Hybrid approaches enhance hydrological model usability for local streamflow prediction

Hybrid approaches enhance hydrological model usability for local streamflow prediction | 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 Article Hybrid approaches enhance hydrological model usability for local streamflow prediction Ilias Pechlivanidis, Yiheng Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5142634/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. Here we explore hybrid methods combining process-based modelling and statistical or machine learning post-processors to improve streamflow predictive accuracy, including extremes, across Europe’s hydro-climatic gradient. We investigate various post-processing methods, such as random forest, long short-term memory model, quantile mapping and generalised linear model, demonstrating significant improvements in model performance, particularly in terms of reducing total volume errors and increasing robustness across diverse climatic and geographic conditions. We further show that hydrologic similarity is one of the key drivers that control the hybrid approach’s improvements, together with other basin characteristics, such as mean precipitation and mean temperature. Our results also reveal spatial complementarity among the post-processing methods, with no absolute superiority identified from a single method, pointing towards multi-model averaging approaches for the future evolution of hybrid hydrological modelling. Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Hybrid hydrological modelling Machine learning Post-processing Performance attribution Streamflow prediction Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted 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. 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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-5142634","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":364144560,"identity":"bd58c42e-1fdd-4473-b8fc-43a63250613d","order_by":0,"name":"Ilias Pechlivanidis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3QMUvDQBTA8RcOkuU1t56k2K9wUuji0K+SInTq4qYgmCC0m3PAL+HU+coDO3ZVmqEi3NwskizinbWd7pwd7j8kN+THvReAUOgfFidRcTgJVijzQiCIdgAKvYSdSHQiTP5FgB0P4mgVxMI+faWMlU0L9Tl/KkvCu7qfrhN9283qPnByOjPYQ4agh6JeFYQvGs8IR9veUiOIae7bJQOgybOYFKsqJpSE8TZakiEofbd0LdD9gXxZkujrzhK+2XvIXJi/lEtDVDO3BEbQswRmzvUtuUSpL6pXSx7J7jLMLInF1DnYYLH4eGtv6gGvrmiff9I43azfGzPYmHPaOa/5SSrXAP7vbS4SCoVCod++AUKNY6rzWypbAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3416-317X","institution":"Swedish Meteorological and Hydrological Institute","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ilias","middleName":"","lastName":"Pechlivanidis","suffix":""},{"id":364144561,"identity":"fee671c1-0552-41b6-9c25-cc54a5668e4e","order_by":1,"name":"Yiheng Du","email":"","orcid":"https://orcid.org/0000-0002-5176-8111","institution":"Swedish Meteorological and Hydrological Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiheng","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-09-24 07:10:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5142634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5142634/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67286344,"identity":"0dba7df0-a36f-4838-bcca-4e5d09d44f42","added_by":"auto","created_at":"2024-10-23 09:34:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2124211,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5142634/v1_covered_1ce4c88e-c98a-4e03-9359-2f05e489a967.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Hybrid approaches enhance hydrological model usability for local streamflow prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hybrid hydrological modelling, Machine learning, Post-processing, Performance attribution, Streamflow prediction","lastPublishedDoi":"10.21203/rs.3.rs-5142634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5142634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. 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