A Bayesian approach for testing drought intensity trends using daily SPI data: The case of China

A Bayesian approach for testing drought intensity trends using daily SPI data: The case of 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 A Bayesian approach for testing drought intensity trends using daily SPI data: The case of China Georgios Tsiotas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6152589/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 The standardized precipitation index (SPI), is one of the mostly used drought indicators for assessing drought duration and intensity. Drought intensity and its trends due to its effect on eco-system has attracted many researchers in fields such as meteorology, agriculture and hydrology. Here, we introduce a Bayesian trend analysis by fitting alternative stochastic-trend models based on the Normal, the Generalized Extreme Value and the Gumbel distributions. Our intention is to test the time-trend assumption in the mean and variance. We apply the above methodology using a new multi-scale daily SPI dataset in mainland China from 1961 to 2018. Based on these data series, we derive the annual total drought intensity (ATDS) for several station in east and south of China. Posterior simulations results based on the best fitting model reveal the locations and time-periods with the strongest and the weakest inter-annual trends for various aspects of ATDS's distribution. Furthermore, a model comparison experiment based on Bayes Factors compares the above stochastic-trend models with the corresponding ones with no-time trends. The results are very significant since they reveal the locations and the regions where inter-annual trends for ATDS have real grounds. Bayesian trend analysis Drought index Standardized precipitation index Climate change Bayes factors China Full Text 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-6152589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436330786,"identity":"79edacb1-c89b-4f28-b2ec-ada370ac7058","order_by":0,"name":"Georgios Tsiotas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFACxgYQKUe8Bh6QlgMMDMakaAECoJbEBqK12PMfbnz8oaYufcPx5geMX/cQY4tEYrPBgWOHczecOWbALPOMKC2MbRIH2A7kbriRw8AscYAYLfwHgVr+1aUbEK+FIbFN4mAbcwJIC+MHorTcAPrlbN9hw5lAvxxmIEYLe//xhw8qvtXJ8x1vfvjwBzFaUMBhHlJ1MDD+IFnLKBgFo2AUjAQAAIXZOjeJszxEAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1752-724X","institution":"University of Crete - Rethymno Campus: Panepistemio Kretes","correspondingAuthor":true,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Tsiotas","suffix":""}],"badges":[],"createdAt":"2025-03-04 08:50:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6152589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6152589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82802735,"identity":"35721f4d-5c60-4a6b-b2f3-1fb2d25fe687","added_by":"auto","created_at":"2025-05-15 11:40:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4448495,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.tsiotas.china.2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6152589/v1_covered_a92965ad-7019-4318-957b-54a17aa8f6f7.pdf"}],"financialInterests":"","formattedTitle":"A Bayesian approach for testing drought intensity trends using daily SPI data: The case of 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":"Bayesian trend analysis, Drought index, Standardized precipitation index, Climate change, Bayes factors, China","lastPublishedDoi":"10.21203/rs.3.rs-6152589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6152589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The standardized precipitation index (SPI), is one of the mostly used drought indicators for assessing drought duration and intensity. Drought intensity and its trends due to its effect on eco-system has attracted many researchers in fields such as meteorology, agriculture and hydrology. Here, we introduce a Bayesian trend analysis by fitting alternative stochastic-trend models based on the Normal, the Generalized Extreme Value and the Gumbel distributions. Our intention is to test the time-trend assumption in the mean and variance. We apply the above methodology using a new multi-scale daily SPI dataset in mainland China from 1961 to 2018. Based on these data series, we derive the annual total drought intensity (ATDS) for several station in east and south of China. Posterior simulations results based on the best fitting model reveal the locations and time-periods with the strongest and the weakest inter-annual trends for various aspects of ATDS's distribution. Furthermore, a model comparison experiment based on Bayes Factors compares the above stochastic-trend models with the corresponding ones with no-time trends. The results are very significant since they reveal the locations and the regions where inter-annual trends for ATDS have real grounds.","manuscriptTitle":"A Bayesian approach for testing drought intensity trends using daily SPI data: The case of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 19:41:18","doi":"10.21203/rs.3.rs-6152589/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":"1a7da3fe-f7bd-4c85-a549-aee93cc4ca49","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-15T11:32:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-03 19:41:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6152589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6152589","identity":"rs-6152589","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}