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dc.contributor.authorVries, J
dc.contributor.authorMaanen, B
dc.contributor.authorRuessink, G
dc.contributor.authorVerweij, PA
dc.contributor.authorJong, SM
dc.date.accessioned2022-05-18T16:05:16Z
dc.date.issued2022-04-26
dc.date.updated2022-05-18T15:17:48Z
dc.description.abstractFor the development of climate-resilient coastal management strategies, which focus on challenges in the decades to come, it is critical to incorporate spatial and temporal variability of coastline changes. This is particularly true for the mud-dominated coastline of Suriname, part of the Guianas, where migrating subtidal mudbanks cause a cyclic instability of erosion and accretion of the coast that can be directly related to interbank and bank phases. The coastline hosts extensive mangrove forests, providing valuable ecosystem services to local communities. Recent studies on mudbank dynamics in Suriname predominantly focused on large-scale trends without accounting for local variability, or on local changes considering the dynamics of a single mudbank over relatively short time scales. Here we use a remote sensing approach, with sufficient spatial and temporal resolution and full spatial and temporal coverage, to quantify the influence of mudbank migration on spatiotemporal coastline dynamics along the entire coast of Suriname. We show that migration of six to eight subtidal mudbanks in front of the Suriname coast has a strong imprint on local coastline dynamics between 1986 and 2020, with an average 32 m/yr accretion during mudbank presence and 4 m/yr retreat of the coastline during mudbank absence. Yet, coastal erosion can still occur when mudbanks are present and coastal aggregation may happen in the absence of mudbanks, exemplifying local variability and thus suggesting the importance of other drivers of coastline changes. The novel remote sensing workflow allowed us to analyse local spatial and temporal variations in the magnitude and timing of expanding and retreating trajectories. Our results demonstrate that it is essential that all coastal behaviours, including changes that cannot be explained by the migration of mudbanks, are included in multi-decadal management frameworks that try to explain current variability, and predict future coastline changes in Suriname.en_GB
dc.description.sponsorshipNWO WOTRO Joint Sustainability Development Goal Research Programen_GB
dc.identifier.citationPublished online 26 April 2022en_GB
dc.identifier.doihttps://doi.org/10.1002/esp.5390
dc.identifier.grantnumberW07.303.106en_GB
dc.identifier.urihttp://hdl.handle.net/10871/129678
dc.language.isoenen_GB
dc.publisherWileyen_GB
dc.relation.urlhttps://github.com/jobbo90/offshore_boundary/ releases/tag/v0.2en_GB
dc.relation.urlhttps://code.earthengine.google.com/?accept_repo=users/jobdevries90/MangroMuden_GB
dc.rights© 2022 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_GB
dc.subjectcoastline dynamicsen_GB
dc.subjectGoogle Earth Engineen_GB
dc.subjectmangroveen_GB
dc.subjectmigrating subtidal mudbanksen_GB
dc.subjectremote sensingen_GB
dc.subjectSurinameen_GB
dc.titleMulti‐decadal coastline dynamics in Suriname controlled by migrating subtidal mudbanksen_GB
dc.typeArticleen_GB
dc.date.available2022-05-18T16:05:16Z
dc.identifier.issn0197-9337
dc.descriptionThis is the final version. Available from Wiley via the DOI in this record. en_GB
dc.descriptionDATA AVAILABILITY STATEMENT The pre-processing scripts that are used to define outliers for coastline position estimates and annual change metrics are available through Github: https://github.com/jobbo90/offshore_boundary/ releases/tag/v0.2 The reported coastline position estimates and indications of mudbank presence can be found in the online GEE repository (v02), which also includes the scripts used to derive these indicators: https://code.earthengine.google.com/?accept_repo=users/jobdevries90/ MangroMud The UAV drone datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.en_GB
dc.identifier.eissn1096-9837
dc.identifier.journalEarth Surface Processes and Landformsen_GB
dc.relation.ispartofEarth Surface Processes and Landforms
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2022-04-14
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-04-26
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-05-18T16:01:22Z
refterms.versionFCDVoR
refterms.dateFOA2022-05-18T16:05:38Z
refterms.panelCen_GB
refterms.dateFirstOnline2022-04-26


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© 2022 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.  
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's licence is described as © 2022 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.