Study on Flood Characteristics under Different Rainfall Mechanisms

Study on Flood Characteristics under Different Rainfall Mechanisms | 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 Study on Flood Characteristics under Different Rainfall Mechanisms Jing-Lin Qian, Yun-Xin Wu, Zhu-Sheng Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6036137/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 methods the annual maximum flood (AMF) and the peak over threshold (POT) methods, and applies extreme value theory to analyze the flood data. In the AMF method, the statistical characteristics of flood peaks are described by fitting a Gumbel distribution; in the POT method, the tail behavior of flood events exceeding a set threshold is modeled using the Generalized Pareto Distribution (GPD). Additionally, rainfall intensity, duration, and frequency are statistically analyzed, and the Mann-Kendall non-parametric trend test, which accounts for autocorrelation effects, is used to reveal significant changes in the spatiotemporal distribution and extreme value characteristics of floods. The results show that, since 1990, the peak flood month in the XF watershed has shifted from July to June, while flood events in August have significantly increased in the HJT watershed. Furthermore, the temperature has shown a significant upward trend, consistent with the theoretical expectation of increased atmospheric water vapor and intensified extreme rainfall. Although a single trend test is insufficient to directly prove the causal effect of climate change, the combined changes in extreme value distribution parameters provide solid statistical evidence for the potential impact of climate change on flood extremes and their spatiotemporal distribution. The findings of this study offer scientific support for regional flood management, infrastructure design, and future climate-adaptive flood risk management. Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Earth and environmental sciences/Climate sciences/Hydrology Climate change flood extremes extreme value theory Gumbel distribution Generalized Pareto Distribution rainfall mechanisms 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 05 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Editor assigned by journal 16 Apr, 2025 Editor invited by journal 27 Mar, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 26 Mar, 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-6036137","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444040569,"identity":"c601d411-955c-4ee3-9ece-f8a238143268","order_by":0,"name":"Jing-Lin Qian","email":"","orcid":"","institution":"Zhejiang University of Water Resources and Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Jing-Lin","middleName":"","lastName":"Qian","suffix":""},{"id":444040570,"identity":"c52bfdab-59c3-485a-aec8-bb4ef6f91daa","order_by":1,"name":"Yun-Xin Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYFCCw20MDBUScvIkaDkI1HLGwtiwgXgtjG1AVJHIcIBYDXwHD7Y9/DlPIoGxgfnhoxvEaJE8cLDdQHKbRB47A5uxcQ4xWgwOHGyTMNwmUczYwMMmTbyWxDkSiQ0HSNJysIEULUC/tEk2HJMwNmwm1i98Nw4fk/xRUycnz9788DFRWhhuHIAymIlSDgLnG4hWOgpGwSgYBSMVAACe9TSjRSmuoQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang University of Water Resources and Electric Power","correspondingAuthor":true,"prefix":"","firstName":"Yun-Xin","middleName":"","lastName":"Wu","suffix":""},{"id":444040571,"identity":"ef823692-287a-4098-8eb0-32eb76110107","order_by":2,"name":"Zhu-Sheng Qian","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Zhu-Sheng","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2025-02-15 11:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6036137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6036137/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30736-5","type":"published","date":"2025-12-11T15:59:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98244815,"identity":"da3a2408-a75b-43eb-8bc8-bfc2c771a5c7","added_by":"auto","created_at":"2025-12-15 16:15:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1801737,"visible":true,"origin":"","legend":"","description":"","filename":"0Fina.EnglishRevised3doublespace1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6036137/v1_covered_029f9884-6bbd-4c78-82e1-21d67f1c260c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on Flood Characteristics under Different Rainfall Mechanisms","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":"Climate change, flood extremes, extreme value theory, Gumbel distribution, Generalized Pareto Distribution, rainfall mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-6036137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6036137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003emethods\u003c/h2\u003e \u003cp\u003ethe annual maximum flood (AMF) and the peak over threshold (POT) methods, and applies extreme value theory to analyze the flood data. 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