Spatial Analysis of Extreme Coastal Water Levels and Dominant Forcing Factors Along the Senegalese Coast
Bibliographic record
Abstract
Coastal flooding risk is intensifying under climate change, especially along the low sandy Senegalese coastline. This study explores the spatiotemporal variability of extreme coastal water levels (ECWL) from 1993 to 2023 by combining ERA5 reanalysis (waves, wind, pressure), tide gauge and meteorological data, and applying a generalized Pareto distribution (GPD) to estimate the 99th percentile and return levels for 50 and 100 year events. The analysis of the upper 1% of ECWLs reveals significant spatial heterogeneity: 99th percentile values exceed 1.2 m in the Dakar region (Yoff, Ouakam, Ngor) and around Saint-Louis/Langue de Barbarie, with 95% confidence intervals ranging from approximately 1.15 m to 1.30 m, while Casamance and the Saloum Delta exhibit much lower extremes (0.8–1.0 m). For return periods, ECWLs vary between 1.6 m and 2.3 m, with the 100 year return level (T100) exceeding 2.25 m in Dakar, above 2.0 m in Saint-Louis, and intermediate values (1.5–1.9 m) along the Petite Côte (Mbour–Toubab Dialaw) and in the Saloum Delta. The 50 year return level (T50) follows a similar spatial pattern but is 5–10 cm lower than T100 in the most exposed areas. Sensitivity analysis shows that ECWLs are primarily controlled by astronomical tide along much of the coast, whereas wave runup dominates in the southern estuarine zones (Saloum, Casamance, Mbour). Trend analysis using the Mann–Kendall test reveals a latitudinal gradient: stronger positive slopes in the south, weaker trends in central and northern sections, but all p values lie between 0.1 and 0.4, meaning none of the trends reach conventional significance. These findings point to a potential intensification of extreme water levels in socio-economically critical areas (Dakar, Saint-Louis, Mbour) but should be interpreted with caution given the lack of robust statistical significance. The results provide a quantitative basis for coastal risk management in light of projected sea level rise.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".