Global hydropower infrastructure and its environmental risks mapped by multimodal AI

Global hydropower infrastructure and its environmental risks mapped by multimodal AI | 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 Analysis Global hydropower infrastructure and its environmental risks mapped by multimodal AI Xiaomeng Huang, Jiahao Li, Jiancheng Pan, Ramit Debnath, Dabo Guan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8405778/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 Hydropower is the largest source of renewable electricity globally, yet its infrastructure is poorly mapped at scale. Existing public inventories, compiled predominantly from bottom-up reporting, are often incomplete and lack consistent geolocation. Here, we develop a multimodal AI framework for the automated detection of hydropower plants from remote-sensing imagery within a globally consistent, top-down manner. Applied to 8,330,487 river segments worldwide, the framework identifies 12,640 hydropower plants, 55.7% of which are absent from state-of-the-art public inventories. The resulting dataset reveals pronounced regional disparities and transboundary clustering of hydropower development, and shows that hydropower infrastructure affects 56.97% of the world’s protected areas, with substantial biomass loss during construction. Hydrological analyses further indicate that 29.9% of plants have experienced declining runoff over the past two decades and 12.0% are exposed to high flood risk. This work provides a scalable framework for monitoring global hydropower development and associated environmental and climatic risks. Earth and environmental sciences/Hydrology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationofGlobalhydropowerinfrastructureanditsenvironmentalrisksmappedbymultimodalAI.pdf Supplementary Information of Global hydropower infrastructure and its environmental risks mapped by multimodal AI 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. 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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-8405778","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Analysis","associatedPublications":[],"authors":[{"id":572030662,"identity":"3f677510-fabd-46db-b259-4a795fc982ec","order_by":0,"name":"Xiaomeng 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