Optimal Model Complexity for Sediment Assessment in Mangrove Restoration: A One-Dimensional Framework Under Data Constraints

Optimal Model Complexity for Sediment Assessment in Mangrove Restoration: A One-Dimensional Framework Under Data Constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimal Model Complexity for Sediment Assessment in Mangrove Restoration: A One-Dimensional Framework Under Data Constraints Astha Fernando, Shakthi K. Gunawardana, Sathira Abeysinghe, Medhisha P. Gunawardena, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9060398/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Excessive suspended sediment concentration (SSC) is a primary driver of mangrove seedling dieback in canal networks dug to provide hydrology for mangrove restoration. However, SSC prediction under the strict data gathering capacity constraints of resource-limited and community-based restoration projects remains challenging. This study develops and evaluates a one-dimensional (1-D) advection model across a branching mangrove canal network in Sri Lanka, calibrated against 264 manual samples from 23 instances across four ponds. The model solves steady-state advection and requires only boundary SSC and routine hydraulic inputs. Against withheld observations (n = 96), the calibrated model achieved RMSE = 0.053 g L − 1 , Pearson r = 0.605, and, sensitivity = 84.6%, correctly identifying approximately four in five instances that exceeded an empirically validated 0.08 g L − 1 SSC dieback threshold. In contrast, higher-order and data-driven models spanning the complexity spectrum failed at classification despite comparable RMSEs. Evaluated alternatives were: explicit FOCDMS, implicit TDMA, rating curve, radial basis function, and ensemble machine learning. Both FOCDMS and TDMA models failed due to grid Péclet numbers of O(10 4 –10 5 ) at manual sampling spacings. Data-driven models failed through geometric overfitting under leave-one-plot-out cross-validation, with Random Forest sensitivity falling to 34.6% and Pearson r=-0.025. The results demonstrate an analytically locatable optimal model complexity that is sufficient to encode governing physics without requiring data volumes unavailable through manual data gathering. Further, the developed sampler is deployable at USD 43.50, a 96.5% cost reduction relative to conventional instrumentation. The framework provides a scoping-level tool that enables sediment-aware restoration under data-constraints, transitioning away from “design by intuition”. Accelerated natural regeneration Data-scarce modelling Hydraulic connectivity Hydraulic rehabilitation Mangrove restoration Suspended sediment Full Text Supplementary Files Repository.txt Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 27 Mar, 2026 Editor invited by journal 12 Mar, 2026 Editor assigned by journal 09 Mar, 2026 First submitted to journal 07 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9060398","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613090572,"identity":"d584847e-ca1c-4270-9f7c-071d5249882c","order_by":0,"name":"Astha Fernando","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACNgYGGwhLgqECSDIzNxDQwQzUkgbTcgYkwEiKFsY2EEVAC3//+WMPfiTY5Juznz144+O82mj+dqCWHxXbcGqRuJHMbtiTkGa5sycv2XLmtuO5Mw4zNjD2nLmN25obzGwSvD8OGxgcyDGT5t12LLcBqIWZsQ23Fvnzh9kk/yT8NzA4/waoZc6x3PmEtBgcSGaT5kk4YGBwA2RLQ03uBkJaDG8km0nLJCQDtbwxtpxx7EDuRqCWg/j8Inf+4DPJNwl2QIflGN74UFOXO+/84YMPflTg8T4aOAwmDxCtHgjqSFE8CkbBKBgFIwQAAIZOWuCArRJ8AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-8098-7621","institution":"Curtin University","correspondingAuthor":true,"prefix":"","firstName":"Astha","middleName":"","lastName":"Fernando","suffix":""},{"id":613090573,"identity":"0a76d992-70d0-4b88-b240-9fd0764b9e63","order_by":1,"name":"Shakthi K. Gunawardana","email":"","orcid":"","institution":"Curtin University","correspondingAuthor":false,"prefix":"","firstName":"Shakthi","middleName":"K.","lastName":"Gunawardana","suffix":""},{"id":613090574,"identity":"fa2a8147-cab5-406e-8011-e50b8a33ecbd","order_by":2,"name":"Sathira Abeysinghe","email":"","orcid":"","institution":"Curtin University","correspondingAuthor":false,"prefix":"","firstName":"Sathira","middleName":"","lastName":"Abeysinghe","suffix":""},{"id":613090575,"identity":"66b92adf-9f9d-4906-b8d4-60be2e158c51","order_by":3,"name":"Medhisha P. Gunawardena","email":"","orcid":"","institution":"Wildlife and Nature Protection Society of Sri Lanka","correspondingAuthor":false,"prefix":"","firstName":"Medhisha","middleName":"P.","lastName":"Gunawardena","suffix":""},{"id":613090576,"identity":"4816f63a-f08f-44c4-9e31-4d0597c768fc","order_by":4,"name":"Sevvandi Jayakody","email":"","orcid":"","institution":"Wayamba University of Sri Lanka Faculty of Livestock Fisheries \u0026 Nutrition","correspondingAuthor":false,"prefix":"","firstName":"Sevvandi","middleName":"","lastName":"Jayakody","suffix":""}],"badges":[],"createdAt":"2026-03-07 18:54:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9060398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9060398/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105752182,"identity":"bd79c4b7-8cb6-4076-b558-64f920095b3f","added_by":"auto","created_at":"2026-03-30 15:55:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1658901,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptAsthaFernando.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9060398/v1_covered_28359683-fec2-427a-84c6-def0b394f812.pdf"},{"id":105751248,"identity":"145981e9-da50-4b3a-8b56-a1a42113dcb5","added_by":"auto","created_at":"2026-03-30 15:27:28","extension":"txt","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":98,"visible":true,"origin":"","legend":"","description":"","filename":"Repository.txt","url":"https://assets-eu.researchsquare.com/files/rs-9060398/v1/e3618f82c970002d6abd3020.txt"}],"financialInterests":"","formattedTitle":"Optimal Model Complexity for Sediment Assessment in Mangrove Restoration: A One-Dimensional Framework Under Data Constraints","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Accelerated natural regeneration, Data-scarce modelling, Hydraulic connectivity, Hydraulic rehabilitation, Mangrove restoration, Suspended sediment","lastPublishedDoi":"10.21203/rs.3.rs-9060398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9060398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExcessive suspended sediment concentration (SSC) is a primary driver of mangrove seedling dieback in canal networks dug to provide hydrology for mangrove restoration. However, SSC prediction under the strict data gathering capacity constraints of resource-limited and community-based restoration projects remains challenging. This study develops and evaluates a one-dimensional (1-D) advection model across a branching mangrove canal network in Sri Lanka, calibrated against 264 manual samples from 23 instances across four ponds. The model solves steady-state advection and requires only boundary SSC and routine hydraulic inputs. Against withheld observations (n\u0026thinsp;=\u0026thinsp;96), the calibrated model achieved RMSE\u0026thinsp;=\u0026thinsp;0.053 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Pearson r\u0026thinsp;=\u0026thinsp;0.605, and, sensitivity\u0026thinsp;=\u0026thinsp;84.6%, correctly identifying approximately four in five instances that exceeded an empirically validated 0.08 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e SSC dieback threshold. In contrast, higher-order and data-driven models spanning the complexity spectrum failed at classification despite comparable RMSEs. Evaluated alternatives were: explicit FOCDMS, implicit TDMA, rating curve, radial basis function, and ensemble machine learning. Both FOCDMS and TDMA models failed due to grid P\u0026eacute;clet numbers of O(10\u003csup\u003e4\u003c/sup\u003e\u0026ndash;10\u003csup\u003e5\u003c/sup\u003e) at manual sampling spacings. Data-driven models failed through geometric overfitting under leave-one-plot-out cross-validation, with Random Forest sensitivity falling to 34.6% and Pearson r=-0.025. The results demonstrate an analytically locatable optimal model complexity that is sufficient to encode governing physics without requiring data volumes unavailable through manual data gathering. Further, the developed sampler is deployable at USD 43.50, a 96.5% cost reduction relative to conventional instrumentation. The framework provides a scoping-level tool that enables sediment-aware restoration under data-constraints, transitioning away from \u0026ldquo;design by intuition\u0026rdquo;.\u003c/p\u003e","manuscriptTitle":"Optimal Model Complexity for Sediment Assessment in Mangrove Restoration: A One-Dimensional Framework Under Data Constraints","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 15:26:32","doi":"10.21203/rs.3.rs-9060398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-03-29T06:50:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-27T07:05:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2026-03-12T19:53:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T07:08:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2026-03-07T13:53:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7096f7b9-cd5f-4f9f-9c85-9b7f9a28bd56","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T15:26:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 15:26:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9060398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9060398","identity":"rs-9060398","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}