A Novel Geospatial Simulation Framework for Projecting Climate Dynamics

A Novel Geospatial Simulation Framework for Projecting Climate Dynamics | 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 Novel Geospatial Simulation Framework for Projecting Climate Dynamics Abdol Rassoul Zarei, Mohammad Reza Mahmoudi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7059108/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study presents a novel geospatial approach to model, predict, and analyze climate change patterns in Iran. The methodology began with calculating the UNEP aridity index using data from 34 stations for the 1967–2024 period. Subsequently, these data were used to generate interpolation maps via the Inverse Distance Weighting (IDW) method. The study area was then discretized into approximately 41,000 pixels, for which future climatic conditions (2025–2034) were predicted using the integrated Circular Automata-Markov Chain and Log-Normal Distribution (CAMLND) model. Validation using NSE, CCC, and R² indices confirmed the robust performance of both the IDW and CAMLND models. Projections for 2025–2034 indicate a significant expansion of hyper-arid (from 10% to 20%) and humid (from 3% to 12%) zones of Iran's total area. Conversely, arid regions are anticipated to shrink by 18% and semi-arid regions by 2%, while sub-humid regions are projected to expand by 1.5%. The trend assessment extending to 2034 projects a decline in the area exhibiting a significant decreasing trend (p < 0.01), from 65.14% to 57.73%. In contrast, the analysis forecasts increases in the proportion of pixels with non-significant decreasing trends (by 4%), non-significant increasing trends (by 3%), and slight increases for significant trends at the p < 0.05 level. Collectively, these findings point to a substantial and complex transformation of Iran's climatic landscape. CA-Markov NSE UNEP Climatic change Trend Iran Full Text Additional Declarations No competing interests reported. Supplementary Files suplifile.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Nov, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 12 Jul, 2025 Submission checks completed at journal 12 Jul, 2025 First submitted to journal 06 Jul, 2025 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. 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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-7059108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492510308,"identity":"f50309a3-0c8a-4799-bc7d-49c8509803dc","order_by":0,"name":"Abdol Rassoul Zarei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie3SMWrDMBSA4WcE9vKKVgeSOwgCmUJ8FQuDp/QEHaLJXVx6m3bJ8IKHLE7nQofGiyYP6VIcELRqAgkZImfsoB+MZcGHJGQAn+8fFgJEO/tGALbuTtOpg3D7iCMJ8/O0iwzUkfypyW0bEx8E6X45HfKo/IbOzKSKqi00Swd5S2FV6hwH5ea1eSoyqTAXkGoHqSFQSBWK9/sXuFNMKpjbs9B1ktTAAkM/mHy2OjBmIRVv3cSuEjIkQhGjHYSVVHHPKgcypAzjOp+wUbEeF7EW1EOir5ZmCX+sdNCah9Ezz5qmcxB7mbvLb/s/gBP4fD6fr79fvdNPs03sRyEAAAAASUVORK5CYII=","orcid":"","institution":"Fasa University","correspondingAuthor":true,"prefix":"","firstName":"Abdol","middleName":"Rassoul","lastName":"Zarei","suffix":""},{"id":492510309,"identity":"f8039ae7-2406-45f7-9a3c-2eefc8a8209f","order_by":1,"name":"Mohammad Reza Mahmoudi","email":"","orcid":"","institution":"Fasa University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Reza","lastName":"Mahmoudi","suffix":""}],"badges":[],"createdAt":"2025-07-06 16:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7059108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7059108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88024280,"identity":"a050c067-4e25-4865-b54f-a915e770600e","added_by":"auto","created_at":"2025-07-31 14:25:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1641267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptall.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7059108/v1_covered_12e31d32-294c-491b-be75-47b7175576a4.pdf"},{"id":88021880,"identity":"e7c080c5-4936-40cc-b01d-c15501c177a9","added_by":"auto","created_at":"2025-07-31 14:01:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15992,"visible":true,"origin":"","legend":"","description":"","filename":"suplifile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7059108/v1/4b69fa4ab9536973b260ed5e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Geospatial Simulation Framework for Projecting Climate Dynamics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CA-Markov, NSE, UNEP, Climatic change, Trend, Iran","lastPublishedDoi":"10.21203/rs.3.rs-7059108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7059108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a novel geospatial approach to model, predict, and analyze climate change patterns in Iran. The methodology began with calculating the UNEP aridity index using data from 34 stations for the 1967–2024 period. Subsequently, these data were used to generate interpolation maps via the Inverse Distance Weighting (IDW) method. The study area was then discretized into approximately 41,000 pixels, for which future climatic conditions (2025–2034) were predicted using the integrated Circular Automata-Markov Chain and Log-Normal Distribution (CAMLND) model. Validation using NSE, CCC, and R² indices confirmed the robust performance of both the IDW and CAMLND models. Projections for 2025–2034 indicate a significant expansion of hyper-arid (from 10% to 20%) and humid (from 3% to 12%) zones of Iran's total area. Conversely, arid regions are anticipated to shrink by 18% and semi-arid regions by 2%, while sub-humid regions are projected to expand by 1.5%. The trend assessment extending to 2034 projects a decline in the area exhibiting a significant decreasing trend (p \u0026lt; 0.01), from 65.14% to 57.73%. In contrast, the analysis forecasts increases in the proportion of pixels with non-significant decreasing trends (by 4%), non-significant increasing trends (by 3%), and slight increases for significant trends at the p \u0026lt; 0.05 level. 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