Development of IDF curves for Uganda Using Observed, Remotely Sensed, and Regional Climate Model rainfall data

Development of IDF curves for Uganda Using Observed, Remotely Sensed, and Regional Climate Model rainfall data | 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 Development of IDF curves for Uganda Using Observed, Remotely Sensed, and Regional Climate Model rainfall data Martin Okirya, JA Du Plessis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6969848/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 Climate-resilient hydrological infrastructure design requires accurate estimation of design storm intensities, especially in data-scarce regions such as Uganda. This research investigates the reliability of bias-corrected remotely sensed rainfall (RSR) and regional climate model (RCM) data for constructing intensity‒duration‒frequency (IDF) curves under both current (1991–2020) and future (2036–2065) climate. The accuracy of RSR data in estimating intensities, the impacts of climate change, and the sensitivity of intensities to emission scenarios were assessed through comparisons of developed IDF curves. The performance of the RCMs was evaluated via metrics, including the root mean square error (RMSE) and the Kolmogorov–Smirnov (KS) test. Among the RCMs, REMO2009 performed best at Fort Portal and Mbarara, with the lowest RMSE values, whereas BCCR-WRF331 demonstrated better accuracy at Gulu, Jinja, and Soroti. Under the representative concentration pathway (RCP) 4.5 scenario for 2036–2065, the projected intensities consistently increase across all stations. For example, at Gulu, the 1-hour, 100-year intensity increased by 22.1%, from 94.58 mm/h to 115.45 mm/h. Rainfall intensity comparisons between the RCP4.5 and RCP8.5 scenarios reveal higher intensities under RCP8.5 at Mbarara, Fort Portal, and Soroti, e.g., Mbarara’s 100-year, 1-hour event increases by 15.15%. At Gulu and Jinja, intensities under RCP 8.5 are slightly lower, indicating spatial variability in emission scenario sensitivity. The research concludes that bias-corrected RSR are reliable alternative in IDF development in data-scarce regions, and that RCM outputs can inform future climate risk assessments. It recommends the integration of localized IDF projections into planning and policy, especially in infrastructure design. Intensity-Duration-Frequency Design Storm Intensity Regional Climate Models Climate Change Projections Infrastructure Resilience Gumbel distributions 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-6969848","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477213536,"identity":"28d556c3-61ec-4788-97f4-c025edea92ab","order_by":0,"name":"Martin Okirya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3OMYvCQBCG4QmB5YqBtBMCSWe9EoiNeH9lg5DKwsrKIjZec5g2/o6A9R4Lsdm7ay0DthbaWKqxtHEDNhb7lAMv3wBY1hvyQG2Pp8sQvc6Jv6gzp2RZ6OddE650z/1gKuay80yt2R7QTat/nTQwH0H0ZYid799tPCWWbnaTAYd63F7E88SlPxGUHNsEEwImwQVDwqIDD1BQWhW6TS4SmNc8TxB0EqDkMYdJQs5SApJhhdr3++tchLTLZpSuxkhkWPmU6qc55Vf0CrWh43kURoVh5ZG4f2pZlmW97gZwVEGDp4EV4QAAAABJRU5ErkJggg==","orcid":"","institution":"Stellenbosch University","correspondingAuthor":true,"prefix":"","firstName":"Martin","middleName":"","lastName":"Okirya","suffix":""},{"id":477213537,"identity":"00a9fe72-931d-476f-a780-e6864c3a8260","order_by":1,"name":"JA Du Plessis","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"JA","middleName":"","lastName":"Du Plessis","suffix":""}],"badges":[],"createdAt":"2025-06-25 02:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6969848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6969848/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99141605,"identity":"f100ee60-d997-44e5-ad1c-bab3a9e22378","added_by":"auto","created_at":"2025-12-29 07:54:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1948340,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptsubmissionv1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6969848/v1_covered_ebb654f4-4882-4080-89a8-b8d7750e6765.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of IDF curves for Uganda Using Observed, Remotely Sensed, and Regional Climate Model rainfall data","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":"Intensity-Duration-Frequency, Design Storm Intensity, Regional Climate Models, Climate Change Projections, Infrastructure Resilience, Gumbel distributions","lastPublishedDoi":"10.21203/rs.3.rs-6969848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6969848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate-resilient hydrological infrastructure design requires accurate estimation of design storm intensities, especially in data-scarce regions such as Uganda. This research investigates the reliability of bias-corrected remotely sensed rainfall (RSR) and regional climate model (RCM) data for constructing intensity‒duration‒frequency (IDF) curves under both current (1991\u0026ndash;2020) and future (2036\u0026ndash;2065) climate. The accuracy of RSR data in estimating intensities, the impacts of climate change, and the sensitivity of intensities to emission scenarios were assessed through comparisons of developed IDF curves. The performance of the RCMs was evaluated via metrics, including the root mean square error (RMSE) and the Kolmogorov\u0026ndash;Smirnov (KS) test. Among the RCMs, REMO2009 performed best at Fort Portal and Mbarara, with the lowest RMSE values, whereas BCCR-WRF331 demonstrated better accuracy at Gulu, Jinja, and Soroti. Under the representative concentration pathway (RCP) 4.5 scenario for 2036\u0026ndash;2065, the projected intensities consistently increase across all stations. For example, at Gulu, the 1-hour, 100-year intensity increased by 22.1%, from 94.58 mm/h to 115.45 mm/h. Rainfall intensity comparisons between the RCP4.5 and RCP8.5 scenarios reveal higher intensities under RCP8.5 at Mbarara, Fort Portal, and Soroti, e.g., Mbarara\u0026rsquo;s 100-year, 1-hour event increases by 15.15%. At Gulu and Jinja, intensities under RCP 8.5 are slightly lower, indicating spatial variability in emission scenario sensitivity. The research concludes that bias-corrected RSR are reliable alternative in IDF development in data-scarce regions, and that RCM outputs can inform future climate risk assessments. 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