Estimating Regional Terrestrial Ecosystem Carbon Sinks on Multi-Model Coupling Approach

Estimating Regional Terrestrial Ecosystem Carbon Sinks on Multi-Model Coupling Approach | 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 Estimating Regional Terrestrial Ecosystem Carbon Sinks on Multi-Model Coupling Approach qing zhou Lv, Hui Yang, Jia Wang, Gefei Feng, Wanzeng Liu, Yunhui Zhang, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5323899/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 regional terrestrial ecosystems serve as primary carbon sinks, characterized by strong spatial heterogeneity and significant interannual fluctuations. In Xinjiang, one of China's five autonomous regions, carbon storage increased from 12,967.89 TG to 14,262.31 TG. Traditional carbon sink assessment methods struggle to fully account for the combined impacts of human activities and environmental factors, impeding accurate depiction of the spatial distribution and evolution of regional carbon stocks. This study proposes a regional terrestrial ecosystem carbon density estimation method based on an ARIMA-CatBoost-RNN coupled model. Firstly, the ARIMA model forecasts carbon density time series, the CatBoost model reduces the impacts of spatial heterogeneity, and the RNN model estimates ecosystem carbon density values. Secondly, terrestrial carbon storage is estimated using an improved InVEST model, with an accuracy of up to 78.4%. Finally, the Geodetector model quantifies the influence of nine driving factors on carbon sink capacity. The results reveal that soil carbon stocks comprise 55%-61% of total carbon storage, making them the main component of Xinjiang's terrestrial ecosystems. Annual average carbon sequestration is 39.02 T/km², with forests showing the highest capacity at 103.33 T/km². NDVI(Normalized Difference Vegetation Index) has the most significant impact on Xinjiang's carbon sink capacity, contributing up to 0.615. Earth and environmental sciences/Ecology/Ecosystem ecology Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Ecological modelling Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx 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. 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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-5323899","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376576033,"identity":"e7f7c9b1-2cb6-4261-9429-91a9e97eaff3","order_by":0,"name":"qing zhou 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In Xinjiang, one of China's five autonomous regions, carbon storage increased from 12,967.89 TG to 14,262.31 TG. Traditional carbon sink assessment methods struggle to fully account for the combined impacts of human activities and environmental factors, impeding accurate depiction of the spatial distribution and evolution of regional carbon stocks. This study proposes a regional terrestrial ecosystem carbon density estimation method based on an ARIMA-CatBoost-RNN coupled model. Firstly, the ARIMA model forecasts carbon density time series, the CatBoost model reduces the impacts of spatial heterogeneity, and the RNN model estimates ecosystem carbon density values. Secondly, terrestrial carbon storage is estimated using an improved InVEST model, with an accuracy of up to 78.4%. Finally, the Geodetector model quantifies the influence of nine driving factors on carbon sink capacity. The results reveal that soil carbon stocks comprise 55%-61% of total carbon storage, making them the main component of Xinjiang's terrestrial ecosystems. Annual average carbon sequestration is 39.02 T/km\u0026sup2;, with forests showing the highest capacity at 103.33 T/km\u0026sup2;. NDVI(Normalized Difference Vegetation Index) has the most significant impact on Xinjiang's carbon sink capacity, contributing up to 0.615.\u003c/p\u003e","manuscriptTitle":"Estimating Regional Terrestrial Ecosystem Carbon Sinks on Multi-Model Coupling Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 16:50:08","doi":"10.21203/rs.3.rs-5323899/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":"a1e5265a-be0e-49a1-aed4-e32cc80a5f1e","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40083262,"name":"Earth and environmental sciences/Ecology/Ecosystem ecology"},{"id":40083263,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":40083264,"name":"Earth and environmental sciences/Ecology/Ecological modelling"}],"tags":[],"updatedAt":"2025-03-26T08:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 16:50:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5323899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5323899","identity":"rs-5323899","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}