Frequency analysis of annual maximum daily rainfall in Brazil with multiparameter probability density functions

Frequency analysis of annual maximum daily rainfall in Brazil with multiparameter probability density functions | 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 Frequency analysis of annual maximum daily rainfall in Brazil with multiparameter probability density functions Aryane Araujo Rodrigues, Tamara Leitzke Caldeira Beskow, Tirzah Moreira Siqueira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4076196/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 For water resources engineering and management, understand the extreme rainfall events it’s essential. Using rainfall frequency analysis, one can fit many Probability Density Functions (PDFs) to the rainfall series and identify the best fit through the goodness-of-fit tests, allowing the estimate of Annual Maximum Daily Rainfall (AMDR) quantiles for different Return Periods (RP). Recommendations regarding the best PDFs for this have been made for some countries, however, in the opposite direction, Brazil has no guidelines or recommendations such as the above mentioned, and Gumbel distribution still is the most used PDF for modeling AMDR, frequently without testing others. That said, we focus in modeling thousands of AMDR series in Brazil, evaluating ten PDF candidates to find the best fit and defining the most indicated to describe AMDR in the country. The methodology consisted of: acquisition, structuration and screening process by temporal and statistical criteria; fit of the 2-, 3- and multiparameter PDFs to the AMDR series based on the L-moments method; quantile estimation; and PDFs performance assessment by Filliben test and the relative absolute error. From the almost 4 thousand AMDR series investigated, we concluded that: Gumbel and Exponential provided the poorest performance (32.1–60.2% of non-satisfactory fits); multiparametric PDFs (Wakeby and Kappa) are the most indicated for modeling AMDR in Brazil; Gumbel had the highest error values for quantile estimate, especially for high RP; novelties and advances on probabilistic modeling of AMDR in Brazil were provided, helping decision makers with accurate and essential technical information for many purposes. Heavy rainfall Hydrological series Nonparametric trend test Filliben test Wakeby probabilistic distribution Design storm Full Text 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-4076196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278882120,"identity":"f1e557c1-d6e1-4399-b13a-2adcb164a2b9","order_by":0,"name":"Aryane Araujo 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Using rainfall frequency analysis, one can fit many Probability Density Functions (PDFs) to the rainfall series and identify the best fit through the goodness-of-fit tests, allowing the estimate of Annual Maximum Daily Rainfall (AMDR) quantiles for different Return Periods (RP). Recommendations regarding the best PDFs for this have been made for some countries, however, in the opposite direction, Brazil has no guidelines or recommendations such as the above mentioned, and Gumbel distribution still is the most used PDF for modeling AMDR, frequently without testing others. That said, we focus in modeling thousands of AMDR series in Brazil, evaluating ten PDF candidates to find the best fit and defining the most indicated to describe AMDR in the country. 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