Multiscale Phosphorus Loss in Farmland Driven by Precipitation: Effects of Farmland Type

Multiscale Phosphorus Loss in Farmland Driven by Precipitation: Effects of Farmland Type | 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 Multiscale Phosphorus Loss in Farmland Driven by Precipitation: Effects of Farmland Type Kai Shi, Hui Zhang, Xiaoli Mei, Boyun Liu, Qiang Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8170863/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Precipitation is the primary driver of phosphorus loss from farmland. However, the multi-temporal and spatial characteristics, as well as the mechanisms of phosphorus loss across different farmland types at the watershed scale, remain poorly understood. This study examines the multiscale impacts of different farmland types on phosphorus loss processes driven by long-term precipitation, focusing on two adjacent small watersheds, the Yulin River and the Dahong River, in the Sichuan Basin. First, based on remote sensing data from 2021 to 2022 (precipitation, temperature, land cover) and river water quality monitoring data (total phosphorus (TP), dissolved oxygen (DO), and permanganate index (COD)), the spatiotemporal variations in river water quality are analyzed. The results indicate that under similar climate conditions and pollution source distributions, the downstream TP concentration in the Dahong River (dominated by paddy fields) is significantly lower than that in the upstream, whereas in the Yulin River (dominated by dryland farming), the downstream TP concentration is higher than that in the upstream. Secondly, the study develops a multi-source coupling analysis framework that integrates Detrended Cross-Correlation Analysis (DCCA), Multifractal Detrended Cross-Correlation Analysis (MFDCCA), and remote sensing data to evaluate the impact of different farmland types on phosphorus loss behavior. The DCCA analysis results show that precipitation and TP exhibit a clear long-term correlation, with scaling exponents all exceeding 0.5. In the paddy field control area, precipitation and TP exhibit a positive correlation over long time scales. In contrast, in the dryland control area, a positive correlation is observed only over short time scales (< 60 days), while at longer time scales, the correlation turns negative and shows significant fluctuations. MFDCCA further reveals that the coupling relationship between TP and precipitation generally exhibits multifractal characteristics. The multifractal intensity is higher in the dryland-controlled fields ( Δh = 1.47), indicating that TP is more sensitive to precipitation perturbations and less stable. The multifractal intensity is lower in the paddy fields ( Δh = 1.20), indicating a more stable coupling relationship. Finally, by combining sliding window analysis with 3D convex hull volume calculations, the study quantitatively assesses the distribution differences of multifractal parameters. The results show that areas dominated by paddy fields exhibited stronger aggregation of multifractal parameters (convex hull volume difference of 0.69). In contrast, dryland areas showed more dispersed patterns (convex hull volume difference of − 0.32). This study innovatively integrates fractal theory, remote sensing, and watershed observation data to establish a coupling analysis framework for identifying non-point source pollution. It elucidates the multiscale response patterns of phosphorus loss under different farmland types. It provides a new technical approach for quantitatively assessing the effects of farmland type on the spatial distribution and migration pathways of TP. phosphorus loss precipitation DCCA coefficient MFDCCA farmland type Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 21 Nov, 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. 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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-8170863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561879283,"identity":"7fdb71c4-2fdd-44d9-b785-8d7247af46d3","order_by":0,"name":"Kai Shi","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Shi","suffix":""},{"id":561879285,"identity":"081f7e9a-d9e3-488b-9aaf-8d91c2d99278","order_by":1,"name":"Hui Zhang","email":"","orcid":"","institution":"China West Normal 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Type","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":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"phosphorus loss, precipitation, DCCA coefficient, MFDCCA, farmland type","lastPublishedDoi":"10.21203/rs.3.rs-8170863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8170863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecipitation is the primary driver of phosphorus loss from farmland. However, the multi-temporal and spatial characteristics, as well as the mechanisms of phosphorus loss across different farmland types at the watershed scale, remain poorly understood. This study examines the multiscale impacts of different farmland types on phosphorus loss processes driven by long-term precipitation, focusing on two adjacent small watersheds, the Yulin River and the Dahong River, in the Sichuan Basin. First, based on remote sensing data from 2021 to 2022 (precipitation, temperature, land cover) and river water quality monitoring data (total phosphorus (TP), dissolved oxygen (DO), and permanganate index (COD)), the spatiotemporal variations in river water quality are analyzed. The results indicate that under similar climate conditions and pollution source distributions, the downstream TP concentration in the Dahong River (dominated by paddy fields) is significantly lower than that in the upstream, whereas in the Yulin River (dominated by dryland farming), the downstream TP concentration is higher than that in the upstream. Secondly, the study develops a multi-source coupling analysis framework that integrates Detrended Cross-Correlation Analysis (DCCA), Multifractal Detrended Cross-Correlation Analysis (MFDCCA), and remote sensing data to evaluate the impact of different farmland types on phosphorus loss behavior. The DCCA analysis results show that precipitation and TP exhibit a clear long-term correlation, with scaling exponents all exceeding 0.5. In the paddy field control area, precipitation and TP exhibit a positive correlation over long time scales. In contrast, in the dryland control area, a positive correlation is observed only over short time scales (\u0026lt;\u0026thinsp;60 days), while at longer time scales, the correlation turns negative and shows significant fluctuations. MFDCCA further reveals that the coupling relationship between TP and precipitation generally exhibits multifractal characteristics. The multifractal intensity is higher in the dryland-controlled fields (\u003cem\u003eΔh\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.47), indicating that TP is more sensitive to precipitation perturbations and less stable. The multifractal intensity is lower in the paddy fields (\u003cem\u003eΔh\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.20), indicating a more stable coupling relationship. Finally, by combining sliding window analysis with 3D convex hull volume calculations, the study quantitatively assesses the distribution differences of multifractal parameters. The results show that areas dominated by paddy fields exhibited stronger aggregation of multifractal parameters (convex hull volume difference of 0.69). In contrast, dryland areas showed more dispersed patterns (convex hull volume difference of \u0026minus;\u0026thinsp;0.32). This study innovatively integrates fractal theory, remote sensing, and watershed observation data to establish a coupling analysis framework for identifying non-point source pollution. It elucidates the multiscale response patterns of phosphorus loss under different farmland types. It provides a new technical approach for quantitatively assessing the effects of farmland type on the spatial distribution and migration pathways of TP.\u003c/p\u003e","manuscriptTitle":"Multiscale Phosphorus Loss in Farmland Driven by Precipitation: Effects of Farmland Type","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 05:10:44","doi":"10.21203/rs.3.rs-8170863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T14:06:49+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"267496315962048523445317313474901319471","date":"2026-01-22T14:46:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233776478092204793482839632966394940555","date":"2026-01-08T17:01:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T10:22:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229706510340269179417306225751145536241","date":"2025-12-21T11:49:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201763539729549776650527421000517180059","date":"2025-12-19T17:06:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T19:28:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T00:39:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T00:38:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-11-21T07:31:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"aa5fbdab-e9b9-45ba-addb-78a857a8ccc9","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T13:55:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 05:10:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8170863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8170863","identity":"rs-8170863","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}