Assessment of prediction skill of SEAS5 forecast using ERA5 soil-moisture and relation to crop production

Assessment of prediction skill of SEAS5 forecast using ERA5 soil-moisture and relation to crop production | 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 Assessment of prediction skill of SEAS5 forecast using ERA5 soil-moisture and relation to crop production Padmavathi bevara, Ehud Strobach This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8796665/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Soil moisture is a key component of the climate system and an important parameter of agricultural productivity, but its predictability varies strongly across regions and seasons. In this study, we assess the skill of the SEAS5 seasonal forecasting system in reconstructing soil moisture anomalies relative to ERA5 over 1981–2024. Later, we examine whether this prediction skill can be exploited to estimate seasonal winter wheat and maize yields. SEAS5 shows its strongest performance at short lead times (0–1 months), particularly in the upper soil layer, whereas the forecast skill of near-surface soil moisture decreases rapidly with increasing lead time. In contrast, deeper layers maintain substantially higher skill for several months, especially across central and northern Europe, reflecting the longer hydrological memory of deeper soil moisture, where precipitation and evapotranspiration signals are integrated over time. Projecting SEAS5 anomalies onto the leading ERA5 EOF patterns and reconstructing the first 10 principal components further enhances the agreement between SEAS5 and ERA5, indicating that SEAS5 captures the dominant large-scale, low-frequency modes of soil moisture variability. It was found that the first principal component of the deepest soil layer is contaminated by non-physical discontinuities associated the parallel production streams in ERA5 and the transition for hindcast to forecast in SEAS5. Reconstructing components 2–10 in both ERA5 and SEAS5 soil-moisture anomalies to remove this non-physical errors further improves the correlations. The SEAS5 prediction skill was found to be potentially relevant for agriculture. Winter wheat shows moderate correlation to soil moisture conditions during autumn establishment and spring regrowth, with pronounced relationships in the Balkans, Hungary, Romania, and central Europe. Maize exhibits an even stronger dependence on soil moisture throughout its growing season, especially in rain-fed regions where yield variability is primarily controlled by water availability. In the Balkan region, maize yields closely track soil moisture anomalies, demonstrating the potential for using SEAS5 as an early season predictor of crop outcomes. Overall, the principal component reconstruction of SEAS5 and ERA5 improves the correlation between the two datasets, demonstrating that SEAS5 prediction skill benefits from filtering out high-frequency noise. The refined signal provides meaningful soil-moisture predictability, which is particularly valuable for planning crops up to six–seven months ahead in rain-fed regions where yields are tightly linked to soil moisture variability. Integrating soil moisture forecasts with extended seasonal climate information can therefore strengthen drought preparedness and support climate-informed agricultural decision-making across Europe. Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Earth and environmental sciences/Hydrology ERA5 SEAS5 Soil moisture Winter wheat yield Maize crop yield Principal Component (PC) and Empirical Orthogonal Function (EOF) analysis EOF projection Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SI.pdf Supplementary Information for the manuscript: Assessment of prediction skill of SEAS5 forecast using ERA5 soil moisture data and relation to crop production Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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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-8796665","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":589267899,"identity":"408318f3-b46b-45bc-97b7-c78afccf5dcf","order_by":0,"name":"Padmavathi bevara","email":"data:image/png;base64,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","orcid":"","institution":"Agriculture Research Organization, Rishon-Lezion,","correspondingAuthor":true,"prefix":"","firstName":"Padmavathi","middleName":"","lastName":"bevara","suffix":""},{"id":589267900,"identity":"2d9c5cc1-5421-40dc-ba2b-83271af6bfe1","order_by":1,"name":"Ehud Strobach","email":"","orcid":"https://orcid.org/0000-0003-1465-6855","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ehud","middleName":"","lastName":"Strobach","suffix":""}],"badges":[],"createdAt":"2026-02-05 11:57:35","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8796665/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-8796665/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103503792,"identity":"c915623c-6c81-44ce-8c8b-cd4eaaa5aca1","added_by":"auto","created_at":"2026-02-26 13:01:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8123601,"visible":true,"origin":"","legend":"","description":"","filename":"Maindocument.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8796665/v2_covered_a902172d-95f8-47af-9b42-6284be7d5b27.pdf"},{"id":102948475,"identity":"df1f4751-099a-46ce-b6a7-74ed6ef1ab7a","added_by":"auto","created_at":"2026-02-18 19:39:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11011351,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information for the manuscript: Assessment of prediction skill of SEAS5 forecast using ERA5 soil moisture data and relation to crop production\u003c/p\u003e","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8796665/v2/dbfee3529fde1bcca9b918e8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Assessment of prediction skill of SEAS5 forecast\nusing ERA5 soil-moisture and relation to crop\nproduction","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":"ERA5, SEAS5, Soil moisture, Winter wheat yield, Maize crop yield, Principal Component (PC) and Empirical Orthogonal Function (EOF) analysis, EOF projection","lastPublishedDoi":"10.21203/rs.3.rs-8796665/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8796665/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil moisture is a key component of the climate system and an important parameter of agricultural productivity, but its predictability varies strongly across regions and seasons. In this study, we assess the skill of the SEAS5 seasonal forecasting system in reconstructing soil moisture anomalies relative to ERA5 over 1981–2024. Later, we examine whether this prediction skill can be exploited to estimate seasonal winter wheat and maize yields. SEAS5 shows its strongest performance at short lead times (0–1 months), particularly in the upper soil layer, whereas the forecast skill of near-surface soil moisture decreases rapidly with increasing lead time. In contrast, deeper layers maintain substantially higher skill for several months, especially across central and northern Europe, reflecting the longer hydrological memory of deeper soil moisture, where precipitation and evapotranspiration signals are integrated over time. Projecting SEAS5 anomalies onto the leading ERA5 EOF patterns and reconstructing the first 10 principal components further enhances the agreement between SEAS5 and ERA5, indicating that SEAS5 captures the dominant large-scale, low-frequency modes of soil moisture variability. It was found that the first principal component of the deepest soil layer is contaminated by non-physical discontinuities associated the parallel production streams in ERA5 and the transition for hindcast to forecast in SEAS5. Reconstructing components 2–10 in both ERA5 and SEAS5 soil-moisture anomalies to remove this non-physical errors further improves the correlations. \u0026nbsp;The SEAS5 prediction skill was found to be potentially relevant for agriculture. Winter wheat shows moderate correlation to soil moisture conditions during autumn establishment and spring regrowth, with pronounced relationships in the Balkans, Hungary, Romania, and central Europe. Maize exhibits an even stronger dependence on soil moisture throughout its growing season, especially in rain-fed regions where yield variability is primarily controlled by water availability. In the Balkan region, maize yields closely track soil moisture anomalies, demonstrating the potential for using SEAS5 as an early season predictor of crop outcomes.\u003c/p\u003e\n\u003cp\u003eOverall, \u0026nbsp;the principal component reconstruction of SEAS5 and ERA5 improves the correlation between the two datasets, demonstrating that SEAS5 prediction skill benefits from filtering out high-frequency noise. The refined signal provides meaningful soil-moisture predictability, which is particularly valuable for planning crops up to six–seven months ahead in rain-fed regions where yields are tightly linked to soil moisture variability. Integrating soil moisture forecasts with extended seasonal climate information can therefore strengthen drought preparedness and support climate-informed agricultural decision-making across Europe.\u003c/p\u003e","manuscriptTitle":"Assessment of prediction skill of SEAS5 forecast\nusing ERA5 soil-moisture and relation to crop\nproduction","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2026-02-18 19:39:19","doi":"10.21203/rs.3.rs-8796665/v2","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}},{"code":1,"date":"2026-02-11 00:39:24","doi":"10.21203/rs.3.rs-8796665/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":"6a00bf92-0f58-4d4c-92ac-2b36ce220975","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62698254,"name":"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction"},{"id":62698255,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2026-02-11T08:30:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 19:39:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-8796665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8796665","identity":"rs-8796665","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}