Investigating Below-Canopy CO₂ Accumulation with Turbulence-Based Filtering in a Subtropical Forest

Investigating Below-Canopy CO₂ Accumulation with Turbulence-Based Filtering in a Subtropical Forest | 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 Investigating Below-Canopy CO₂ Accumulation with Turbulence-Based Filtering in a Subtropical Forest Kuan-Ying Chen, Ming-Hsu Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8869933/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Accurate quantification of Net Ecosystem Exchange (NEE) in forests is essential to understand their role in sequestering anthropogenic CO 2 emissions and supporting climate mitigation. The CO 2 flux measured by the above-canopy Eddy-Covariance (EC) provides ground-truth observations worldwide. However, the applicability of above-canopy EC is affected by below-canopy CO₂ accumulation due to decoupling that frequently occurs under nocturnal or weak-turbulence conditions, often resulting in unrealistically high estimates of ecosystem respiration from above-canopy. An above-canopy EC, complemented by below-canopy wind and CO₂ concentration profile measurements, was integrated to investigate the contribution of below-canopy accumulation to above-canopy CO₂ flux. This study evaluates the effectiveness of the standard deviation of vertical wind speed (σ w ) as an alternative turbulence indicator to the conventional friction velocity (u*) threshold. Our results indicate that σ w serves as a direct proxy for vertical mixing efficiency and offers superior detection of decoupling events. Optimal thresholds were objectively identified using knee-point analysis to maximize the trade-off between the Success Rate (SR), defined as the percentage of decoupling events relative to all events, and the Data Retention (DR), defined as the percentage of remaining data relative to all data. The determined σ w threshold of 0.22 m/s outperformed the u* threshold of 0.20 m/s, achieving higher SR (79.8% vs. 72.0%) and better DR (81.4% vs. 74.6%) values. This confirms that σ w effectively minimizes the inclusion of decoupled data while preserving more valid observations, a finding further corroborated by analyzing vertical CO₂ profiles. To address gaps in the filtered dataset, five gap-filling strategies were evaluated using Monte Carlo simulations. The Support Vector Regression (SVR) algorithm proved to be most effective, outperforming traditional Marginal Distribution Sampling (MDS) and other machine learning algorithms by capturing nonlinear interactions between environmental drivers and CO 2 flux. The integration of σ w -based filtering (σ w = 0.22 m/s) with the SVR gap-filling produced an annual CO₂ flux estimate that was 17.9% lower than the case without turbulence filtering. These findings highlight that standard filtering protocols are essential to prevent overestimating CO₂ flux from below-canopy accumulation under weak-turbulence conditions. Eddy-covariance Carbon dioxide flux Turbulence indicator Gap-filling Evergreen broadleaf forest Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 14 Feb, 2026 Submission checks completed at journal 14 Feb, 2026 First submitted to journal 13 Feb, 2026 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-8869933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591348216,"identity":"7e70be43-8098-458e-8874-fb10f37ff5e2","order_by":0,"name":"Kuan-Ying Chen","email":"","orcid":"","institution":"National Central University","correspondingAuthor":false,"prefix":"","firstName":"Kuan-Ying","middleName":"","lastName":"Chen","suffix":""},{"id":591348217,"identity":"ffe9b106-cd10-4e36-a5ec-ed89b549a001","order_by":1,"name":"Ming-Hsu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACdsbGB3AOD1FamBmbDZC1SBChhYENoYooLbrNzG0VP2oY8vhnJDA+eNvGUGdwgIAWs8OMbTd7jjEUS9xIYDac28YgQZSW24wNDIkbJBLYpHmBWsyI0VIM1cL+m2gtzDBbmInV0izZc0wiccaZh82Sc85JSO4nqOV4+8MPP2psEvvbkw9+eFNmwy/ZQEALFIBig7GBgaiYHAWjYBSMglFAGAAA0N45oNsI0YMAAAAASUVORK5CYII=","orcid":"","institution":"National Central University","correspondingAuthor":true,"prefix":"","firstName":"Ming-Hsu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-13 09:24:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8869933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8869933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102749020,"identity":"ffc4057a-2f5a-456e-b9b2-a40150ee16a4","added_by":"auto","created_at":"2026-02-16 09:11:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1408791,"visible":true,"origin":"","legend":"","description":"","filename":"PAPERBelowCanopyCO2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8869933/v1_covered_c3d13def-c895-4498-a142-8eea10e882d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Below-Canopy CO₂ Accumulation with Turbulence-Based Filtering in a Subtropical Forest","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"terrestrial-atmospheric-and-oceanic-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taoj","sideBox":"Learn more about [Terrestrial, Atmospheric and Oceanic Sciences](https://link.springer.com/journal/44195)","snPcode":"44195","submissionUrl":"https://submission.springernature.com/new-submission/44195/3","title":"Terrestrial, Atmospheric and Oceanic Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Eddy-covariance, Carbon dioxide flux, Turbulence indicator, Gap-filling, Evergreen broadleaf forest","lastPublishedDoi":"10.21203/rs.3.rs-8869933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8869933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate quantification of Net Ecosystem Exchange (NEE) in forests is essential to understand their role in sequestering anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions and supporting climate mitigation. The CO\u003csub\u003e2\u003c/sub\u003e flux measured by the above-canopy Eddy-Covariance (EC) provides ground-truth observations worldwide. However, the applicability of above-canopy EC is affected by below-canopy CO₂ accumulation due to decoupling that frequently occurs under nocturnal or weak-turbulence conditions, often resulting in unrealistically high estimates of ecosystem respiration from above-canopy. An above-canopy EC, complemented by below-canopy wind and CO₂ concentration profile measurements, was integrated to investigate the contribution of below-canopy accumulation to above-canopy CO₂ flux. This study evaluates the effectiveness of the standard deviation of vertical wind speed (σ\u003csub\u003ew\u003c/sub\u003e) as an alternative turbulence indicator to the conventional friction velocity (u*) threshold. Our results indicate that σ\u003csub\u003ew\u003c/sub\u003e serves as a direct proxy for vertical mixing efficiency and offers superior detection of decoupling events. Optimal thresholds were objectively identified using knee-point analysis to maximize the trade-off between the Success Rate (SR), defined as the percentage of decoupling events relative to all events, and the Data Retention (DR), defined as the percentage of remaining data relative to all data. The determined σ\u003csub\u003ew\u003c/sub\u003e threshold of 0.22 m/s outperformed the u* threshold of 0.20 m/s, achieving higher SR (79.8% vs. 72.0%) and better DR (81.4% vs. 74.6%) values. This confirms that σ\u003csub\u003ew\u003c/sub\u003e effectively minimizes the inclusion of decoupled data while preserving more valid observations, a finding further corroborated by analyzing vertical CO₂ profiles. To address gaps in the filtered dataset, five gap-filling strategies were evaluated using Monte Carlo simulations. 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