A high-resolution global flood susceptibility dataset derived from multi-source earth observation and geospatial data

Floods are among the most damaging disasters worldwide, necessitating high-resolution modeling of where terrain and environmental conditions predispose societies to flooding. While geospatial data are invaluable for flood susceptibility mapping, comprehensive and globally consistent datasets remain scarce. We present the Global Flood Susceptibility Map (GFSM v1), a globally harmonized 30 m flood susceptibility dataset produced by incorporating multi-source flood conditioning factors accounting for topographic, hydrological, meteorological, and anthropogenic characteristics within a machine learning framework. A gradient-boosted tree model was trained on approximately 17,000 curated grid tiles with approximately 30.45 million training samples, using a global flood inundation dataset as training labels and nine conditioning factors. The model was deployed across 192 geopolitical and climate units to generate global pixel-level susceptibility. Rigorous cross-validation indicates strong predictive accuracy, with a median area under the curve of approximately 0.95, and the output captures localized flood-prone regions worldwide. GFSM v1 offers an open, high-resolution global resource to support informed flood risk management, particularly in data-sparse regions.