Modeling the Rainfall and Large–Scale Climate Influence in the Upper Meghna River Basin of Bangladesh

Modeling the Rainfall and Large–Scale Climate Influence in the Upper Meghna River Basin of Bangladesh | 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 Modeling the Rainfall and Large–Scale Climate Influence in the Upper Meghna River Basin of Bangladesh Md. Musa Nadim, Md. Anowarul Islam, Shah Md Shajib Hossain, Shahrin Kabir Mowmi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7267619/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted 11 You are reading this latest preprint version Abstract Climate change is significantly altering rainfall patterns, especially in regions with diverse landscapes. Focusing on the climate-sensitive Upper Meghna River Basin of Bangladesh, this study analyzes 38 years (1987–2024) of monthly, annual, and seasonal rainfall trends across seven meteorological stations. Rainfall trends were assessed at both the 5% and 10% significance levels. While most monthly trends were only significant at the 5% level, notable findings include an increasing trend in August rainfall at Chandpur (+ 6.29 mm/year) and decreasing trends in September at Tangail (–3.75 mm/year) and in February at Mymensingh (–0.65 mm/year). Additionally, Tangail exhibited a statistically significant decrease in both annual (–11.33 mm/year) and monsoonal (–9.81 mm/year) rainfall at the 10% level, while other stations showed irregular patterns with no significant trends. The study further explored the influence of major climatic drivers, including the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), along with their lagged impacts from 1 to 6 months. Correlation results revealed weak associations with ENSO (r = 0.003) and IOD (r = − 0.014), with slightly increased values at a 6-month lag for ENSO (r = 0.039) and a 5-month lag for IOD (r = 0.068), suggesting minimal lagged influence on regional rainfall variability. In predictive modeling, CatBoost (R² = 0.9481, MAE = 0.8185, RMSE = 4.3784) outperformed XGBoost (R² = 0.9314, MAE = 0.8873, RMSE = 5.0315), demonstrating strong capacity in capturing rainfall dynamics. These findings support informed water resource planning and climate adaptation in vulnerable regions. Climate change Rainfall ENSO IOD Meghna River Bangladesh Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 13 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 10 Aug, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 01 Aug, 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. 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-7267619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499202809,"identity":"dc018e08-713e-4103-beb4-2619b25d9449","order_by":0,"name":"Md. 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Focusing on the climate-sensitive Upper Meghna River Basin of Bangladesh, this study analyzes 38 years (1987\u0026ndash;2024) of monthly, annual, and seasonal rainfall trends across seven meteorological stations. Rainfall trends were assessed at both the 5% and 10% significance levels. While most monthly trends were only significant at the 5% level, notable findings include an increasing trend in August rainfall at Chandpur (+\u0026thinsp;6.29 mm/year) and decreasing trends in September at Tangail (\u0026ndash;3.75 mm/year) and in February at Mymensingh (\u0026ndash;0.65 mm/year). Additionally, Tangail exhibited a statistically significant decrease in both annual (\u0026ndash;11.33 mm/year) and monsoonal (\u0026ndash;9.81 mm/year) rainfall at the 10% level, while other stations showed irregular patterns with no significant trends. The study further explored the influence of major climatic drivers, including the El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), along with their lagged impacts from 1 to 6 months. Correlation results revealed weak associations with ENSO (r\u0026thinsp;=\u0026thinsp;0.003) and IOD (r = \u0026minus;\u0026thinsp;0.014), with slightly increased values at a 6-month lag for ENSO (r\u0026thinsp;=\u0026thinsp;0.039) and a 5-month lag for IOD (r\u0026thinsp;=\u0026thinsp;0.068), suggesting minimal lagged influence on regional rainfall variability. In predictive modeling, CatBoost (R\u0026sup2; = 0.9481, MAE\u0026thinsp;=\u0026thinsp;0.8185, RMSE\u0026thinsp;=\u0026thinsp;4.3784) outperformed XGBoost (R\u0026sup2; = 0.9314, MAE\u0026thinsp;=\u0026thinsp;0.8873, RMSE\u0026thinsp;=\u0026thinsp;5.0315), demonstrating strong capacity in capturing rainfall dynamics. These findings support informed water resource planning and climate adaptation in vulnerable regions.\u003c/p\u003e","manuscriptTitle":"Modeling the Rainfall and Large–Scale Climate Influence in the Upper Meghna River Basin of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 15:46:40","doi":"10.21203/rs.3.rs-7267619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-13T06:54:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T05:12:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T21:20:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T04:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3435363972974360129516673307316369678","date":"2025-08-12T07:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330282211804527667180234547174232250239","date":"2025-08-10T14:05:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61181901061572869837507416938295689803","date":"2025-08-10T07:47:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T06:03:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T05:57:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T05:56:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-08-01T05:31:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ba81bc3d-2711-481b-81f7-5b193177319c","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:03:37+00:00","versionOfRecord":{"articleIdentity":"rs-7267619","link":"https://doi.org/10.1007/s00704-025-05876-3","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-11-10 15:58:31","publishedOnDateReadable":"November 10th, 2025"},"versionCreatedAt":"2025-08-14 15:46:40","video":"","vorDoi":"10.1007/s00704-025-05876-3","vorDoiUrl":"https://doi.org/10.1007/s00704-025-05876-3","workflowStages":[]},"version":"v1","identity":"rs-7267619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7267619","identity":"rs-7267619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}