Predicting Turbidity and Total Organic Carbon Changes under Climate Change in Water Supply Systems using Machine Learning

Predicting Turbidity and Total Organic Carbon Changes under Climate Change in Water Supply Systems using Machine Learning | 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 Predicting Turbidity and Total Organic Carbon Changes under Climate Change in Water Supply Systems using Machine Learning Thi Nhu Khanh Nguyen, Baptiste François, Alexis Dufour, Casey Brown This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7293547/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Impacts of climate change on surface water quality are a concern for water utilities. Adapting to these changes requires accurate predictive models that can assess the effects of climate change on water quality. In this study, we developed machine learning models to support climate change impact assessments, due to their low computational cost and reduced complexity in required input data. We integrated a weather generator to produce scenarios of precipitation and air temperature, along with a water demand model, a hydrologic model, a water system model, and a machine-learning based water quality model. Using upstream hydroclimatic and system state variables, we predicted turbidity and total organic carbon at the Tesla Treatment Facility, which is downstream of the Hetch Hetchy Regional Water Supply System. Our results show that changes in precipitation have a stronger impact on water quality than changes in air temperature or water demand. Increased precipitation intensifies the frequency, duration, and severity of water quality extremes, while also shortening their return periods. These findings provide valuable insights for water managers in developing water quality management plans under climate risk. Vulnerability Assessment Climate Change Water Demand Water Quality Water Supply Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 Jan, 2026 Reviewers invited by journal 02 Dec, 2025 Editor assigned by journal 19 Nov, 2025 First submitted to journal 18 Nov, 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-7293547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554371097,"identity":"2ed949d5-c554-4640-9ef3-6b033076653b","order_by":0,"name":"Thi Nhu Khanh Nguyen","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8236-6182","institution":"University of Massachusetts Amherst","correspondingAuthor":true,"prefix":"","firstName":"Thi","middleName":"Nhu Khanh","lastName":"Nguyen","suffix":""},{"id":554371098,"identity":"d3c47329-5433-4a04-83b1-6d5cca4e567d","order_by":1,"name":"Baptiste François","email":"","orcid":"","institution":"University of Massachusetts Amherst","correspondingAuthor":false,"prefix":"","firstName":"Baptiste","middleName":"","lastName":"François","suffix":""},{"id":554371099,"identity":"aabd0c1e-d613-4303-b17a-7ba23adf6145","order_by":2,"name":"Alexis Dufour","email":"","orcid":"","institution":"Hydro-Québec: Hydro-Quebec","correspondingAuthor":false,"prefix":"","firstName":"Alexis","middleName":"","lastName":"Dufour","suffix":""},{"id":554371100,"identity":"d2eb822d-8b09-498b-ae84-cbc0ff84c58b","order_by":3,"name":"Casey Brown","email":"","orcid":"","institution":"University of Massachusetts Amherst","correspondingAuthor":false,"prefix":"","firstName":"Casey","middleName":"","lastName":"Brown","suffix":""}],"badges":[],"createdAt":"2025-08-04 17:30:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7293547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7293547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104401704,"identity":"beb12ec5-4c46-4ac7-aa34-8f37cfbf3428","added_by":"auto","created_at":"2026-03-11 12:13:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1322762,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293547/v1_covered_3c0d7a08-2e7f-4319-ba43-5f9e3b875326.pdf"}],"financialInterests":"","formattedTitle":"Predicting Turbidity and Total Organic Carbon Changes under Climate Change in Water Supply Systems using Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Vulnerability Assessment, Climate Change, Water Demand, Water Quality, Water Supply","lastPublishedDoi":"10.21203/rs.3.rs-7293547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7293547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImpacts of climate change on surface water quality are a concern for water utilities. 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