Trend analysis of dam inflow data using the Trend Accuracy Index and the Potential-Evapotranspiration Correction Factor

Trend analysis of dam inflow data using the Trend Accuracy Index and the Potential-Evapotranspiration Correction Factor | 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 Trend analysis of dam inflow data using the Trend Accuracy Index and the Potential-Evapotranspiration Correction Factor Won-joon Wang, Hung Soo Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7176953/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Korea is facing growing challenges in water-resources management because climate change is increasing the variability of rainfall and runoff. Accurately identifying station-specific trends in rainfall and dam inflow in advance would help maximize the benefits of efficient water-resources plans and hydraulic structure design. This study performed cluster and trend analyses for past (2000–2019) and future (2021–2050, 2051–2100) periods under the SSP2-4.5 and SSP3-7.0 climate change scenarios, using rainfall data from 101 weather stations and inflow data for Hapcheon Dam in the Nakdong River basin, Korea. In the trend analysis based on the modified Mann-Kendall test, only monthly, seasonal, and annual series that passed all three homogeneity tests were used. To simulate future dam inflows, we adopted a Four-Tank Model augmented with the Trend Accuracy Index (TAI) and a Potential-Evapotranspiration Correction Factor (PET-CF). The model parameters were simultaneously optimized by a genetic algorithm, enabling the simulated dam inflows to replicate observed trend. K-means + + clustering with three clusters revealed distinct rainfall characteristics associated with location and elevation of the station. Trend analyses showed diverse monthly, seasonal, and annual tendencies, and the trends of rainfall and dam inflow were not always consistent in either the historical or future periods. When both TAI and PET-CF were applied, the Four-Tank Model reproduced dam-inflow trends with the highest concordance (76.4%) compared to observations. The identified rainfall clusters and trend relationships provide a practical basis for developing adaptive water-resources strategies that account for climate-driven variability. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Scientific community and society/Water resources Cluster analysis Modified Mann–Kendall test Homogeneity test Tank model Trend Accuracy Index Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Editor invited by journal 06 Aug, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 24 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-7176953","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504393404,"identity":"a83785a9-cff4-480f-8cdd-ad2271ed9b65","order_by":0,"name":"Won-joon Wang","email":"","orcid":"","institution":"Inha University","correspondingAuthor":false,"prefix":"","firstName":"Won-joon","middleName":"","lastName":"Wang","suffix":""},{"id":504393405,"identity":"6f2784d3-3e1a-4e69-8c6b-a6db540f34e1","order_by":1,"name":"Hung Soo Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYHACZoYEBhseBgkIz4BYLWmkamFgOMxAvBb59ubDBg9zzsuYSzc/YPhRw2Bs3kBAi8GZY8kJidtu81jOOWbA2HOMwUzmACEtEjnGB0BaDG4kGDDwNjDYSBB02AywlnNALekfGP8So4XhRo4x0GEHgFpyDJiBtpgR1ALyi0HitmSQloLDMsckjAk7DBhikj+32dkDHbbx4ZsaG8MZBB2GDA7AY2cUjIJRMApGAWUAAEhcOwku8eBkAAAAAElFTkSuQmCC","orcid":"","institution":"Inha University","correspondingAuthor":true,"prefix":"","firstName":"Hung","middleName":"Soo","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-07-21 11:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7176953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7176953/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-40225-y","type":"published","date":"2026-02-20T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103251275,"identity":"22838c75-a7ec-4507-85e2-66598d57d507","added_by":"auto","created_at":"2026-02-23 16:07:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2160455,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificreportsManuscriptR1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7176953/v1_covered_872c12b8-fd37-4974-944e-cbfadb285d91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trend analysis of dam inflow data using the Trend Accuracy Index and the Potential-Evapotranspiration Correction Factor","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":"Cluster analysis, Modified Mann–Kendall test, Homogeneity test, Tank model, Trend Accuracy Index","lastPublishedDoi":"10.21203/rs.3.rs-7176953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7176953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKorea is facing growing challenges in water-resources management because climate change is increasing the variability of rainfall and runoff. Accurately identifying station-specific trends in rainfall and dam inflow in advance would help maximize the benefits of efficient water-resources plans and hydraulic structure design. This study performed cluster and trend analyses for past (2000\u0026ndash;2019) and future (2021\u0026ndash;2050, 2051\u0026ndash;2100) periods under the SSP2-4.5 and SSP3-7.0 climate change scenarios, using rainfall data from 101 weather stations and inflow data for Hapcheon Dam in the Nakdong River basin, Korea. In the trend analysis based on the modified Mann-Kendall test, only monthly, seasonal, and annual series that passed all three homogeneity tests were used. To simulate future dam inflows, we adopted a Four-Tank Model augmented with the Trend Accuracy Index (TAI) and a Potential-Evapotranspiration Correction Factor (PET-CF). The model parameters were simultaneously optimized by a genetic algorithm, enabling the simulated dam inflows to replicate observed trend. 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