BUSLE: A probabilistic tool for forecasting sediment inflow to large reservoirs from soil loss

BUSLE: A probabilistic tool for forecasting sediment inflow to large reservoirs from soil loss | 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 BUSLE: A probabilistic tool for forecasting sediment inflow to large reservoirs from soil loss Jose-Luis Molina, Fernando Espejo, Carmen Patino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9207213/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Occurrence of geodynamic processes like rainfall-runoff as external or seismic processes as internal, lead to frequent unmanageable problems globally. Heightened extreme rainfall events markedly boost soil erosion and hazards associated with soil erosion. The early forecasting of the main processes involved in soil erosion assists in the early identification of soil erosion risk and can reduce damage by implementing suitable actions (preventive, corrective and palliative). This research focuses on forecasting the average likelihood of soil erosion and posterior sediment inflow to large reservoirs through a Machine Learning (ML) method. This approach comprises a Bayesian Causal Reasoning (BCR) development for proposing an innovative stochastic computation of the Universal Soil Loss Equation (USLE) model that is called BUSLE (Bayesian USLE). An Object-Oriented Bayesian Networks (OOBNs) system has been developed, entirely based on automatic learning process approach and mathematical computations. This BUSLE tool comprises the following parameters: R (Rainfall Erosivity): K (Soil Erodibility): L and S correspond to Topography, Length and degree of slope respectively, C (Crop management) and P (Conservation practices). For this, annual records for all parameters were collected from different sources and repositories. The total soil loss (A) is computed as the multiplication of all the parameters for each sub-basin. BUSLE comprises a total of 10 Bayesian nets, one pear each sub-basin, and a final Master Network that collects the information for all sub-basins. This Master net implements the overall module of erosion and sediment inflow to large reservoirs prediction. This research is applied to the Rules reservoir catchment (Granada province, SE Spain). Results indicate that soil loss is up to 2.75 Mm 3 for the studied period, which is a very low fraction of the total reservoir silting/colmatation. BUSLE allows to assist in water policy-level decision-making, and researchers can additionally evaluate various scenarios alongside the BUSLE tool to improve the prediction accuracy of soil erosion and reservoir silting likelihood. Object-oriented Bayesian networks Decision support systems Machine learning Soil erosion modelling Reservoir management Reservoir silting Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 25 Mar, 2026 First submitted to journal 24 Mar, 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-9207213","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617234293,"identity":"d4ae475e-5e6f-440e-8384-908d4285cc9e","order_by":0,"name":"Jose-Luis Molina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACxnYg8cCAgYEfxHtAlJZmIJEA1CLZAGIQZQ0zVKXBAWK1MDdzJz5IKLgjb3wj/eGHBAYbeyIcxrvZIMHgmeG2GznGEgkMaYkNRGjZJpFgcJgRqIUBqOUwYbfBtNhvnpH++EcCw3+iHAbWkrhBIsEMaMsBRmIcBvLL4eQZZ96YWSQYJBP2i2F778YHH/4ctu1vT39840OFHWGHGaIaakBQAwODPBFqRsEoGAWjYKQDAO9bPTNZQwfGAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1001-3601","institution":"University of Salamanca","correspondingAuthor":true,"prefix":"","firstName":"Jose-Luis","middleName":"","lastName":"Molina","suffix":""},{"id":617234294,"identity":"bd8e3e69-6ff5-40a3-80f2-54a127b4a833","order_by":1,"name":"Fernando Espejo","email":"","orcid":"","institution":"Universidad de Salamanca","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Espejo","suffix":""},{"id":617234295,"identity":"d4b7015b-8791-43b1-8908-9dec26c2a3cd","order_by":2,"name":"Carmen Patino","email":"","orcid":"","institution":"Universidad de Salamanca","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"","lastName":"Patino","suffix":""}],"badges":[],"createdAt":"2026-03-24 05:54:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9207213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9207213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106544677,"identity":"60f363ac-de8c-47b1-9a4b-6ba5e08021f1","added_by":"auto","created_at":"2026-04-09 16:41:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":553322,"visible":true,"origin":"","legend":"","description":"","filename":"Molinaetal.2026DEF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9207213/v1_covered_924738f9-ff11-4419-96e4-3a9fb2d73d0c.pdf"}],"financialInterests":"","formattedTitle":"BUSLE: A probabilistic tool for forecasting sediment inflow to large reservoirs from soil loss","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Object-oriented Bayesian networks, Decision support systems, Machine learning, Soil erosion modelling, Reservoir management, Reservoir silting","lastPublishedDoi":"10.21203/rs.3.rs-9207213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9207213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOccurrence of geodynamic processes like rainfall-runoff as external or seismic processes as internal, lead to frequent unmanageable problems globally. Heightened extreme rainfall events markedly boost soil erosion and hazards associated with soil erosion. The early forecasting of the main processes involved in soil erosion assists in the early identification of soil erosion risk and can reduce damage by implementing suitable actions (preventive, corrective and palliative). This research focuses on forecasting the average likelihood of soil erosion and posterior sediment inflow to large reservoirs through a Machine Learning (ML) method. This approach comprises a Bayesian Causal Reasoning (BCR) development for proposing an innovative stochastic computation of the Universal Soil Loss Equation (USLE) model that is called BUSLE (Bayesian USLE). An Object-Oriented Bayesian Networks (OOBNs) system has been developed, entirely based on automatic learning process approach and mathematical computations. This BUSLE tool comprises the following parameters: R (Rainfall Erosivity): K (Soil Erodibility): L and S correspond to Topography, Length and degree of slope respectively, C (Crop management) and P (Conservation practices). For this, annual records for all parameters were collected from different sources and repositories. The total soil loss (A) is computed as the multiplication of all the parameters for each sub-basin. BUSLE comprises a total of 10 Bayesian nets, one pear each sub-basin, and a final Master Network that collects the information for all sub-basins. This Master net implements the overall module of erosion and sediment inflow to large reservoirs prediction. This research is applied to the Rules reservoir catchment (Granada province, SE Spain). Results indicate that soil loss is up to 2.75 Mm\u003csup\u003e3\u003c/sup\u003e for the studied period, which is a very low fraction of the total reservoir silting/colmatation. 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