A Longitudinal Analysis of Water Quality Variations and the Impact on Community Perceptions and Resource Management: A Data-Driven Approach

Spatiotemporal Modeling of Water Quality Trends in a Coastal Wildlife Refuge: A Statistical Approach to Ecological Risk and Resource Management | 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 Spatiotemporal Modeling of Water Quality Trends in a Coastal Wildlife Refuge: A Statistical Approach to Ecological Risk and Resource Management Yiyao Yang, Yasemin Gulbahar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6721691/v4 This work is licensed under a CC BY 4.0 License Status: Posted Version 4 posted You are reading this latest preprint version Show more versions Abstract The research presents a spatiotemporal statistical modeling and analysis of variations in water quality parameters, such as salinity, dissolved oxygen (DO), pH, secchi depth, water depth, water temperature, and air temperature, in the Back Bay National Wildlife Refuge, a coastal ecosystem in Virginia Beach, United States. Based on longitudinal biweekly monitoring data from designated sites, we employed quantitative methodologies including time series decomposition, correlation analysis, Analysis of Variance (ANOVA), Tukey’s Honestly Significant Difference (HSD) test, and seasonal diagnostics to analyze trends and relationships among the key parameters. Our findings revealed a significant decline in DO levels post-1997, with episodic recovery at select locations, reflecting both climatic shifts and potential local interventions. Notably, spatial analyses demonstrated substantial differences in water quality across various sites, with two sites (the Bay Area and Site D) exhibiting the highest levels of instability, indicative of localized anthropogenic stressors such as land use change or pollution discharge. Besides, the observed statistical correlations among water quality parameters reveal complex interdependencies shaped by environmental and anthropogenic influences. The identification of statistical anomalies underscores the importance of localized monitoring and adaptive regulatory strategies. The results emphasize the importance of continuous, spatially resolved monitoring and adaptive water management strategies to enhance ecological engagement and ensure sustainable resource stewardship, offering actionable insight for environmental planners and environmental agencies aiming to preserve aquatic ecosystem integrity and foster long-term public trust in water governance. Applied Statistics Ecological Risk Assessment Environmental Management Spatiotemporal Variability Water Quality Monitoring Water Resource Management Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 4 posted You are reading this latest preprint version Show more versions 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-6721691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505431131,"identity":"a783f751-dedf-40c9-9bc8-b7d73a402c0f","order_by":0,"name":"Yiyao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFAC9uOff/6xgbAfMDAksIEYPHi18KQxMzakSYDZCcRpYTADajmM0MJASIvBjYS0x4U7ztfxNzA/fJDYti2Pj/0A44O3bfi0JB43nnnmtoTEATZjg8S228VsPAnMhnPxaklIkOBhuy1hwMDDJgHUktgG9I40L34tBkAt55C08D9g/01AixnQzANIWiQS2JjxaZE88ybZcMaZZMkZh4F+STgH9IvEw2bJOedwa+E7nn7wwYcKO37+9uaHDz6U3c6T708++OFNGW4tChcSoCxmuBhjA271QCDffwCv/CgYBaNgFIwCBgYAoO1UBT7LALIAAAAASUVORK5CYII=","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Yiyao","middleName":"","lastName":"Yang","suffix":""},{"id":505431132,"identity":"6742086c-d0b2-45d7-bd64-7f79b6fb906f","order_by":1,"name":"Yasemin Gulbahar","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Yasemin","middleName":"","lastName":"Gulbahar","suffix":""}],"badges":[],"createdAt":"2025-05-22 06:23:15","currentVersionCode":4,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6721691/v4","doiUrl":"https://doi.org/10.21203/rs.3.rs-6721691/v4","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93712275,"identity":"91617f38-abf6-40f8-9588-a8e39746623e","added_by":"auto","created_at":"2025-10-16 18:30:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":836247,"visible":true,"origin":"","legend":"","description":"","filename":"V3.1MESEManuscriptYiyaoYasemin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6721691/v4_covered_5cdb6ad1-46e7-40c8-9e54-c3dd8242c387.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Spatiotemporal Modeling of Water Quality Trends in a Coastal Wildlife Refuge: A Statistical Approach to Ecological Risk and Resource Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ecological Risk Assessment, Environmental Management, Spatiotemporal Variability, Water Quality Monitoring, Water Resource Management","lastPublishedDoi":"10.21203/rs.3.rs-6721691/v4","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6721691/v4","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe research presents a spatiotemporal statistical modeling and analysis of variations in water quality parameters, such as salinity, dissolved oxygen (DO), pH, secchi depth, water depth, water temperature, and air temperature, in the Back Bay National Wildlife Refuge, a coastal ecosystem in Virginia Beach, United States. 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