2026/03/10 by Rotem Mayo, Oleg Zlydenko, Moral Bootbool +12 · 2 voices
Environmental Science · Social Sciences · #Disaster Management and Resilience #Flood Risk Assessment and Management #Public Relations and Crisis Communication
paper · pdf · doi:10.31223/x5rr2k
openalex created_date 2026/02/17 · openalex publication_date 2026/03/10 · openalex updated_date 2026/07/16
High-quality historical flood data is critical for disaster risk management, infrastructural planning, and climate change attribution, however, existing global archives are constrained by sparse geographical coverage, coarse spatial resolution, or reliance on prolonged satellite observation.To address this gap, we introduce Groundsource, an open-access global dataset comprising 2.6 million high-resolution historical flood events, curated from the automated processing of over 5 million news articles across more than 150 countries.Our methodology leverages Gemini large language models (LLMs) to systematically extract structured spatial and temporal data from unstructured journalistic text.Comprehensive technical validation demonstrates that the pipeline achieves an 82% practical precision rate in manual evaluations.Furthermore, spatiotemporal matching against established external databases reveals recall capturing 85% to 100% of severe flood events recorded in the Global Disaster Alert and Coordination System (GDACS) between 2020 and 2026.By transforming unstructured global news media into a structured, localized event archive, Groundsource provides a massive-scale, extensible resource to support the training of predictive hydrological models, quantify historical exposure, and advance global disaster research.