Advancing Near-Real-Time Flood Inundation Mapping in Australia

Abstract

Floods are the second-most deadly natural hazard in Australia, following heatwaves. Monitoring flood extent and depth in near real-time (NRT) is crucial to minimize loss of life and socio-economic impacts. This study leverages advanced computing, data management systems, and high-quality data, including river gauge data APIs and Australian Water Outlook, Digital Earth Australia, Google Earth Engine and Amazon Web Service, to develop a flood monitoring workflow in Australia. Our framework provides NRT 5-m spatial resolution flood extent and depth maps using airborne LiDAR observations through three approaches: (a) gauge data, (b) coupled hydrological and hydrodynamics model, and (c) satellite observations (i.e., Sentinel-1, Sentinel-2, Landsat-7/8/9). We evaluated this flood monitoring framework in seven river catchments across Australia, using both deterministic and ensemble modes. This study highlights the importance of low-latency gauge data for flood monitoring, as well as the necessity of high-resolution airborne LiDAR DEMs for accurate flood mapping. In ungauged areas, the ensemble modeling approach enhances the model's ability to capture flood inundation dynamics. In cases where this remains challenging, multi-source remote sensing can help mitigate the limitations of the modeling approach. We also demonstrated the potential for transferring this flood monitoring framework to other regions around the world. Overall, this study advances the operationalization of high-resolution flood analytics, offering a replicable blueprint to strengthen community resilience against escalating flood risks under climate change.