Automated Detection of Flood Events from CYGNSS: Observing Flood Evolution Along Propagating Tropical Waves

Flooding is a major natural hazard across the global tropics. Although flood occurrence is shaped by rainfall characteristics—including duration, frequency, and intensity—accurate prediction remains challenging. A key limitation is the lack of reliable, long-term flood databases that capture events across all spatial scales and durations, hindering a clear understanding of how rainfall variability translates into flood onset. This limitation is particularly critical in the Maritime Continent, where extreme rainfall is common and many small, short-lived, yet severe, floods remain undocumented. To address this limitation, we investigate whether a relatively new approach, global navigation satellite system reflectometry (GNSS-R), can help close this observational gap.

In this work, we assess whether data from the CYGNSS small-satellite constellation can be used to identify small- to regional-scale floods, including short-lived events. Our study focuses on Sumatra, an island within the Maritime Continent that is frequently affected by such hazards. A joint analysis of CYGNSS inundation estimates and two independent flood databases allowed us to evaluate how CYGNSS measurements can be used for flood detection. Three detailed case studies demonstrate that CYGNSS provides an unprecedented ability to monitor day-to-day changes in surface water extent, including floods at the urban scale. Specifically, we show that CYGNSS-derived inundation anomalies can clearly capture evolution of a flooding event, with the largest signature one day after known flood initiation. A systematic analysis of 555 flood events over a 21-month period enabled us to identify characteristic patterns in inundation anomalies that reliably distinguish flood events from non-flooding conditions, through the definition of an inundation-anomaly threshold and a maximum distance between CYGNSS detections and reported flood locations. We established that CYGNSS observations within 15 km not-only significantly differ from base-line conditions, but they allow tracking day-to-day flood dynamics as well.

The proposed methodology is transferable and can be applied to establish flood-inundation thresholds for any region within the global tropics, enabling automated detection of previously unreported flood events or the study of relationships between extreme precipitation and flood evolution. An example of its application is the automatic detection of flooding from CYGNSS data associated with subseasonal variability in tropical circulation: the passage of multiple convectively coupled Kelvin waves embedded within an active Madden–Julian Oscillation in July 2021. These waves propagated eastward across the Maritime Continent, triggering extreme rainfall and widespread flooding in equatorial Indonesia and East Malaysia. The day-to-day evolution of floods could be observed alongside the propagating waves, with the termination of the MJO coinciding with the cessation of the flood events.

Relying on low-cost small satellites, this approach shows strong potential for future scalability with larger constellations, ultimately improving flood monitoring and advancing our understanding of how rainfall patterns shape flood dynamics across global tropics.