Assessment of Rainfall And Temperature Trends in The Yellow River Basin, China from 2023 to 2100

Assessment of Rainfall And Temperature Trends in The Yellow River Basin, China from 2023 to 2100 | 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 Assessment of Rainfall And Temperature Trends in The Yellow River Basin, China from 2023 to 2100 Shengqi Jian, Qinghao Pei, Xin Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3974657/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 China's Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB's ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations of precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5°×0.5° (i.e., about 55 km×55 km), which is improved to 1 km×1 km by the downscaling of Delta, and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 ℃/10a, 0.3 ℃/10a, 0.5 ℃/10a, and 0.7 ℃/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast-northwest. climate model optimization Delta downscaling EOF precipitation temperature 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-3974657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274022600,"identity":"5a8d603d-176a-4804-b265-46e5feb935a9","order_by":0,"name":"Shengqi Jian","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shengqi","middleName":"","lastName":"Jian","suffix":""},{"id":274022601,"identity":"38e15c5f-5940-4c9d-8b09-684e8a6207bc","order_by":1,"name":"Qinghao Pei","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Qinghao","middleName":"","lastName":"Pei","suffix":""},{"id":274022602,"identity":"5326b951-b7ad-4e1e-9933-d0a714b65901","order_by":2,"name":"Xin Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie2Rv0rEQBCHdxnINWvSbjDgPcJI4LA4TnwOm5FAqpO7BwgYODibgO2BLyH4AisDuebANmVEsI7YWFiYpLByk/aK/cplvv3NHyEcjiPFCMwWwSQnQ98ZBABcjyvrMgkL82Y+i3IS3nspjuc0IJ+qq/eXnQcBvqqpHirGannOa/RAVECslHcasxIosvm1XUmJdxj58nHbKhdRPOMTU4syvc2tSmJYtSkQHfqUZMY+ocx5QLnJWwXkVq8aVh7cPW8U6mGlTwFZaKJufEAYUcLDR9sPlolWhrolg+Z2yTQwi79fxl/qJ1tc7vOk6U/5wFw32dyqTM2/z2Qp7ziz/eVwOByOP34B/Udh4mCcEhIAAAAASUVORK5CYII=","orcid":"","institution":"Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-02-21 05:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3974657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3974657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54473483,"identity":"59ad8e8d-16ff-4a71-9989-195899db8d53","added_by":"auto","created_at":"2024-04-11 05:52:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1490851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3974657/v1_covered_5c6ffabb-296d-4c6c-82a1-256414f2a1e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Rainfall And Temperature Trends in The Yellow River Basin, China from 2023 to 2100","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":"climate model optimization, Delta downscaling, EOF, precipitation, temperature","lastPublishedDoi":"10.21203/rs.3.rs-3974657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3974657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChina's Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB's ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations of precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5\u0026deg;\u0026times;0.5\u0026deg; (i.e., about 55 km\u0026times;55 km), which is improved to 1 km\u0026times;1 km by the downscaling of Delta, and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 ℃/10a, 0.3 ℃/10a, 0.5 ℃/10a, and 0.7 ℃/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast-northwest.\u003c/p\u003e","manuscriptTitle":"Assessment of Rainfall And Temperature Trends in The Yellow River Basin, China from 2023 to 2100","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 09:01:38","doi":"10.21203/rs.3.rs-3974657/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":"448f2bf7-415a-4c87-aba2-3716899bd1e5","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-11T05:44:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-22 09:01:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3974657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3974657","identity":"rs-3974657","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}