SWOT satellite assessment for monitoring the extreme 2025 floods in Australia’s inland Channel Country

Abstract In early 2025 mainland Australia experienced one of the most extreme inland flooding events in the observational period. Extensive floodwaters gradually flowed southward toward Kati Thanda–Lake Eyre via the ‘Channel Country’, with the Cooper Creek basin being one of the most severely affected regions. Typically considered as a low-gradient dryland catchment, the Cooper recorded its highest-ever flood with water depths reaching 13.4 m in the more confined locations and with a flood width up to 55 km; surpassing the previous historical 1974 record flood. The flood caused severe impacts on local communities and infrastructure, damaging roads, agriculture, and resulting in major livestock losses. Despite the flood’s severity, monitoring was limited to just four active gauging stations with none on the lowermost 350 km. We developed a novel approach for assessing the magnitude of this event by combining Surface Water and Ocean Topography (SWOT) Pixel Cloud data product, optical satellite imagery and a Lidar-derived digital elevation model to assess both flood depth and volume. SWOT-derived water depth hydrographs were validated against water level data at four gauging stations, showing excellent agreement with errors as small as ±11 cm root mean square error and ±8.1 cm mean absolute error. By coincidence, the flood’s peak at two gauge locations (Cullyamurra waterhole and Nappa Merrie) coincided with the SWOT data period of acquisition, providing a valuable flood snapshot. Backscattering analysis of the peak flood (mean σ 0 = 10–24 dB) reveals that SWOT effectively captures the complex backscatter response of dryland floodwaters, reflecting strong sensitivity to the heterogeneous flow regime of dryland channels. Furthermore, comparison with the JRC Global Flood Model further revealed substantial underestimation of flood volume (23%–48%) in existing global datasets across Australia’s Channel Country. Overall, this study demonstrates the capability of SWOT to capture and monitor extreme flooding in low-gradient dryland rivers, providing new opportunities for flood assessment and management in data-sparse regions.