Sensitivity Analysis of Potential Evapotranspiration in Northeast China

Sensitivity Analysis of Potential Evapotranspiration in Northeast China | 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 Sensitivity Analysis of Potential Evapotranspiration in Northeast China Sen Liu, Mingqian Li, He Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6600987/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 Based on meteorological data from 106 stations in Northeast China from 1960 to 2020, this study calculated potential evapotranspiration ( ET₀ ) using the Penman-Monteith formula. The research area was divided into four subregions using the K-means clustering method, revealing the sensitivity responses and contribution rates of different meteorological factors to ET₀ changes under climate change. The results are as follows: Daily mean temperature (TEM) significantly increased in all four subregions, while wind speed (WIND) significantly decreased. Relative humidity (RH) and sunshine duration (SSD) showed slight declining trends. ET₀ changes exhibited regional differences: a slight decrease in Subregion I and eastern Subregion II, a significant increase in western Subregion II, no significant trend in Subregion III, and a significant increase in Subregion IV. Sensitivity coefficients for all subregions were higher in the south and lower in the north. The sensitivity coefficient for RH was negative, while those for sunshine duration and WIND were positive. Mean temperature sensitivity coefficients were positive except in Subregion IV, where the anomaly may be regulated by high-latitude factors. The decline in RH was the dominant factor driving ET₀ changes in Subregions III and IV. In Subregions I and II, ET₀ changes may also be influenced by non-climatic factors or other meteorological variables. This study clarifies the dominant driving factors of evapotranspiration changes in each region, providing a theoretical basis for optimizing agro-hydrological models in cold regions and supporting regional agricultural water consumption assessment and water resource optimization. Potential evapotranspiration K-means clustering analysis Sensitivity analysis Contribution rate Northeast China 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-6600987","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456076158,"identity":"b216be82-c21c-4a13-b38a-35baf450314e","order_by":0,"name":"Sen Liu","email":"","orcid":"","institution":"Institute of Disaster Prevention","correspondingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Liu","suffix":""},{"id":456076159,"identity":"4a1c4824-1fc6-4cb4-9c19-730e950c15f4","order_by":1,"name":"Mingqian Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3QsWrCQBzH8ZODc/nRrP9A0K3zQcHFgIOrD5EjEJcWHDM4HKQ0g4oP4Es4OkaEc0k7Z4yPcJtTaTu3eHHrcJ/5vtz9jjHP+4dEvzhb+xkj6BfHNsmX7uQBJuMksihcm1S2tXEnA3oeMYhTLJvZY3h55R0ehmrUEk5gDUSutGBBuUocW/RcLmiO3m5jGnWIGNXve9ctFZEcg0cfWaNqwSS9OBJSmpBwiO9RC/XGuySpIFRT4OcfuiUw/CnUGQgmpaQ2cG4Zllt7sTqeTM7F0V7z5SAoN7eTX3Dfcc/zPO9PX7QaRzxsAmPsAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Disaster Prevention","correspondingAuthor":true,"prefix":"","firstName":"Mingqian","middleName":"","lastName":"Li","suffix":""},{"id":456076160,"identity":"b0331b0d-ece7-4c80-bf65-eae85eaf5ae5","order_by":2,"name":"He Wang","email":"","orcid":"","institution":"Institute of Disaster Prevention","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-06 08:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6600987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6600987/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83910390,"identity":"6eab73eb-c351-4841-939f-78041184da0c","added_by":"auto","created_at":"2025-06-04 11:32:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1446480,"visible":true,"origin":"","legend":"","description":"","filename":"SensitivityAnalysisofPotentialEvapotranspirationinNortheastChinaF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6600987/v1_covered_67dac121-7f18-47e6-8cd7-53376791c860.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sensitivity Analysis of Potential Evapotranspiration in Northeast China","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":"Potential evapotranspiration, K-means clustering analysis, Sensitivity analysis, Contribution rate, Northeast China","lastPublishedDoi":"10.21203/rs.3.rs-6600987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6600987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on meteorological data from 106 stations in Northeast China from 1960 to 2020, this study calculated potential evapotranspiration (\u003cem\u003eET₀\u003c/em\u003e) using the Penman-Monteith formula. The research area was divided into four subregions using the K-means clustering method, revealing the sensitivity responses and contribution rates of different meteorological factors to \u003cem\u003eET₀\u003c/em\u003e changes under climate change. The results are as follows: Daily mean temperature (TEM) significantly increased in all four subregions, while wind speed (WIND) significantly decreased. Relative humidity (RH) and sunshine duration (SSD) showed slight declining trends. \u003cem\u003eET₀\u003c/em\u003e changes exhibited regional differences: a slight decrease in Subregion I and eastern Subregion II, a significant increase in western Subregion II, no significant trend in Subregion III, and a significant increase in Subregion IV. Sensitivity coefficients for all subregions were higher in the south and lower in the north. The sensitivity coefficient for RH was negative, while those for sunshine duration and WIND were positive. Mean temperature sensitivity coefficients were positive except in Subregion IV, where the anomaly may be regulated by high-latitude factors. The decline in RH was the dominant factor driving \u003cem\u003eET₀\u003c/em\u003e changes in Subregions III and IV. In Subregions I and II, \u003cem\u003eET₀\u003c/em\u003e changes may also be influenced by non-climatic factors or other meteorological variables. This study clarifies the dominant driving factors of evapotranspiration changes in each region, providing a theoretical basis for optimizing agro-hydrological models in cold regions and supporting regional agricultural water consumption assessment and water resource optimization.\u003c/p\u003e","manuscriptTitle":"Sensitivity Analysis of Potential Evapotranspiration in Northeast China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 02:55:17","doi":"10.21203/rs.3.rs-6600987/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":"8bff2c31-da4a-4cb7-913b-5a5827800d74","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T11:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 02:55:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6600987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6600987","identity":"rs-6600987","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}