Assessing Agricultural Yield Loss from Compound Extreme Events Using Three-Dimensional Vine Copulas: Evidence from Jiangsu Province

Assessing Agricultural Yield Loss from Compound Extreme Events Using Three-Dimensional Vine Copulas: Evidence from Jiangsu Province | 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 Assessing Agricultural Yield Loss from Compound Extreme Events Using Three-Dimensional Vine Copulas: Evidence from Jiangsu Province Pin Ruan, yan chen, Zhaozhong Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9339750/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 Existing bivariate copula approaches inadequately capture complex nonlinear dependencies among multiple climate stressors and crop yields, limiting precise risk quantification of compound extreme events' agricultural impacts. We developed a probabilistic framework integrating SPEI and STI via three-dimensional vine copula models to quantify compound climate risks in Jiangsu Province, providing region-specific risk estimates previously unavailable for this major rice-producing area. Using 27-year rice yield data from 58 counties in Jiangsu Province (1994–2020), we identified four compound event types (cold-dry, cold-wet, hot-dry, hot-wet) and established a data-driven yield loss threshold system. Cold-type compound events occurred more frequently (cold-dry: 0.267, cold-wet: 0.257) than hot-type events (hot-dry: 0.084, hot-wet: 0.061), reflecting Jiangsu Province's humid subtropical climate characteristics. Yield loss probabilities increased systematically with stress intensity, with water stress (SPEI) dominating impacts over temperature stress (STI). Notably, cold-wet events exhibited the highest severe loss probability (27.3%), challenging conventional assumptions that hot-dry events pose the greatest agricultural threat. These findings provide new insights into compound climate risk mechanisms and advance probabilistic assessment methods applicable to humid subtropical agricultural regions globally. Compound extreme events Vine copula model Agricultural yield risk assessment SPEI-STI joint indicators Probabilistic modeling methods Full Text Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 14 Apr, 2026 First submitted to journal 13 Apr, 2026 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. 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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-9339750","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630801167,"identity":"732b93fb-89b7-47d7-ad6d-80f242fa3c20","order_by":0,"name":"Pin Ruan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pin","middleName":"","lastName":"Ruan","suffix":""},{"id":630801168,"identity":"5881cdd7-c502-4b50-bc22-ef9085954400","order_by":1,"name":"yan chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYJACZiDmYZN/fIBBwgbETyBOixw/Q1oCg0QaCVqMJRtyDBgYiNEi33724OeCijuJGw6c+fbAIuEwAz87UO/PHbi1GJzJS5aeceZZ4oaDvdsNJIBaJHveGDD2nsGjhSHHjJm37XDihsO82yQkfxxmMLiRY8DM2IbHYf1vgFr+AbUc43kmAbLFnpAWhhsgWxoOG0v28LCBtRhIENBicOONsTTPscNy/BJsZkAt6TwSZ54VHOzF67Acw888NYeBVjA/k5ZIsJbjb0/e+OAnPochA2YJYDIAMQ4QqYGBgfED0UpHwSgYBaNgJAEAo+dNm2TqL5YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-0465-4033","institution":"Nanjing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"yan","middleName":"","lastName":"chen","suffix":""},{"id":630801169,"identity":"58afb327-969f-44f8-9b31-7a9ed05dbf77","order_by":2,"name":"Zhaozhong Feng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhaozhong","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2026-04-07 05:22:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9339750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9339750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108804911,"identity":"50e2f7b6-aeb8-4560-b2af-f1d5d6fb8d61","added_by":"auto","created_at":"2026-05-08 15:24:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":817595,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9339750/v1_covered_3ca8eded-1477-42d6-b5b4-ec3d2932a328.pdf"},{"id":108572476,"identity":"a13d55c3-af47-4d65-9ae9-cf6688576dd1","added_by":"auto","created_at":"2026-05-06 06:28:59","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1227445,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9339750/v1/3c6c43a5acc31f67f9a43d97.docx"}],"financialInterests":"","formattedTitle":"Assessing Agricultural Yield Loss from Compound Extreme Events Using Three-Dimensional Vine Copulas: Evidence from Jiangsu Province","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"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":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Compound extreme events, Vine copula model, Agricultural yield risk assessment, SPEI-STI joint indicators, Probabilistic modeling methods","lastPublishedDoi":"10.21203/rs.3.rs-9339750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9339750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExisting bivariate copula approaches inadequately capture complex nonlinear dependencies among multiple climate stressors and crop yields, limiting precise risk quantification of compound extreme events' agricultural impacts. We developed a probabilistic framework integrating SPEI and STI via three-dimensional vine copula models to quantify compound climate risks in Jiangsu Province, providing region-specific risk estimates previously unavailable for this major rice-producing area. Using 27-year rice yield data from 58 counties in Jiangsu Province (1994\u0026ndash;2020), we identified four compound event types (cold-dry, cold-wet, hot-dry, hot-wet) and established a data-driven yield loss threshold system. Cold-type compound events occurred more frequently (cold-dry: 0.267, cold-wet: 0.257) than hot-type events (hot-dry: 0.084, hot-wet: 0.061), reflecting Jiangsu Province's humid subtropical climate characteristics. Yield loss probabilities increased systematically with stress intensity, with water stress (SPEI) dominating impacts over temperature stress (STI). Notably, cold-wet events exhibited the highest severe loss probability (27.3%), challenging conventional assumptions that hot-dry events pose the greatest agricultural threat. These findings provide new insights into compound climate risk mechanisms and advance probabilistic assessment methods applicable to humid subtropical agricultural regions globally.\u003c/p\u003e","manuscriptTitle":"Assessing Agricultural Yield Loss from Compound Extreme Events Using Three-Dimensional Vine Copulas: Evidence from Jiangsu Province","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 06:28:55","doi":"10.21203/rs.3.rs-9339750/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-18T00:45:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T06:33:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T12:14:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2026-04-13T10:05:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4b44e758-b0ce-40d5-a7cc-677b6eb61ce2","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-18T00:45:35+00:00","index":0,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T06:28:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 06:28:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9339750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9339750","identity":"rs-9339750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}