Resolving the cascade of uncertainty in global flood projections

Resolving the cascade of uncertainty in global flood projections | 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 Resolving the cascade of uncertainty in global flood projections Boen Zhang, Louise Slater, Simon Moulds, Michel Wortmann, Neil Hart, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718520/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Projections of river flooding are crucial for adaptation, but conventional impact modelling is plagued by a ‘cascade of uncertainty’ arising from global climate model (GCM) outputs, statistical corrections, and hydrologic model structures. To overcome this cascade, we developed a data-driven approach that uses machine learning (ML) models to directly predict 10-year flood magnitudes from spatially-varying summary statistics derived from 19 uncorrected CMIP6 GCMs. We find that ML models trained on uncorrected GCMs are 40% more accurate than hydrologic models driven with bias-corrected GCMs. ML rectifies spatially-variable GCM biases by adjusting the contribution of basin attributes, correcting heavy rainfall underestimation in wet river basins. Applying our ML models to over 4.7 million kilometres of global river channels, we find that precipitation intensification is more likely to increase river flood magnitude in dry and high-altitude regions. By 2100, only 34% of rivers are projected to experience larger floods under SSP5-8.5, with the largest increases found in dry climates and high-altitude, steep river segments. Earth and environmental sciences/Hydrology Earth and environmental sciences/Climate sciences/Hydrology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationfinalls.docx Supplementary Information Cite Share Download PDF Status: Under Review 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-6718520","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465368056,"identity":"53127957-991f-4d7e-937c-46855f1df88d","order_by":0,"name":"Boen Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACAwh1AIiZD2CTwauFLYFkLTwG2GQwgTl787EHDH/uyJvzr/n4mKfGhoFfIoHxww+Gw8a4tFj2HEs3YOB5ZrhzxtvNxjzH0hgkew4wS/YwHDbD6bAbOWYSDBKHGTfcOLtNmrfhMIPB8QYGaQaGwza4teR/k2AwOGy/4caZZ0At/4FsBubf+LXksEkwJBxO3HC+hw2o5QDIFjaQLTgdBvSLmUTCgcPJG26wGRvOOZbMI9lzsM2yxyAdp/eBIfZM4sOfw7Ybzh9++OBNjZ0cv0Ty4Rs/KqwNG3DpAYEEECEBJhl4GBgYG/DECjLgP0CMqlEwCkbBKBiJAABCXVW81SfI1wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4633-3033","institution":"School of Geography and the Environment, University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Boen","middleName":"","lastName":"Zhang","suffix":""},{"id":465368057,"identity":"5a8ee738-946b-489c-829b-ff82d332aae6","order_by":1,"name":"Louise Slater","email":"","orcid":"https://orcid.org/0000-0001-9416-488X","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Louise","middleName":"","lastName":"Slater","suffix":""},{"id":465368058,"identity":"91fbaa08-f8f3-4155-953c-57adabf483fe","order_by":2,"name":"Simon Moulds","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Moulds","suffix":""},{"id":465368059,"identity":"9c19fe35-79aa-4c03-a985-abcd701ca5eb","order_by":3,"name":"Michel Wortmann","email":"","orcid":"","institution":"European Centre for Medium-Range Weather Forecasts","correspondingAuthor":false,"prefix":"","firstName":"Michel","middleName":"","lastName":"Wortmann","suffix":""},{"id":465368060,"identity":"09490a84-72b9-4fa3-8291-7e8d693f65ea","order_by":4,"name":"Neil Hart","email":"","orcid":"https://orcid.org/0000-0002-6902-8699","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Hart","suffix":""},{"id":465368061,"identity":"8f22e345-46ac-42b2-8b92-6646200aa5bb","order_by":5,"name":"Yinxue Liu","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Yinxue","middleName":"","lastName":"Liu","suffix":""},{"id":465368062,"identity":"f5e6be35-e3ea-4cda-a64e-aaea9359848e","order_by":6,"name":"Jiabo Yin","email":"","orcid":"https://orcid.org/0000-0002-2305-8729","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jiabo","middleName":"","lastName":"Yin","suffix":""},{"id":465368063,"identity":"095f36aa-7cbe-416e-b6ad-1e781337806f","order_by":7,"name":"Xihui Gu","email":"","orcid":"https://orcid.org/0000-0002-7414-5371","institution":"China University of Geosciences","correspondingAuthor":false,"prefix":"","firstName":"Xihui","middleName":"","lastName":"Gu","suffix":""},{"id":465368064,"identity":"a90ebc41-5f24-43e3-91f3-0bcaac4194fc","order_by":8,"name":"Grey Nearing","email":"","orcid":"","institution":"Google Research","correspondingAuthor":false,"prefix":"","firstName":"Grey","middleName":"","lastName":"Nearing","suffix":""},{"id":465368065,"identity":"4bed79ac-43a7-4957-bce9-81f71dbe94e0","order_by":9,"name":"Manuela Brunner","email":"","orcid":"","institution":"Institute for Atmospheric and Climate Science, ETH Zurich; 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