Dynamic equivalent drainage method for urban flood modeling: A rainfall-adaptive fusion approach

Dynamic equivalent drainage method for urban flood modeling: A rainfall-adaptive fusion approach | 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 Dynamic equivalent drainage method for urban flood modeling: A rainfall-adaptive fusion approach Ming Wu, Yuqin Gao, Yaya Cheng, Xiao Chen, Jingang Zhang, Lin Yi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8614460/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The scarcity of detailed drainage network data severely constrains urban flood modeling and risk assessment. To address this challenge, this study proposes a novel Dynamic Fusion Method (DFM) for equivalent drainage modeling in data-scarce areas. The DFM dynamically integrates three existing approaches—the Rainfall Reduction Method (RRM), Road-based Equivalent Drainage Method (REDM), and Stormwater Inlet Equivalent Drainage Method (SIEDM)—using a rainfall-adaptive nonlinear weighting function. A high-resolution 1D/2D coupled model (SWMM/HEC-RAS), validated against historical inundation records, was established as a benchmark (HRPN) to evaluate the DFM against individual methods under various design storm scenarios in a typical urbanized catchment in Nanjing, China. The comparative results reveal a critical trade-off between volumetric error and spatial reliability. While the RRM produced the lowest total area error, it suffered from significant under-prediction, failing to identify critical flood-prone zones. In contrast, the DFM demonstrated superior spatial consistency, achieving the highest Intersection over Union (IoU) with the benchmark (average IoU of 0.268), outperforming RRM and SIEDM by 16.0% and 10.7%, respectively. Mechanistically, the DFM’s adaptive weighting system effectively acts as a proxy for the drainage system's nonlinear state transition, shifting dominance from global capacity reduction during light rain to localized, surface-based drainage representation during extreme peaks. Although the DFM tends toward a conservative over-prediction of inundated areas, it avoids the dangerous underestimation risks associated with traditional static methods. These findings suggest that the DFM provides a robust and safer alternative framework for high-precision flood risk banding and management in regions lacking detailed infrastructure data. Urban flood modeling Equivalent drainage method Rainfall-adaptive fusion Data-scarce areas Spatial consistency Flood risk assessment Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 10 Mar, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 17 Jan, 2026 First submitted to journal 15 Jan, 2026 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. 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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-8614460","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581123990,"identity":"6550778f-77bc-4975-a463-ae6d4c891123","order_by":0,"name":"Ming Wu","email":"","orcid":"","institution":"Hohai University College of Water Conservancy and Hydropower Engineering","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Wu","suffix":""},{"id":581123991,"identity":"2d029e55-bbe6-4f06-b14f-d38eb886f26f","order_by":1,"name":"Yuqin 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To address this challenge, this study proposes a novel Dynamic Fusion Method (DFM) for equivalent drainage modeling in data-scarce areas. The DFM dynamically integrates three existing approaches\u0026mdash;the Rainfall Reduction Method (RRM), Road-based Equivalent Drainage Method (REDM), and Stormwater Inlet Equivalent Drainage Method (SIEDM)\u0026mdash;using a rainfall-adaptive nonlinear weighting function. A high-resolution 1D/2D coupled model (SWMM/HEC-RAS), validated against historical inundation records, was established as a benchmark (HRPN) to evaluate the DFM against individual methods under various design storm scenarios in a typical urbanized catchment in Nanjing, China. The comparative results reveal a critical trade-off between volumetric error and spatial reliability. While the RRM produced the lowest total area error, it suffered from significant under-prediction, failing to identify critical flood-prone zones. 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