Enhanced Estimation of Reference Evapotranspiration (ET₀) through Multiple Linear Regression Approaches in Semi-Arid Regions of Madhya

Enhanced Estimation of Reference Evapotranspiration (ET₀) through Multiple Linear Regression Approaches in Semi-Arid Regions of Madhya | 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 Enhanced Estimation of Reference Evapotranspiration (ET₀) through Multiple Linear Regression Approaches in Semi-Arid Regions of Madhya YADVENDRA PAL SINGH, A. S. TOMAR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7720442/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 Reference evapotranspiration (ET₀) at the Ashok Nagar station was estimated using multiple linear regression (MLR) techniques. A 30-year dataset (1994–2023) comprising climatological parameters such as maximum and minimum air temperatures, mean relative humidity, wind speed, and solar radiation for the Ashok Nagar district, Madhya Pradesh, India, was utilized. Observed ET₀ values, computed using the FAO-56 Penman–Monteith equation, served as the dependent variable for model development in SPSS v21. Three MLR models were formulated with different combinations of independent variables: Model-1 included maximum and minimum temperatures, Model-2 incorporated maximum and minimum temperatures along with solar radiation, and Model-3 combined maximum and minimum temperatures, wind speed, and relative humidity. Performance evaluation was conducted by comparing the models with the Penman–Monteith standard using statistical indices such as the correlation coefficient (R), coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Model-3 exhibited superior predictive performance, with R, R², MAE, and RMSE values of 0.976, 0.952, 0.366, and 0.477 for the calibration dataset (70%), and 0.978, 0.957, 0.449, and 0.584 for the validation dataset (30%). The strong correlation and low error metrics confirm that Model-3 is the most reliable approach for ET₀ estimation in the semi-arid conditions of this region. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Reference Evapotranspiration (ET₀) Multiple Linear Regression (MLR) FAO-56 Penman–Monteith Climatic Parameters Semi-Arid Region Ashok Nagar Station Model Evaluation Metrics Irrigation Planning and Water Resource Management 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-7720442","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":533640772,"identity":"a3a4830b-cbbd-4a10-8fc0-7061768b85f3","order_by":0,"name":"YADVENDRA PAL SINGH","email":"data:image/png;base64,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","orcid":"","institution":"LovelyProfessional University","correspondingAuthor":true,"prefix":"","firstName":"YADVENDRA","middleName":"PAL","lastName":"SINGH","suffix":""},{"id":533640776,"identity":"65f51467-e97a-4441-ab1f-d11d92eb2750","order_by":1,"name":"A. 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Madhya","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 (ET₀), Multiple Linear Regression (MLR), FAO-56 Penman–Monteith, Climatic Parameters, Semi-Arid Region, Ashok Nagar Station, Model Evaluation Metrics, Irrigation Planning and Water Resource Management","lastPublishedDoi":"10.21203/rs.3.rs-7720442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7720442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReference evapotranspiration (ET₀) at the Ashok Nagar station was estimated using multiple linear regression (MLR) techniques. A 30-year dataset (1994\u0026ndash;2023) comprising climatological parameters such as maximum and minimum air temperatures, mean relative humidity, wind speed, and solar radiation for the Ashok Nagar district, Madhya Pradesh, India, was utilized. Observed ET₀ values, computed using the FAO-56 Penman\u0026ndash;Monteith equation, served as the dependent variable for model development in SPSS v21. Three MLR models were formulated with different combinations of independent variables: \u003cb\u003eModel-1\u003c/b\u003e included maximum and minimum temperatures, \u003cb\u003eModel-2\u003c/b\u003e incorporated maximum and minimum temperatures along with solar radiation, and \u003cb\u003eModel-3\u003c/b\u003e combined maximum and minimum temperatures, wind speed, and relative humidity. Performance evaluation was conducted by comparing the models with the Penman\u0026ndash;Monteith standard using statistical indices such as the correlation coefficient (R), coefficient of determination (R\u0026sup2;), mean absolute error (MAE), and root mean square error (RMSE). \u003cb\u003eModel-3\u003c/b\u003e exhibited superior predictive performance, with R, R\u0026sup2;, MAE, and RMSE values of \u003cb\u003e0.976, 0.952, 0.366, and 0.477\u003c/b\u003e for the calibration dataset (70%), and \u003cb\u003e0.978, 0.957, 0.449, and 0.584\u003c/b\u003e for the validation dataset (30%). The strong correlation and low error metrics confirm that Model-3 is the most reliable approach for ET₀ estimation in the semi-arid conditions of this region.\u003c/p\u003e","manuscriptTitle":"Enhanced Estimation of Reference Evapotranspiration (ET₀) through Multiple Linear Regression Approaches in Semi-Arid Regions of Madhya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:30:38","doi":"10.21203/rs.3.rs-7720442/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":"ef68c27f-6191-483a-b5dc-0a1fc29cd275","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56736742,"name":"Earth and environmental sciences/Climate sciences"},{"id":56736743,"name":"Earth and environmental sciences/Environmental sciences"},{"id":56736744,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-11-03T13:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:30:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7720442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7720442","identity":"rs-7720442","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}