effect of changes in coastal extreme events and topographic subsidence on coastal flooding in Saint-Louis. As part of this process, we have quantified total water levels at the coast by using the regional sea level variation, ocean tide, surge, wind sea and swell waves data over the 1996–2021 period. All these datasets have been analyzed by Mann-Kendall statistical trend, the synthetic aperture radar (InSAR) interferometry technique, and the ‘zero side rule’ bathtub model. The results reveal a monotonic trend in total water levels on the Langue de Barbarie with the order of 0.049 m/yr the topographic subsidence varies from −6.4 to −0.4 mm/year. The spatialization of the flood wave reveals that the three spatial entities of Saint-Louis are extremely vulnerable to coastal flooding, but the extension of the flood wave is unevenly distributed at spatial scale. A comparison between the trend in maximum subsidence (-6.4 m/yr) and that in extreme mean water levels (0.049 m/yr) shows that the maximum trend in subsidence represents 13.06 % of the maximum trend in extreme coastal water level. This study enabled us to understand the influence of subsidence on flooding in Saint-Louis.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".