Publicação
Can global datasets be used to predict storm-induced coastal erosion?
| datacite.subject.sdg | 13:Ação Climática | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| datacite.subject.sdg | 14:Proteger a Vida Marinha | |
| dc.contributor.author | Fanti, Valeria | |
| dc.contributor.author | Ferreira, Óscar | |
| dc.contributor.author | Loureiro, Carlos | |
| dc.date.accessioned | 2026-07-24T09:46:22Z | |
| dc.date.available | 2026-07-24T09:46:22Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | The scarcity of high-resolution in situ data hampers effective storm impact assessment and the development of early warning systems for coastal bar riers worldwide. This study introduces a novel methodology utilising a SWAN XBeach modelling chain, leveraging global hydrodynamic (WAVERYS and GTSM) and topo-bathymetric (TanDEM-X and ETOPO2022) datasets to perform first-order, globally applicable storm impact assessments for barrier coasts. The approach is validated with high-resolution data from Duck, North Carolina, using pre- and post-storm topographic LiDAR, recorded wave conditions and water levels. The results indicate that global datasets can reasonably reproduce barrier ero sion patterns, despite underestimation of total water levels and dune morphology due to the coarse resolution and inaccuracies in the GTSM and TanDEM-X data. The simulations effectively capture dune retreat and erosion trends, aligning with measured data, but inconsistencies are observed in morphological changes for the upper beach face. These discrepancies are likely due to differences in intertidal elevation and beach face steepness between the LiDAR data and global models at Duck. Additionally, the approach does not account for short-scale (~100 m) longshore variability, which can contribute to deviations in modelled results. This study demonstrates the potential of global datasets for storm impact mod elling, providing a first-order assessment with relevant information about storm induced erosion, which can be particularly useful in areas lacking high resolution data. However, it also highlights the need for further validation across diverse sites to enhance reliability and address methodological constraints. | eng |
| dc.identifier.doi | 10.1007/978-3-032-15473-6_29 | |
| dc.identifier.eissn | 2211-0585 | |
| dc.identifier.isbn | 9783032154729 | |
| dc.identifier.isbn | 9783032154736 | |
| dc.identifier.issn | 2211-0577 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/29306 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Coastal Research Library | |
| dc.relation.ispartof | Coastal Dynamics 2025 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Coastal erosion | |
| dc.subject | Storm impact assessment | |
| dc.subject | Barrier Islands | |
| dc.subject | XBeach modelling | |
| dc.subject | Global datasets | |
| dc.title | Can global datasets be used to predict storm-induced coastal erosion? | eng |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 190 | |
| oaire.citation.startPage | 185 | |
| oaire.citation.title | Coastal Dynamics 2025 | |
| oaire.citation.volume | 1 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Fanti | |
| person.familyName | Ferreira | |
| person.familyName | Loureiro | |
| person.givenName | Valeria | |
| person.givenName | Óscar | |
| person.givenName | Carlos | |
| person.identifier | 453264 | |
| person.identifier.ciencia-id | 1F1C-DF44-94C9 | |
| person.identifier.ciencia-id | 011D-31BD-C6B6 | |
| person.identifier.orcid | 0000-0001-8554-4282 | |
| person.identifier.orcid | 0000-0001-9975-0036 | |
| person.identifier.orcid | 0000-0003-3117-3492 | |
| person.identifier.rid | U-9863-2018 | |
| person.identifier.scopus-author-id | 23667861200 | |
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