High-Resolution Forecasting of Soil Thermal Regimes Using Different Deep Learning Frameworks under Climate Change

High-Resolution Forecasting of Soil Thermal Regimes Using Different Deep Learning Frameworks under Climate Change | 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 Article High-Resolution Forecasting of Soil Thermal Regimes Using Different Deep Learning Frameworks under Climate Change Mehri Saeidinia, Amir Hamzeh Haghiabi, Mohammad Nazeri Tahroudi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7642696/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Soil temperature is a critical parameter influencing ecological and hydrological processes, yet its accurate projection under climate change remains challenging due to coarse-resolution climate models and complex soil-atmosphere interactions. This study develops a deep learning framework to downscale soil temperature (5 cm depth) in western Iran, under climate change scenarios. Using Kendall’s Tau statistic, we identified optimal predictors from 26 CanESM5 (CMIP6) variables, selecting those with strong monotonic relationships (τ > 0.4) with observed soil temperature. Four deep learning models—CNN, LSTM, GRU, and a hybrid CNN-LSTM—were evaluated for downscaling performance using historical data (1980–2014). The hybrid CNN-LSTM model outperformed others, achieving the highest accuracy (NSE > 89%, RMSE < 3.94°C) by capturing spatial and temporal dependencies in soil thermal dynamics. Validation (2015–2020) revealed regional climate patterns: western stations, more arid and warming-sensitive, aligned with SSP245/SSP370, while eastern stations, influenced by the Zagros Mountains, showed cooling and precipitation feedbacks favoring SSP119/SSP126. Future projections under SSP126, SSP245, and SSP585 scenarios indicated nonlinear soil temperature responses, with high-emission pathways (SSP585) causing initial cooling (-4.11°C by 2040) followed by accelerated warming (+ 2.09°C by 2100). Elevation played a critical role, with high-altitude stations exhibiting persistent warming (+ 4.02°C) contrary to cooling trends at lower elevations. The model’s robust validation (Kendall's τ = 0.72–0.80) confirms its reliability for climate projection studies, bridging the resolution gap between global models and local conditions to support agricultural and ecosystem management. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Soil temperature downscaling Deep learning CNN-LSTM Climate change scenarios CMIP6 SSPs Full Text Additional Declarations No competing interests reported. Supplementary Files Appendixs.docx Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers invited by journal 27 Sep, 2025 Editor assigned by journal 27 Sep, 2025 Editor invited by journal 23 Sep, 2025 Submission checks completed at journal 21 Sep, 2025 First submitted to journal 21 Sep, 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. 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-7642696","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":526231256,"identity":"6b2daef9-092c-4ec8-b624-442a6204e066","order_by":0,"name":"Mehri 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Change","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soil temperature downscaling, Deep learning, CNN-LSTM, Climate change scenarios, CMIP6, SSPs","lastPublishedDoi":"10.21203/rs.3.rs-7642696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7642696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil temperature is a critical parameter influencing ecological and hydrological processes, yet its accurate projection under climate change remains challenging due to coarse-resolution climate models and complex soil-atmosphere interactions. This study develops a deep learning framework to downscale soil temperature (5 cm depth) in western Iran, under climate change scenarios. Using Kendall\u0026rsquo;s Tau statistic, we identified optimal predictors from 26 CanESM5 (CMIP6) variables, selecting those with strong monotonic relationships (τ\u0026thinsp;\u0026gt;\u0026thinsp;0.4) with observed soil temperature. Four deep learning models\u0026mdash;CNN, LSTM, GRU, and a hybrid CNN-LSTM\u0026mdash;were evaluated for downscaling performance using historical data (1980\u0026ndash;2014). The hybrid CNN-LSTM model outperformed others, achieving the highest accuracy (NSE\u0026thinsp;\u0026gt;\u0026thinsp;89%, RMSE\u0026thinsp;\u0026lt;\u0026thinsp;3.94\u0026deg;C) by capturing spatial and temporal dependencies in soil thermal dynamics. Validation (2015\u0026ndash;2020) revealed regional climate patterns: western stations, more arid and warming-sensitive, aligned with SSP245/SSP370, while eastern stations, influenced by the Zagros Mountains, showed cooling and precipitation feedbacks favoring SSP119/SSP126. Future projections under SSP126, SSP245, and SSP585 scenarios indicated nonlinear soil temperature responses, with high-emission pathways (SSP585) causing initial cooling (-4.11\u0026deg;C by 2040) followed by accelerated warming (+\u0026thinsp;2.09\u0026deg;C by 2100). Elevation played a critical role, with high-altitude stations exhibiting persistent warming (+\u0026thinsp;4.02\u0026deg;C) contrary to cooling trends at lower elevations. The model\u0026rsquo;s robust validation (Kendall's τ\u0026thinsp;=\u0026thinsp;0.72\u0026ndash;0.80) confirms its reliability for climate projection studies, bridging the resolution gap between global models and local conditions to support agricultural and ecosystem management.\u003c/p\u003e","manuscriptTitle":"High-Resolution Forecasting of Soil Thermal Regimes Using Different Deep Learning Frameworks under Climate Change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 14:56:18","doi":"10.21203/rs.3.rs-7642696/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-09T12:00:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T17:45:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T09:52:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318451670388125636134883915403293796782","date":"2025-09-30T10:17:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207781652716762771919574608654857410875","date":"2025-09-29T01:11:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-28T01:46:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-28T01:39:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-23T12:38:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-21T10:33:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-21T10:28:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08711072-cbd5-4506-be6c-e6d914172951","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55930414,"name":"Earth and environmental sciences/Climate sciences"},{"id":55930415,"name":"Biological sciences/Ecology"},{"id":55930416,"name":"Earth and environmental sciences/Ecology"},{"id":55930417,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55930418,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2026-02-09T16:03:15+00:00","versionOfRecord":{"articleIdentity":"rs-7642696","link":"https://doi.org/10.1038/s41598-026-38496-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-05 15:59:41","publishedOnDateReadable":"February 5th, 2026"},"versionCreatedAt":"2025-10-09 14:56:18","video":"","vorDoi":"10.1038/s41598-026-38496-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-38496-6","workflowStages":[]},"version":"v1","identity":"rs-7642696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7642696","identity":"rs-7642696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}