Elucidating Reference Evapotranspiration Drivers in Contrasting Climates using Machine Learning: From Advection-Driven to Energy-Limited Processes

Elucidating Reference Evapotranspiration Drivers in Contrasting Climates using Machine Learning: From Advection-Driven to Energy-Limited Processes | 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 Elucidating Reference Evapotranspiration Drivers in Contrasting Climates using Machine Learning: From Advection-Driven to Energy-Limited Processes Alireza Shahriari, Mojtaba Mohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8755754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate estimation of reference evapotranspiration (ET₀) is fundamental to sustainable water resource management, particularly in regions characterized by high climatic heterogeneity. This study evaluates the performance of machine learning models across three distinct climatic regimes in Sistan and Baluchestan Province, Iran: hyper-arid (Zabol), semi-arid mountainous (Zahedan), and coastal-humid (Chabahar). Using daily meteorological data, the predictive capabilities of Random Forest (RF) and Support Vector Machine (SVM) models were benchmarked against the classical Multiple Linear Regression (MLR) under three input scenarios: temperature-based, temperature-humidity, and full-input. The results demonstrate that no single model is universally superior across all climatic conditions. The Random Forest (RF) model exhibited the highest precision in the extreme climates of hyper-arid Zabol (R²=0.99, RMSE = 0.46 mm/day) and coastal Chabahar (R²=0.88), whereas the Support Vector Machine (SVM) was superior in the stable, semi-arid conditions of Zahedan (R²=0.98, RMSE = 0.31 mm/day). Feature importance analysis revealed fundamental divergences in the governing physical processes: the ET₀ process in Zabol is primarily “advection-dominated,” driven by wind-speed dynamics; in contrast, the Chabahar regime is “energy-limited,” with maximum temperature as the primary controlling factor. These findings underscore the necessity of tailoring ET₀ modeling strategies to regional climatic drivers. This research provides a robust framework for enhancing the precision of hydrological modeling and water-resource allocation in territories with sharp climatic gradients. Reference Evapotranspiration Random Forest Support Vector Machine Sensitivity Analysis Advection Sistan and Baluchestan Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-8755754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588304913,"identity":"f72a52cc-ca42-4319-857b-cce77b2869d6","order_by":0,"name":"Alireza Shahriari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3RsWqDQBjA8e/4wFtObi1Yeq/gIaRL8FkUoWMJBEoghRwIl1cwEPoSgdAxIMQlD5EunQsuydR+2qWhZ8nY4f6ocOKPz1MAn+8/xpkBOjJAWmSz75v44/o7vCCHqwidPeliFv58uE+WaNj5NX28X+L7x9vLWEkQSTuBVAEPjy5yUzOD4aGY3tZBUuXbB70yYhRVUGiDPHaO6QizmFcICeTbOot3YoQCkHYXOIUiws52QYS3kK8/O5K0AhaDJCYCoa2JCJpidh2JIwH1INH9XmzTkSlk+0KvyuApquJG2wFy1yyP9GLzvJLNhp2eUyV5uWkns7mScu8kFD9drvufQt/KPcPn8/l81/QFOK9NuGv1cJ0AAAAASUVORK5CYII=","orcid":"","institution":"University of Sistan and Baluchistan","correspondingAuthor":true,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Shahriari","suffix":""},{"id":588304914,"identity":"848a1454-55bd-4f37-8453-acba259dc349","order_by":1,"name":"Mojtaba Mohammadi","email":"","orcid":"","institution":"University of Saravan","correspondingAuthor":false,"prefix":"","firstName":"Mojtaba","middleName":"","lastName":"Mohammadi","suffix":""}],"badges":[],"createdAt":"2026-02-01 11:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8755754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8755754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107881545,"identity":"fcc57686-cbef-455a-9f02-3187e7082ea4","added_by":"auto","created_at":"2026-04-27 08:43:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":575282,"visible":true,"origin":"","legend":"","description":"","filename":"ET0Manuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8755754/v1_covered_c9276934-7698-48ff-82f3-ab09706f0474.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating Reference Evapotranspiration Drivers in Contrasting Climates using Machine Learning: From Advection-Driven to Energy-Limited Processes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Reference Evapotranspiration, Random Forest, Support Vector Machine, Sensitivity Analysis, Advection, Sistan and Baluchestan","lastPublishedDoi":"10.21203/rs.3.rs-8755754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8755754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate estimation of reference evapotranspiration (ET₀) is fundamental to sustainable water resource management, particularly in regions characterized by high climatic heterogeneity. This study evaluates the performance of machine learning models across three distinct climatic regimes in Sistan and Baluchestan Province, Iran: hyper-arid (Zabol), semi-arid mountainous (Zahedan), and coastal-humid (Chabahar). Using daily meteorological data, the predictive capabilities of Random Forest (RF) and Support Vector Machine (SVM) models were benchmarked against the classical Multiple Linear Regression (MLR) under three input scenarios: temperature-based, temperature-humidity, and full-input.\u003c/p\u003e \u003cp\u003eThe results demonstrate that no single model is universally superior across all climatic conditions. The Random Forest (RF) model exhibited the highest precision in the extreme climates of hyper-arid Zabol (R\u0026sup2;=0.99, RMSE\u0026thinsp;=\u0026thinsp;0.46 mm/day) and coastal Chabahar (R\u0026sup2;=0.88), whereas the Support Vector Machine (SVM) was superior in the stable, semi-arid conditions of Zahedan (R\u0026sup2;=0.98, RMSE\u0026thinsp;=\u0026thinsp;0.31 mm/day). Feature importance analysis revealed fundamental divergences in the governing physical processes: the ET₀ process in Zabol is primarily \u0026ldquo;advection-dominated,\u0026rdquo; driven by wind-speed dynamics; in contrast, the Chabahar regime is \u0026ldquo;energy-limited,\u0026rdquo; with maximum temperature as the primary controlling factor. These findings underscore the necessity of tailoring ET₀ modeling strategies to regional climatic drivers. This research provides a robust framework for enhancing the precision of hydrological modeling and water-resource allocation in territories with sharp climatic gradients.\u003c/p\u003e","manuscriptTitle":"Elucidating Reference Evapotranspiration Drivers in Contrasting Climates using Machine Learning: From Advection-Driven to Energy-Limited Processes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 17:55:20","doi":"10.21203/rs.3.rs-8755754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"3c9623a2-0450-455b-82b0-2af654110610","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T08:42:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 17:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8755754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8755754","identity":"rs-8755754","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}