Benchmarking shoreline prediction models over multi-decadal timescales
Abstract Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitte...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02550-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332095032229888 |
|---|---|
| author | Yongjing Mao Giovanni Coco Sean Vitousek Jose A. A. Antolinez Georgios Azorakos Masayuki Banno Clément Bouvier Karin R. Bryan Laura Cagigal Kit Calcraft Bruno Castelle Xinyu Chen Maurizio D’Anna Lucas de Freitas Pereira Iñaki de Santiago Aditya N. Deshmukh Bixuan Dong Ahmed Elghandour Amirmahdi Gohari Eduardo Gomez-de la Peña Mitchell D. Harley Michael Ibrahim Déborah Idier Camilo Jaramillo Cardona Changbin Lim Ivana Mingo Julian O’Grady Daniel Pais Oxana Repina Arthur Robinet Dano Roelvink Joshua Simmons Erdinc Sogut Katie Wilson Kristen D. Splinter |
| author_facet | Yongjing Mao Giovanni Coco Sean Vitousek Jose A. A. Antolinez Georgios Azorakos Masayuki Banno Clément Bouvier Karin R. Bryan Laura Cagigal Kit Calcraft Bruno Castelle Xinyu Chen Maurizio D’Anna Lucas de Freitas Pereira Iñaki de Santiago Aditya N. Deshmukh Bixuan Dong Ahmed Elghandour Amirmahdi Gohari Eduardo Gomez-de la Peña Mitchell D. Harley Michael Ibrahim Déborah Idier Camilo Jaramillo Cardona Changbin Lim Ivana Mingo Julian O’Grady Daniel Pais Oxana Repina Arthur Robinet Dano Roelvink Joshua Simmons Erdinc Sogut Katie Wilson Kristen D. Splinter |
| author_sort | Yongjing Mao |
| collection | DOAJ |
| description | Abstract Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities. |
| format | Article |
| id | doaj-art-19a6e481f50841c5a98445d9b7a9ecae |
| institution | Kabale University |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-19a6e481f50841c5a98445d9b7a9ecae2025-08-20T03:46:20ZengNature PortfolioCommunications Earth & Environment2662-44352025-07-016111510.1038/s43247-025-02550-4Benchmarking shoreline prediction models over multi-decadal timescalesYongjing Mao0Giovanni Coco1Sean Vitousek2Jose A. A. Antolinez3Georgios Azorakos4Masayuki Banno5Clément Bouvier6Karin R. Bryan7Laura Cagigal8Kit Calcraft9Bruno Castelle10Xinyu Chen11Maurizio D’Anna12Lucas de Freitas Pereira13Iñaki de Santiago14Aditya N. Deshmukh15Bixuan Dong16Ahmed Elghandour17Amirmahdi Gohari18Eduardo Gomez-de la Peña19Mitchell D. Harley20Michael Ibrahim21Déborah Idier22Camilo Jaramillo Cardona23Changbin Lim24Ivana Mingo25Julian O’Grady26Daniel Pais27Oxana Repina28Arthur Robinet29Dano Roelvink30Joshua Simmons31Erdinc Sogut32Katie Wilson33Kristen D. Splinter34Water Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneySchool of Environment, Faculty of Science, University of AucklandU.S. Geological SurveyDepartment of Hydraulic Engineering, Delft University of TechnologyUniversité de Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, Alleé Geoffroy Saint-HilairePort and Airport Research InstituteBRGM (French Geological Survey), Parc technologique EuroparcSchool of Environment, Faculty of Science, University of AucklandGeomatics and Ocean Engineering Group, Departamento de Ciencias y Tecnicas del Agua y del Medio Ambiente, Universidad de CantabriaWater Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneyUniversité de Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, Alleé Geoffroy Saint-HilairePort and Airport Research InstituteSchool of Environment, Faculty of Science, University of AucklandIHCantabria – Instituto de Hidráulica Ambiental de la Universidad de CantabriaAZTI Marine Research, Basque Research and Technology Alliance (BRTA), Herrera Kaia. Portualdea z/gWater Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneyWater Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneyDepartment of Hydraulic Engineering, Delft University of TechnologySchool of Environment, Faculty of Science, University of AucklandSchool of Environment, Faculty of Science, University of AucklandWater Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneyComputer Engineering Department, Cairo UniversityBRGM (French Geological Survey)IHCantabria – Instituto de Hidráulica Ambiental de la Universidad de CantabriaIHCantabria – Instituto de Hidráulica Ambiental de la Universidad de CantabriaUniversité de Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, Alleé Geoffroy Saint-HilaireCSIRO EnvironmentCoLAB+ATLANTIC LVT, Museu das ComunicaçõesCoastal and Marine Research Centre, Griffith University, SouthportBRGM (French Geological Survey), Parc technologique EuroparcDepartment of Hydraulic Engineering, Delft University of TechnologyARC Training Centre in Data Analytics for Resources and Environments (DARE), University of SydneyCherokee Nation System Solutions, Contractor to the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, StWater Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneyWater Research Laboratory, School of Civil and Environmental Engineering, UNSW SydneyAbstract Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.https://doi.org/10.1038/s43247-025-02550-4 |
| spellingShingle | Yongjing Mao Giovanni Coco Sean Vitousek Jose A. A. Antolinez Georgios Azorakos Masayuki Banno Clément Bouvier Karin R. Bryan Laura Cagigal Kit Calcraft Bruno Castelle Xinyu Chen Maurizio D’Anna Lucas de Freitas Pereira Iñaki de Santiago Aditya N. Deshmukh Bixuan Dong Ahmed Elghandour Amirmahdi Gohari Eduardo Gomez-de la Peña Mitchell D. Harley Michael Ibrahim Déborah Idier Camilo Jaramillo Cardona Changbin Lim Ivana Mingo Julian O’Grady Daniel Pais Oxana Repina Arthur Robinet Dano Roelvink Joshua Simmons Erdinc Sogut Katie Wilson Kristen D. Splinter Benchmarking shoreline prediction models over multi-decadal timescales Communications Earth & Environment |
| title | Benchmarking shoreline prediction models over multi-decadal timescales |
| title_full | Benchmarking shoreline prediction models over multi-decadal timescales |
| title_fullStr | Benchmarking shoreline prediction models over multi-decadal timescales |
| title_full_unstemmed | Benchmarking shoreline prediction models over multi-decadal timescales |
| title_short | Benchmarking shoreline prediction models over multi-decadal timescales |
| title_sort | benchmarking shoreline prediction models over multi decadal timescales |
| url | https://doi.org/10.1038/s43247-025-02550-4 |
| work_keys_str_mv | AT yongjingmao benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT giovannicoco benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT seanvitousek benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT joseaaantolinez benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT georgiosazorakos benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT masayukibanno benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT clementbouvier benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT karinrbryan benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT lauracagigal benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT kitcalcraft benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT brunocastelle benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT xinyuchen benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT mauriziodanna benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT lucasdefreitaspereira benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT inakidesantiago benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT adityandeshmukh benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT bixuandong benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT ahmedelghandour benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT amirmahdigohari benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT eduardogomezdelapena benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT mitchelldharley benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT michaelibrahim benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT deborahidier benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT camilojaramillocardona benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT changbinlim benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT ivanamingo benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT julianogrady benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT danielpais benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT oxanarepina benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT arthurrobinet benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT danoroelvink benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT joshuasimmons benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT erdincsogut benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT katiewilson benchmarkingshorelinepredictionmodelsovermultidecadaltimescales AT kristendsplinter benchmarkingshorelinepredictionmodelsovermultidecadaltimescales |