Flash Flood Reconstruction and Analysis—A Case Study Using Social Data

Abstract

This work proposes a methodology for post-flood analysis in ungauged basins with low data availability located in semi-arid regions. The methodology combines social perception with recorded data. Social perception can be a useful tool to enhance the modeling process in cases where official records are nonexistent or unsatisfactory. For this aim, we structured a four-step methodology. First, we create a repository with the information that reconstructs the analyzed event. Photos and news of the flood event are collected from social media platforms. The next step is to consult official government agencies to obtain documented information about the disaster. Then, semi-structured interviews are carried out with residents to obtain the extension and depth of the flooded spot. This social information creates an overview of the flood event that can be used to evaluate the hydraulic/hydrological modeling of the flood event and the quality of the recorded data. We analyzed a flood event in a city in semi-arid Brazil. The event caused several damages such as the breaking of dams and about 40% of the population was somehow impacted although the official rain data pointed to non-extreme precipitation.