Simulation and prediction of soil loss using the RMMF and CA-Markov models in the Upper Tana River basin, Kenya

Simulation and prediction of soil loss using the RMMF and CA-Markov models in the Upper Tana River basin, Kenya | 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 Simulation and prediction of soil loss using the RMMF and CA-Markov models in the Upper Tana River basin, Kenya Eugine Wafula, Duncan Maina Kimwatu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4263095/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 Soil loss is one of the major environmental concerns with significant negative implications on top soil loss, land degradation, waterbody sedimentation and agricultural productivity. These losses arise from the complex interaction of climatic, biophysical, and anthropogenic factors. This study aimed at assessing and predicting soil loss using the Revised Morgan-Morgan Finney (RMMF) model and CA-Markov chain analysis in the Upper Tana basin. The datasets used included: rainfall intensity, mean daily rainfall, total annual rainfall, land use land cover, canopy height, soil moisture content, soil bulk density, canopy cover fraction, fraction of rainfall not intercepted by canopy, ground cover fraction, root depth, soil surface cohesion, soil detachability factor, evapotranspiration and the digital elevation model. The GIS-based RMMF model was used to simulate soil losses for the years 2002, 2012 and 2022 while the CA-Markov was used for predicting soil loss for the year 2030. The findings revealed that total soil loss exhibited a decreasing trend between 2002 and 2012 from 30159416.72 t/ha to 28762653.24 t/ha and later increased in 2022 which showcased the highest recorded level of 43527091.89 t/ha with a mean of 14.838±32.55 t/ha, 14.400±32.11 t/ha and 21.063±29.87 t/ha respectively. By 2030, the very low soil loss is expected to have a higher coverage of 60.14% followed by the low at 36.77%, the moderate at 0.31%, the high at 0.12% and the very high class at 2.67% of the total area. The study concluded that the anthropogenic, biophysical and climatic factors each play a key role in soil loss. Soil loss Revised Morgan-Morgan Finney (RMMF) CA – Markov chain analysis surface runoff climatic variables biophysical variables anthropogenic variables. Full Text Additional Declarations No competing interests reported. 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. 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-4263095","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305162964,"identity":"7b03f67b-c7d1-4a47-8a19-6da5e9f81d08","order_by":0,"name":"Eugine Wafula","email":"data:image/png;base64,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","orcid":"","institution":"Dedan Kimathi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Eugine","middleName":"","lastName":"Wafula","suffix":""},{"id":305162965,"identity":"ba6b4f3e-b38e-48f1-8c83-6ba817fb7a11","order_by":1,"name":"Duncan Maina Kimwatu","email":"","orcid":"","institution":"Dedan Kimathi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Duncan","middleName":"Maina","lastName":"Kimwatu","suffix":""}],"badges":[],"createdAt":"2024-04-13 21:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4263095/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4263095/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69913897,"identity":"d1089de0-a65b-4207-a8c1-7b90cc13c6f7","added_by":"auto","created_at":"2024-11-26 14:17:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1079035,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4263095/v1_covered_1db08c70-3385-44fa-b0d8-81004c820c95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simulation and prediction of soil loss using the RMMF and CA-Markov models in the Upper Tana River basin, Kenya","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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},"keywords":"Soil loss, Revised Morgan-Morgan Finney (RMMF), CA – Markov chain analysis, surface runoff, climatic variables, biophysical variables, anthropogenic variables.","lastPublishedDoi":"10.21203/rs.3.rs-4263095/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4263095/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil loss is one of the major environmental concerns with significant negative implications on top soil loss, land degradation, waterbody sedimentation and agricultural productivity. These losses arise from the complex interaction of climatic, biophysical, and anthropogenic factors. This study aimed at assessing and predicting soil loss using the Revised Morgan-Morgan Finney (RMMF) model and CA-Markov chain analysis in the Upper Tana basin. The datasets used included: rainfall intensity, mean daily rainfall, total annual rainfall, land use land cover, canopy height, soil moisture content, soil bulk density, canopy cover fraction, fraction of rainfall not intercepted by canopy, ground cover fraction, root depth, soil surface cohesion, soil detachability factor, evapotranspiration and the digital elevation model. The GIS-based RMMF model was used to simulate soil losses for the years 2002, 2012 and 2022 while the CA-Markov was used for predicting soil loss for the year 2030. 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