Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery With Real-Time Gauges and Tweets

Flood inundation probability is critical for situation awareness, flood mitigation, emergency response, and postevent damage assessment. Current flood inundation mapping approaches can be categorized into real-time (RT) and near-RT (NRT) processes based on the timing of data acquisition. However, the intrinsic limitations of each category largely hamper their applications for flood mapping. Taking the 2015 South Carolina flood in downtown Columbia as a case study, this paper proposes a flood inundation reconstruction model by enhancing the NRT normalized difference water index (NDWI) derived from remote sensing imagery with the RT data including stream gauge readings and social media (tweets). Splitting into three modules: water height module, global enhancement module, and local enhancement module, the proposed model first incorporates the gauge readings and the NDWI image to reconstruct a macroscale flood probability layer, which is then locally enhanced using the verified flood-related tweets. The final output of the model matches well with the U.S. Geological Survey inundation map and its surveyed high-water marks. Results suggest that by enhancing NRT imagery with RT data sources, the proposed flood inundation probability reconstruction model renders a more robust, spatially enhanced flood probability index for emergency responders to quickly identify areas in need of urgent attention.