Global Flood Monitoring with SWOT: Automated Detection and Analysis of Flood Occurrence

Floods are among the most destructive natural hazards globally, affecting billions of people over recent decades. While several databases track flood impacts or inundation extent, no existing system systematically characterizes the hydraulic properties of floods at a global scale. The recent launch of the Surface Water and Ocean Topography (SWOT) mission provides simultaneous measurements of water surface elevation and inundation extent, offering an unprecedented opportunity to evaluate flood dynamics worldwide. However, it remains unclear how consistently and under what conditions SWOT observes flood events. Using a novel compilation of recorded flood events since SWOT’s launch (Fear et al. 2026), I assess whether and how each flood was captured by SWOT between March 2023 and the present. Using an automated processing pipeline, I analyze changes in water surface elevation and inundation extent across pre-flood, flood, and post-flood windows to quantify hydrologic signals associated with each event. I then compare observation rates across floods of varying duration, magnitude, and spatial extent to evaluate SWOT’s sensitivity to different flood characteristics. Preliminary results indicate that SWOT preferentially observes floods that are spatially extensive or long-lasting, while short-duration or localized events are more likely to be missed due to orbital timing constraints. Out of a compiled database of over 4,000 floods/events occurring between August 2023 and February 2024, 202 were captured as flood signals in SWOT imagery, with an additional 339 as being above baseline water volume. These findings provide the first global-scale assessment of SWOT flood detection capability and establish a validated database and analytical framework for future research, improving our understanding of both global flood occurrence and the practical utility of SWOT for characterizing flood occurrence patterns and individual flood events.