A spatially and temporally disaggregated inland flood dataset with flood metrics (2000-2024)

Abstract. Floods are some of the most widespread and expensive natural disasters globally, yet current flood research suffers from a lack of detailed historical data on flood events and their impacts at fine spatial and temporal scales. We created a spatially and temporally disaggregated global flood dataset over 2000-2024 by integrating flood disaster records from EM-DAT with satellite-based flood detection and gridded population data. Starting with 4,073 inland flood events, we disaggregate each event to the admin1-month scale (first-level administrative regions by calendar month), generate satellite-derived flood maps using MODIS surface reflectance imagery, and combine these maps with population density data to calculate direct flood-exposed population. The resulting dataset contains 23,334 admin1-month flood records across 2,375 unique administrative regions in 177 countries, with satellite-derived metrics including flooded area and flooded population. Flooded population estimates based on the satellite flood maps are correlated with reported impact metrics, validating our approach despite known limitations in the flooded pixel detection algorithm.