Direct Human Interventions Drive Non-Stationarity in Annual Peak Streamflow Patterns Across the United States

Direct Human Interventions Drive Non-Stationarity in Annual Peak Streamflow Patterns Across the United States | 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 Physical Sciences - Article Direct Human Interventions Drive Non-Stationarity in Annual Peak Streamflow Patterns Across the United States Venkatesh Merwade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4077594/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Understanding the factors driving non-stationarity in annual peak streamflow, hereafter referred to as peakflow, remains pivotal amid climate change and direct human interventions 1,2 . Utilizing extensive streamflow observations from 3907 United States Geological Survey (USGS) stations, we have detected significant trends in 34% of these stations. Among these, two-thirds exhibit decreasing trends distributed across the United States, while the remaining one-third show increasing trends, predominantly in the Northeast and Great Lakes regions. Most USGS stations (84%) are influenced by direct human interventions such as water management and land use changes. Employing high-resolution climate and land-use data along with geospatial analytics, this study reveals urbanization and water management as the primary drivers, followed by agriculture and climate change. Urbanization emerges as the principal driver of peakflow trends in the Texas-Gulf, California, and Mid-Atlantic regions, accounting for up to 62%, 44%, and 32% of the variance, respectively. Water management explains most of the variance in the Tennessee (37%) and Ohio River Basins (30%). In the Upper Colorado River Basin, both agricultural and water management play significant roles, explaining up to 28% and 24% of the variance, respectively. Additionally, agricultural land use explains 17% of the variance in the Great Lakes region. Climate contributes modestly in the Rio Grande (15%) and California (11%) regions. Despite their extensive number of climate realizations (large ensemble), the latest generation of climate and earth system models inadequately captures these human-induced factors, limiting their predictive accuracy. By demonstrating the outsized influence of human interventions on peakflow trends and inadequacies in current climate models, our findings stress the imperative of integrating water management and urbanization effects into climate models for more accurate water predictions. Earth and environmental sciences/Hydrology Physical sciences/Engineering Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Communications Earth & Environment → 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4077594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":281108705,"identity":"2030a3ac-8eff-4cbd-ba25-a8003063acc0","order_by":0,"name":"Venkatesh Merwade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYPACZgZ+EMUD5iQQqUWygWQtBgeI1WJw7fCxzzw11nLGt3vMPrxts2HgZ88xwK/ldlrybJ5j6cZmd84Yz5zblsYg2fMGvxbJ2TnGzDlshxO33QAyeNsOMxjcIGCL5Oz8z8w5/w7Xb54B1vKfwZ6QFn7pHGbm3LbDCQYSYC0HGIAMQlrSjJn/9qUbzrhzrJhxzrlkHokzzwrwamGTTn7MOOObtTz/7ObNDG/K7OT425M34NWCABIQiodI5UhaRsEoGAWjYBRgAABceUCW8n7P9gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5518-2890","institution":"Purdue University","correspondingAuthor":true,"prefix":"","firstName":"Venkatesh","middleName":"","lastName":"Merwade","suffix":""}],"badges":[],"createdAt":"2024-03-11 19:10:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4077594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4077594/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02738-8","type":"published","date":"2025-09-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92475396,"identity":"88ff923c-c268-4545-b438-acf9d65199db","added_by":"auto","created_at":"2025-09-30 07:16:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4546041,"visible":true,"origin":"","legend":"","description":"","filename":"Josephetal2024draft10SK.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4077594/v1_covered_c637dd02-eb3b-4a64-a2de-9d695fe7141e.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Direct Human Interventions Drive Non-Stationarity in Annual Peak Streamflow Patterns Across the United States","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4077594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4077594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the factors driving non-stationarity in annual peak streamflow, hereafter referred to as peakflow, remains pivotal amid climate change and direct human interventions\u003csup\u003e1,2\u003c/sup\u003e. 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In the Upper Colorado River Basin, both agricultural and water management play significant roles, explaining up to 28% and 24% of the variance, respectively. Additionally, agricultural land use explains 17% of the variance in the Great Lakes region. Climate contributes modestly in the Rio Grande (15%) and California (11%) regions. Despite their extensive number of climate realizations (large ensemble), the latest generation of climate and earth system models inadequately captures these human-induced factors, limiting their predictive accuracy. 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