Predicting Flood Water Levels in Data-scarce Meso-Scale River Basins for Support Early Warning Systems

Predicting Flood Water Levels in Data-scarce Meso-Scale River Basins for Support Early Warning Systems | 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 Predicting Flood Water Levels in Data-scarce Meso-Scale River Basins for Support Early Warning Systems Vinicius Santanna Castiglio, Gean Paulo Michel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9045388/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study proposes a methodology for predicting maximum river water levels during rainfall events in meso-scale river basins with limited data availability. The approach reformulates flood peak prediction as a multi-horizon conditional mapping problem, thereby reducing the dependence on long historical time series and recurrent architectures commonly employed in the literature. The proposed methodology estimates flood levels based on prior observations of river stage, precipitation data, and the elapsed time since the onset of the event, aiming to support early flood warning systems. For the proposed approach, water level and accumulated precipitation data were used for forecast horizons of 1, 2, 6, 12, and 14 hours prior to the occurrence of the predicted level. The methodology is based on an artificial neural network model, with the dataset divided into training (70.0%) and validation (30.0%) subsets. The model was applied to the Mascarada River basin, located in northeastern Rio Grande do Sul State, Brazil. The results demonstrated strong predictive performance, particularly for forecast horizons equal to or shorter than 6 hours, for both training and validation datasets. Nash–Sutcliffe efficiency values exceeded 0.75, while RSR values remained below 0.50. For forecast horizons of 12 hours or longer, performance ranged from good to satisfactory. Overall, the proposed methodology and forecasting model demonstrated high potential for water level prediction in data-scarce river basins, representing a promising tool for the anticipation of extreme hydrological events under such conditions. Artificial Neural Network Ungauged Catchments Flood forecasting Water level prediction Machine learning Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 23 Mar, 2026 Editor assigned by journal 08 Mar, 2026 First submitted to journal 08 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-9045388","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616753129,"identity":"b59a75e4-73c5-4e34-a889-944c21c28cb4","order_by":0,"name":"Vinicius Santanna Castiglio","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0460-5966","institution":"Federal University of Rio Grande do Sul: Universidade Federal do Rio Grande do Sul","correspondingAuthor":true,"prefix":"","firstName":"Vinicius","middleName":"Santanna","lastName":"Castiglio","suffix":""},{"id":616753130,"identity":"53b05079-2d4e-465c-bbca-80031ed779b7","order_by":1,"name":"Gean Paulo Michel","email":"","orcid":"","institution":"Federal University of Rio Grande do Sul: Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Gean","middleName":"Paulo","lastName":"Michel","suffix":""}],"badges":[],"createdAt":"2026-03-06 02:59:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9045388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9045388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106391191,"identity":"df50be91-6381-43eb-a574-21fa201c5e47","added_by":"auto","created_at":"2026-04-08 07:13:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":949755,"visible":true,"origin":"","legend":"","description":"","filename":"PREDICTINGFLOODWATERLEVELSINDATASCARCEMESOSCALE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9045388/v1_covered_ef82ce74-2772-40bc-9c46-183cfa7621ac.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePredicting Flood Water Levels in Data-scarce Meso-Scale River Basins for Support Early Warning Systems\u003c/p\u003e","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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial Neural Network, Ungauged Catchments, Flood forecasting, Water level prediction, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-9045388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9045388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study proposes a methodology for predicting maximum river water levels during rainfall events in meso-scale river basins with limited data availability. 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