Structural trend and variability identification (STVI) method

Structural trend and variability identification (STVI) method | 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PDF Research Article Structural trend and variability identification (STVI) method Zekâi Şen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6693024/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2026 Read the published version in Water Resources Management → Version 1 posted 5 You are reading this latest preprint version Abstract This paper proposes an effective method for internal dynamic trend analysis on the mean value and variability trends on the basis of standard deviation. Thus, one is able to follow not only classical trends also coupled with it standard deviation trends, which provides additional information about the tendency of fluctuations around mean values. Thus, one is able to assess not only conventional trend analysis, but also trends concerning deviations from the mean value. The proposed method is referred to as the structural trend and variability identification (STVI), which is a new version of the existing innovative trend analysis (ITA) methodology. The STVI procedure provides to extract embedded trends of any duration along the hydro-meteorology time series records. In this paper, lag-one overlapping and non-overlapping classical and variability trends are presented for two finite durations of 10-year and 30-year, respectively. The application of the STVI method is presented for Danube River annual flows records of more than 160 years. The probability cumulative distribution function (CDF) for 10-year slopes confirms theoretically with Pearson CDF, which provides possibility of trend and variability future prediction possibility at different risk levels (return periods). It is found that in the future, there are structural trend possibilities at a set of 50% (2-year), 20% (5-year), 10% (10-year), 4% (25-year) and 2% (50-year) levels corresponding to slopes of 66.32 m 3 /year, 91.60 m 3 /year, 103.07 m 3 /year, 113.45 m 3 /year and 118.98 m 3 /year, respectively, for 10-year structural trends. Similar results are also presented for 30-year trend and variability slopes. innovative piecewise probability risk structural trend slope variability Full Text Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2026 Read the published version in Water Resources Management → Version 1 posted Editorial decision: Major revisions 19 Dec, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 19 May, 2025 First submitted to journal 18 May, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6693024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462942897,"identity":"275e5a6f-1acd-43e1-b0ed-40eb01618863","order_by":0,"name":"Zekâi Şen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYLCCDwwMjG1AfICBgZk4HYwzIFoYIFrYiNDCzAPU0kC0Fvn+w8ce2+bYyPZJNx84wFBhndgg3/uAgKPS0o1zt6UZt8kcSzjAcCY9sYGN3QC/oyR4zKRztx1ObJPIMTjA2HYYqIWAy9j4z3+Tttz2H6rlHxFaeBhy2KQZtx2AamkgQouERJqZZO+2ZIhfEo6lG7expeHXAgyxZxI/t9nJzp/dfPDBhxpr2X7mY/i1INkHxAkMxMUkkpZRMApGwSgYBdgAAINHQY7AXtl1AAAAAElFTkSuQmCC","orcid":"","institution":"Istanbul Medipol Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Zekâi","middleName":"","lastName":"Şen","suffix":""}],"badges":[],"createdAt":"2025-05-18 17:15:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6693024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6693024/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11269-026-04542-1","type":"published","date":"2026-02-25T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103766610,"identity":"784e00f4-6fe4-4ad3-88b2-90b32aced738","added_by":"auto","created_at":"2026-03-02 16:15:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":774047,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693024/v1_covered_b709aeaf-368c-4dbb-83c8-400df0921e79.pdf"}],"financialInterests":"","formattedTitle":"Structural trend and variability identification (STVI) method","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":"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":"innovative, piecewise, probability, risk, structural, trend, slope, variability","lastPublishedDoi":"10.21203/rs.3.rs-6693024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6693024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes an effective method for internal dynamic trend analysis on the mean value and variability trends on the basis of standard deviation. Thus, one is able to follow not only classical trends also coupled with it standard deviation trends, which provides additional information about the tendency of fluctuations around mean values. Thus, one is able to assess not only conventional trend analysis, but also trends concerning deviations from the mean value. The proposed method is referred to as the structural trend and variability identification (STVI), which is a new version of the existing innovative trend analysis (ITA) methodology. The STVI procedure provides to extract embedded trends of any duration along the hydro-meteorology time series records. In this paper, lag-one overlapping and non-overlapping classical and variability trends are presented for two finite durations of 10-year and 30-year, respectively. The application of the STVI method is presented for Danube River annual flows records of more than 160 years. The probability cumulative distribution function (CDF) for 10-year slopes confirms theoretically with Pearson CDF, which provides possibility of trend and variability future prediction possibility at different risk levels (return periods). It is found that in the future, there are structural trend possibilities at a set of 50% (2-year), 20% (5-year), 10% (10-year), 4% (25-year) and 2% (50-year) levels corresponding to slopes of 66.32 m\u003csup\u003e3\u003c/sup\u003e/year, 91.60 m\u003csup\u003e3\u003c/sup\u003e/year, 103.07 m\u003csup\u003e3\u003c/sup\u003e/year, 113.45 m\u003csup\u003e3\u003c/sup\u003e/year and 118.98 m\u003csup\u003e3\u003c/sup\u003e/year, respectively, for 10-year structural trends. 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