Modeling Climate Change-Induced Risk Through Tipping Points, Stressors, Resilience, and Bifurcation: A Non-Autonomous Dynamical Systems Approach Using CMIP6

Modeling Climate Change-Induced Risk Through Tipping Points, Stressors, Resilience, and Bifurcation: A Non-Autonomous Dynamical Systems Approach Using CMIP6 | 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 Modeling Climate Change-Induced Risk Through Tipping Points, Stressors, Resilience, and Bifurcation: A Non-Autonomous Dynamical Systems Approach Using CMIP6 Hasan TATLI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6805350/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 Climate change is accelerating the intensity and frequency of droughts, particularly threatening the resilience of semi-arid socio-ecological systems such as those in the eastern Mediterranean. Traditional drought risk assessments often overlook the complex, non-linear dynamics and abrupt transitions driven by interacting climate stressors and system feedbacks. In this study, we present a non-autonomous dynamical systems model based on coupled Ordinary Differential Equations (ODEs) to quantify climate change-induced risk. The model formalizes the Ecological-Climatic Strain Index (ECSI) as a function of four normalized state variables: Climate Stressor (CS), Resilience Capacity (RC), Tipping Point Probability (TPP), and Bifurcation Transition Index (BTI). By employing non-linear functions and conditional regimes, the model captures threshold behaviors, regime bifurcations, and critical transitions, including Normal, Crisis, Recovery, and Transformative Crisis states. To test the model’s applicability, we applied it to Türkiye using CMIP6-derived SPEI-12 data under the SSP5-8.5 scenario (2015–2100). The results reveal widespread increases in CS and TPP, particularly after 2050, signaling heightened exposure to abrupt ecological transitions. Meanwhile, RC declines across all grids, indicating diminishing adaptive capacity, while ECSI intensifies sharply by the late 21st century—highlighting rising systemic fragility. Spatial heterogeneity is also evident: Grid 16 demonstrates early and severe collapse trajectories, while Grid 33 exhibits relatively greater resilience. These findings illustrate how integrating tipping dynamics, resilience loss, and bifurcation signals into risk modeling can enable earlier detection of critical transitions and inform proactive, location-specific adaptation strategies. This modeling framework offers a transferable tool for assessing climate risk in drought-prone systems globally. Drought Dynamical Systems Ecological-Climatic Strain Tipping Points Resilience Climate Change Adaptation Türkiye 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-6805350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475117723,"identity":"62ad95a7-68d0-44a3-9c12-5a1addbda585","order_by":0,"name":"Hasan TATLI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYHACxgMJFVAmDzHqeRiYGQ4knEFokSBOC2MbKVrs2c8fOPBwnp2c7owExgdv2xjqzBsI2cKTzHAgcVuysdmNBGbDuW0MEjIHCDoMrAWIbiSwSfMCtRB0GQ//Y6CWOWAt7L+J0yIBsqUBYgszcVpuPDY4kHAM6JczD5sl55yTkJxBSAt7f+LDhz9q7OTMjicf/PCmzIafcCgjAGMDAzHRMgpGwSgYBaOACAAAP5w75lgOHioAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1960-0618","institution":"Canakkale Onsekiz Mart University","correspondingAuthor":true,"prefix":"","firstName":"Hasan","middleName":"","lastName":"TATLI","suffix":""}],"badges":[],"createdAt":"2025-06-02 21:56:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6805350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6805350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100363522,"identity":"535eca81-be11-4735-ad65-9ef5ee91fb00","added_by":"auto","created_at":"2026-01-16 07:49:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4008523,"visible":true,"origin":"","legend":"","description":"","filename":"BlindedArticleTatli.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6805350/v1_covered_6e6b89c6-4d5d-4fc3-98be-47185fab5fb4.pdf"}],"financialInterests":"","formattedTitle":"Modeling Climate Change-Induced Risk Through Tipping Points, Stressors, Resilience, and Bifurcation: A Non-Autonomous Dynamical Systems Approach Using CMIP6","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":true,"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":"Drought, Dynamical Systems, Ecological-Climatic Strain, Tipping Points, Resilience, Climate Change Adaptation, Türkiye","lastPublishedDoi":"10.21203/rs.3.rs-6805350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6805350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change is accelerating the intensity and frequency of droughts, particularly threatening the resilience of semi-arid socio-ecological systems such as those in the eastern Mediterranean. Traditional drought risk assessments often overlook the complex, non-linear dynamics and abrupt transitions driven by interacting climate stressors and system feedbacks. In this study, we present a non-autonomous dynamical systems model based on coupled Ordinary Differential Equations (ODEs) to quantify climate change-induced risk. The model formalizes the Ecological-Climatic Strain Index (ECSI) as a function of four normalized state variables: Climate Stressor (CS), Resilience Capacity (RC), Tipping Point Probability (TPP), and Bifurcation Transition Index (BTI). By employing non-linear functions and conditional regimes, the model captures threshold behaviors, regime bifurcations, and critical transitions, including Normal, Crisis, Recovery, and Transformative Crisis states. To test the model’s applicability, we applied it to Türkiye using CMIP6-derived SPEI-12 data under the SSP5-8.5 scenario (2015–2100). 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