Leveraging Night-Time Lights (NTL) data to fingerprint the impact of the 2022 Indus floods

Leveraging Night-Time Lights (NTL) data to fingerprint the impact of the 2022 Indus floods | 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 Physical Sciences - Article Leveraging Night-Time Lights (NTL) data to fingerprint the impact of the 2022 Indus floods Ekta Aggarwal, Alex Whittaker, Philippa Mason, Kartikeya Sangwan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8662620/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 Floods are among the most damaging natural hazards, disrupting settlements, infrastructure, and livelihoods. While most flood-impact research focuses on inundation mapping, exposure, and post-disaster loss assessments, flood effects extend far beyond mapped flood zones, disturbing energy systems, transport networks, and supply chains, with recovery often uneven across socio-economic groups. Here, we demonstrate how satellite observations, using NASA’s Black Marble night-time lights (NTL) dataset, enable high-resolution monitoring of infrastructure disruption and recovery following large-scale flooding. Focusing on the 2022 Indus River floods in Pakistan, we identify a statistically significant decline in radiance beginning in June 2022 and persisting for 18 weeks—longer than the period of intense rainfall and extending beyond inundation extents derived from satellite imagery. Urban areas showed the largest radiance losses (80–100%) yet recovered faster than rural areas that experienced smaller declines, underscoring disparities in recovery trajectories. By integrating NTL data with radar-based flood extents, we show that cascading and compounding disruptions reach well beyond inundated areas. Our results highlight NTL’s value for real-time, socio-economically disaggregated monitoring of flood impacts and recovery, offering a scalable framework to support disaster response and resilience planning. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Environmental social sciences/Climate-change impacts Full Text Additional Declarations There is NO Competing Interest. Supplementary Files aggarwaletal2026induspapersupplementary.pdf 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. 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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-8662620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":580626163,"identity":"bd5bed52-cb00-4e49-80f7-04933ec0a52f","order_by":0,"name":"Ekta 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