Modeling the Carbon Landscape of Northwest China: From Climatic Controls to Vegetation-Driven Hotspots

Modeling the Carbon Landscape of Northwest China: From Climatic Controls to Vegetation-Driven Hotspots | 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 the Carbon Landscape of Northwest China: From Climatic Controls to Vegetation-Driven Hotspots Hongwei Zhang, Qiang Bie, B Yao, Huajun Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7155963/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 Background As a sensitive response area to global climate change, the terrestrial ecosystem carbon sink function of the Northwest Arid Region of China is not only a core element in constructing the regional ecological security barrier but also a crucial strategic fulcrum for China to achieve its dual carbon goals. However, the spatial distribution and driving factors of multiple carbon density types in this region remain unclear. The research provides a scientific basis for optimizing ecological barrier construction and formulating gradient-based carbon sink management strategies in arid regions. Methodology Five types of carbon density data—including aboveground and belowground biomass carbon, soil organic and inorganic carbon, and dead organic matter carbon—were obtained through literature review and field investigations. Environmental drivers were analyzed using generalized dissimilarity modeling (GDM) and structural equation modeling (SEM), and spatial simulations were performed using three machine learning models: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Results Climatic and soil factors were the primary drivers of carbon density variation. Among the models, XGBoost demonstrated the best performance in simulating all five types of carbon density. Spatially, high carbon density values were mainly concentrated in mountainous and oasis areas, while low values were found in the southern desert regions. Vegetation cover and precipitation were identified as dominant regulating factors. Conclusions Forest ecosystems play a central role in regional carbon storage. The findings offer a scientific foundation for enhancing ecological barrier construction and developing gradient-based carbon sink management strategies in arid regions. Carbon density Interpolation analysis Multi-model Northwest Arid Region Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Jan, 2026 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 25 Aug, 2025 Editor assigned by journal 21 Jul, 2025 Submission checks completed at journal 21 Jul, 2025 First submitted to journal 18 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. 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-7155963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507550091,"identity":"fd138488-8e6e-4adb-9ad7-be0f18e9468e","order_by":0,"name":"Hongwei Zhang","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Zhang","suffix":""},{"id":507550092,"identity":"1666c65d-e100-4c85-86e0-3c8b45fe8474","order_by":1,"name":"Qiang Bie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACA2Yg8aGAgRnM4yFWC+MMAwZmHuK1ADEzjwFUNVFazNmZD962Mahjt5dIYHzwto1B3pyQFstmtmTrHAM2Zh6JBGbDuW0MhjsbCDnsMI+ZdI4BD0gLmzRvG0OCwQGCWvi/SVsYSIC0sP8mUgsPmzSDgQHYFmaitAD9YmzZY5DAzHPmYbPknHMShhsIaTHnP/zwxo+KumT29uSDH96U2cgTtAUEJIA4GRijDVA2kVrsiFM6CkbBKBgFIxIAAM9lMMAXD48eAAAAAElFTkSuQmCC","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Bie","suffix":""},{"id":507550093,"identity":"2414b455-5686-4d06-9665-011c1f1779d6","order_by":2,"name":"B Yao","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"","lastName":"Yao","suffix":""},{"id":507550094,"identity":"67365ccf-a925-4ced-b8d5-b337ba1f3c21","order_by":3,"name":"Huajun Liang","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Huajun","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-07-18 09:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7155963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7155963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90438147,"identity":"8628d14a-d8d1-4558-8bb5-bfde7d3831ad","added_by":"auto","created_at":"2025-09-02 17:28:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1375526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7155963/v1_covered_8aeb945f-6b92-4037-a1ab-0140bcfae2a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling the Carbon Landscape of Northwest China: From Climatic Controls to Vegetation-Driven Hotspots","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":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carbon density, Interpolation analysis, Multi-model, Northwest Arid Region","lastPublishedDoi":"10.21203/rs.3.rs-7155963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7155963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e As a sensitive response area to global climate change, the terrestrial ecosystem carbon sink function of the Northwest Arid Region of China is not only a core element in constructing the regional ecological security barrier but also a crucial strategic fulcrum for China to achieve its dual carbon goals. However, the spatial distribution and driving factors of multiple carbon density types in this region remain unclear. The research provides a scientific basis for optimizing ecological barrier construction and formulating gradient-based carbon sink management strategies in arid regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e Five types of carbon density data—including aboveground and belowground biomass carbon, soil organic and inorganic carbon, and dead organic matter carbon—were obtained through literature review and field investigations. 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