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Blind testing of shoreline evolution models

dc.rights.licenseopenen_US
dc.contributor.authorMONTAÑO, Jennifer
dc.contributor.authorCOCO, Giovanni
dc.contributor.authorANTOLÍNEZ, Jose
dc.contributor.authorBEUZEN, Tomas
dc.contributor.authorBRYAN, Karin
dc.contributor.authorCAGIGAL, Laura
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorCASTELLE, Bruno
hal.structure.identifierSchool of Biological and Marine Science [SBMS]
dc.contributor.authorDAVIDSON, Mark
dc.contributor.authorGOLDSTEIN, Evan
dc.contributor.authorIBACETA, Raimundo
hal.structure.identifierBureau de Recherches Géologiques et Minières [BRGM]
dc.contributor.authorIDIER, Déborah
dc.contributor.authorLUDKA, Bonnie
dc.contributor.authorMASOUD-ANSARI, Sina
hal.structure.identifierMarketing Department
dc.contributor.authorMÉNDEZ, Fernando
hal.structure.identifierDivision of Earth and Ocean Sciences, Nicholas School of the Environment and Earth Sciences, Center for Nonlinear and Complex Systems
dc.contributor.authorMURRAY, A. Brad
dc.contributor.authorPLANT, Nathaniel
dc.contributor.authorRATLIFF, Katherine
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorROBINET, Arthur
dc.contributor.authorRUEDA, Ana
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorSENECHAL, Nadia
IDREF: 077248430
dc.contributor.authorSIMMONS, Joshua
hal.structure.identifierWater Research Laboratory [WRL]
dc.contributor.authorSPLINTER, Kristen
dc.contributor.authorSTEPHENS, Scott
dc.contributor.authorTOWNEND, Ian
dc.contributor.authorVITOUSEK, Sean
dc.contributor.authorVOS, Kilian
dc.date.accessioned2024-04-02T12:59:45Z
dc.date.available2024-04-02T12:59:45Z
dc.date.issued2020-02-07
dc.identifier.issn2045-2322en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189126
dc.description.abstractEnBeaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.
dc.language.isoENen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nd/
dc.title.enBlind testing of shoreline evolution models
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41598-020-59018-yen_US
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre/Océanographieen_US
bordeaux.journalScientific Reportsen_US
bordeaux.volume10en_US
bordeaux.hal.laboratoriesEPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.teamMETHYSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-02506235
hal.version1
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccCC BYen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific%20Reports&rft.date=2020-02-07&rft.volume=10&rft.issue=1&rft.eissn=2045-2322&rft.issn=2045-2322&rft.au=MONTA%C3%91O,%20Jennifer&COCO,%20Giovanni&ANTOL%C3%8DNEZ,%20Jose&BEUZEN,%20Tomas&BRYAN,%20Karin&rft.genre=article


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