Wavelet Analysis of ENSO Impact on Rainfall Variability in the Andaman Islands: Implications for Climate Resilience and Predictive Modelling

Wavelet Analysis of ENSO Impact on Rainfall Variability in the Andaman Islands: Implications for Climate Resilience and Predictive Modelling | 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 Wavelet Analysis of ENSO Impact on Rainfall Variability in the Andaman Islands: Implications for Climate Resilience and Predictive Modelling Shravan Kumar, Dr. Ganesh G, Lavanya Bukke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5675635/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 In this research project it is explored how El Nino-Southern Oscillation (ENSO) affects rain patterns on Andaman Islands by employing wavelet analysis methods. The primary research goal is to assess temporal correlations between ENSO phases and local rainfall using both Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT). Methodology utilized included the analysis of Oceanic Nino Index (ONI) data coupled with local rainfall statistics; Pearson and Spearman correlation coefficients were then employed to establish relationships. Results revealed a moderate negative relationship (Pearson coefficient =-0.31) between ONI values and annual rainfall; higher ONI levels associated with El Nino events tend to correlate to reduced precipitation across the region. Additionally, using Long Short-Term Memory (LSTM) neural networks in predictive modelling significantly improved forecast accuracy as evidenced by their R-squared value of 0.95 and their relative risk-maximization error estimate of 0.15. This implies a complex interrelation between global climate events and regional hydrological responses; further challenging prior assumptions of ENSO's effects. These insights contribute to improving regional climate dynamics knowledge, underscoring the role of ENSO in rainfall variability prediction, supporting improved climate resilience efforts as well as predictive modelling efforts in Andaman Islands. Climate Correlation El Nino-Southern Oscillation (ENSO) Hydrological Cycles Oceanic Nino Index (ONI) Rainfall Variability Wavelet Analysis 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. 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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-5675635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394950898,"identity":"1d5775c4-88ad-4c85-8d59-6a53b0a4f944","order_by":0,"name":"Shravan Kumar","email":"data:image/png;base64,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","orcid":"","institution":"Sreenidhi Institute of Science and Technology Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"Shravan","middleName":"","lastName":"Kumar","suffix":""},{"id":394950899,"identity":"845f327f-8f00-4152-95c6-2a8fb5d2de20","order_by":1,"name":"Dr. Ganesh G","email":"","orcid":"","institution":"Sreenidhi Institute of Science and Technology Hyderabad","correspondingAuthor":false,"prefix":"Dr.","firstName":"Ganesh","middleName":"","lastName":"G","suffix":""},{"id":394950900,"identity":"c10168ed-70d0-46cf-a0cd-5fb8d2368d95","order_by":2,"name":"Lavanya Bukke","email":"","orcid":"","institution":"Sreenidhi Institute of Science and Technology Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Lavanya","middleName":"","lastName":"Bukke","suffix":""}],"badges":[],"createdAt":"2024-12-19 09:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5675635/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5675635/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77394512,"identity":"a97e1ff9-728c-4a27-8c43-f3aa3b24c77a","added_by":"auto","created_at":"2025-02-28 07:17:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2132288,"visible":true,"origin":"","legend":"","description":"","filename":"MS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5675635/v1_covered_8e33e780-935c-4238-9190-fc35e07a50ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wavelet Analysis of ENSO Impact on Rainfall Variability in the Andaman Islands: Implications for Climate Resilience and Predictive Modelling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Climate Correlation, El Nino-Southern Oscillation (ENSO), Hydrological Cycles, Oceanic Nino Index (ONI), Rainfall Variability, Wavelet Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5675635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5675635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this research project it is explored how El Nino-Southern Oscillation (ENSO) affects rain patterns on Andaman Islands by employing wavelet analysis methods. The primary research goal is to assess temporal correlations between ENSO phases and local rainfall using both Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT). Methodology utilized included the analysis of Oceanic Nino Index (ONI) data coupled with local rainfall statistics; Pearson and Spearman correlation coefficients were then employed to establish relationships. Results revealed a moderate negative relationship (Pearson coefficient =-0.31) between ONI values and annual rainfall; higher ONI levels associated with El Nino events tend to correlate to reduced precipitation across the region. Additionally, using Long Short-Term Memory (LSTM) neural networks in predictive modelling significantly improved forecast accuracy as evidenced by their R-squared value of 0.95 and their relative risk-maximization error estimate of 0.15. 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