A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff

A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff | 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 A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff Chunchen Xia, Lingna Zhang, Zekai Zhu, Huangjie Xia, Haoyong Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7245414/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Attribution analysis of runoff variation holds significant importance for water resource conservation and management. This study established a methodological framework for runoff reconstruction, quantification, and analysis of contribution rates. The framework comprises four core components: (1) Multi-temporal scale analysis: implementing explicit aggregation rules based on variable physical meanings to aggregate hydro-meteorological variables across daily, weekly, two-week, monthly, two-month, seasonal, and annual scales; (2) Variable screening mechanism: determining optimal explanatory variables by calculating Spearman correlations between meteorological factors and runoff at each time scale combined with variance inflation factor (VIF) analysis to address multicollinearity; (3) Multi-model comparison: reconstructing runoff using Random Forest Regression Model (RFRM) and Soil and Water Assessment Tool (SWAT), with validation through metrics including Coefficient of Determination (R 2 ) and relative bias (RBIAS); (4) Integrated attribution approach: combining remote sensing and statistical data to identify driving factors of anthropogenic impacts. Tested on river time-series data from the Lan River Basin, this framework quantified contribution percentages and provided methodological references for temporal scale/model selection and driver analysis in watershed hydrological studies. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Scientific community and society/Water resources Runoff attribution Multi-temporal scale Machine learning model Hydrological model Remote sensing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 31 Jul, 2025 First submitted to journal 29 Jul, 2025 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-7245414","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509208319,"identity":"f5a7d1dc-36a6-405a-8485-8f747d202366","order_by":0,"name":"Chunchen Xia","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chunchen","middleName":"","lastName":"Xia","suffix":""},{"id":509208320,"identity":"ddaf23d2-447f-407a-96ac-6d3a664c0bed","order_by":1,"name":"Lingna Zhang","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lingna","middleName":"","lastName":"Zhang","suffix":""},{"id":509208321,"identity":"691afd1e-43c4-4d85-a110-4744ff1dd368","order_by":2,"name":"Zekai Zhu","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zekai","middleName":"","lastName":"Zhu","suffix":""},{"id":509208322,"identity":"51796989-839b-4853-82ea-6a3d0c4633a4","order_by":3,"name":"Huangjie Xia","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huangjie","middleName":"","lastName":"Xia","suffix":""},{"id":509208323,"identity":"26b09dd3-cb70-497b-968d-4a3b655424e2","order_by":4,"name":"Haoyong Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACAyjNw8bewAZiMDYQq0WGn+cAiVpsJGckEKnFnL3H8DHvjloeg5tvzB7zMNjIbjjA/OwBPi2WPWeMjXnPHOcxuJ1jbszDkGa84QCbuQE+LQY3csykeduOAbXkbpPmYTicuOEAD5sEcVpungVp+U+0lhoeyRm8IC0HiNBy5lix4dy2Azz8PPnfJOcYJBvPPMxmhl/L8eaND9621dmzsR9Lk3hTYSfbd7z5GV4tDAwcoOA5DDMBiJnxqwcC9gdAoo6gslEwCkbBKBjBAADGbEXfFcj5DwAAAABJRU5ErkJggg==","orcid":"","institution":"POWERCHINA Huadong Engineering Corporation Limited","correspondingAuthor":true,"prefix":"","firstName":"Haoyong","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2025-07-29 16:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7245414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7245414/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-32088-6","type":"published","date":"2025-12-11T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98243988,"identity":"1671458d-f4af-4359-95a9-a638ff002dfa","added_by":"auto","created_at":"2025-12-15 16:12:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350426,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7245414/v1_covered_7d0944a8-a3f2-4299-916b-f549e28b5a8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Runoff attribution, Multi-temporal scale, Machine learning model, Hydrological model, Remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-7245414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7245414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAttribution analysis of runoff variation holds significant importance for water resource conservation and management. This study established a methodological framework for runoff reconstruction, quantification, and analysis of contribution rates. The framework comprises four core components: (1) Multi-temporal scale analysis: implementing explicit aggregation rules based on variable physical meanings to aggregate hydro-meteorological variables across daily, weekly, two-week, monthly, two-month, seasonal, and annual scales; (2) Variable screening mechanism: determining optimal explanatory variables by calculating Spearman correlations between meteorological factors and runoff at each time scale combined with variance inflation factor (VIF) analysis to address multicollinearity; (3) Multi-model comparison: reconstructing runoff using Random Forest Regression Model (RFRM) and Soil and Water Assessment Tool (SWAT), with validation through metrics including Coefficient of Determination (R\u003csup\u003e2\u003c/sup\u003e) and relative bias (RBIAS); (4) Integrated attribution approach: combining remote sensing and statistical data to identify driving factors of anthropogenic impacts. 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