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Coastal Flood Mapping with Two Approaches Based on Observations at Furadouro, Northern Portugal.

Authors :
Carneiro-Barros, Jose E.
Plomaritis, Theocharis A.
Fazeres-Ferradosa, Tiago
Rosa-Santos, Paulo
Taveira-Pinto, Francisco
Source :
Remote Sensing. Nov2023, Vol. 15 Issue 21, p5215. 17p.
Publication Year :
2023

Abstract

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD); in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the 'Tilted Bathtub Approach' (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30-year-long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea states according to different return periods and also for performing a sensitivity test for established empirical formulas to estimate wave run-up in this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment, planning, and understanding of coastal responses under extreme weather conditions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
21
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
173568280
Full Text :
https://doi.org/10.3390/rs15215215