Regional-scale coastal hazard modelling using a univariate framework for extreme total water levels including sea-level rise
A case study in Portugal
Bibliographic Data
| ID | 22028950 |
|---|---|
| Authors | Jose Eduardo Carneiro Barros (0000-0002-6458-8996, Universidade do Porto, corresponding author), Elaine Baroni de Oliveira (0000-0001-8902-8867, Universidade do Vale do Rio dos Sinos), Daniel Clemente (Universidade do Porto), Xavier Romão (0000-0002-2372-6440, Universidade do Porto), Tiago Fazeres-Ferradosa (0000-0002-8549-3320, Universidade do Porto), Paulo Rosa-Santos (0000-0002-3768-3314, Universidade do Porto), Francisco Taveira-Pinto (0000-0003-4337-8428, Universidade do Porto) |
| Year | 2026 |
| Volume | 143 |
| Pages | 106272 |
| Publication date | 2026-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2026.106272 |
| OpenAlex | W7165945401 |
| Language | EN |
| References cited | 87 |
Coastal flooding poses significant threats to coastal communities, typically characterized as low-probability, high-impact events that can be effectively analysed using Extreme Value Theory. This study applies a systematic univariate methodology for assessing the combined influence of coastal flooding-related variables along the Northern Portuguese Coast, namely those contributing towards the Total Water Level (TWL). The robust and dynamic Peaks-Over-Threshold (POT) selection criterion is employed and matched against the alternative Block Maxima (BM) approach, both used for sampling extreme events. The Generalized Pareto distribution combined with POT sampling proved quite effective, while the ExGaussian distribution, traditionally less common in coastal flooding studies, emerged as a promising alternative to the Generalized Extreme Value distribution when using the BM approach. Sea-level rise (SLR) scenarios were then quantified and integrated into the extreme value analysis, namely by overlapping with the preceding TWLs, across 400 beach profiles and spanning 120 km of coastline. Results underscore the significant increase in the frequency of future extreme flooding events, even under the most conservative SLR scenario. The analysis also highlights the physical constraints inherently limiting the maximum attainable TWL, thus providing realistic caps on future extreme event projections. Overall, the proposed univariate framework balances simplicity with accuracy, offering clear advantages over multivariate methods by reducing computational complexity without sacrificing robustness. Moreover, by estimating, with relevant accuracy, both the local extreme wave conditions and climate change-driven shifts in coastal flooding hazards, the findings make a significant contribution to coastal hazard management and climate change adaptation planning
Environmental hazard · Hazard · Hazard analysis · Univariate · Coastal and Marine Dynamics · Geological and Geophysical Studies Worldwide · Hydrology and Drought Analysis
An Introduction to Statistical Modeling of Extreme Values
Doubling of coastal flooding frequency within decades due to sea-level rise
The complexity of the coastal zone
Assessment of tangible coastal inundation damage related to critical infrastructure and buildings
Coastal flood susceptibility assessment along the Northern coast of Portugal
Early warning services for disaster risk reduction in Brazil
A coastal flooding database from 1980 to 2018 for the continental Portuguese coastal zone
Future sea level rise dominates changes in worst case extreme sea levels along the global coastline by 2100
| Highly cited | No |
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