Abstract
ABSTRACT This paper explores how crowdsourced social media data complements urban flood modelling to improve model performance and achieve a better classification of impacts. In addition to georeferencing flood impacts, Twitter allows monitoring the events in terms of hazards and impacts, and YouTube facilitates a retrospective analysis from audiovisual data. The analysis of 2800 tweets collected during four storm events and of almost 900 videos of the recent history of the basin, together with the implementation of a high-resolution model, contributed to the expansion of the capacity to represent the temporal and spatial scales of the problem. The complementation of crowdsourced social media data and urban modelling enhances the understanding of the flood dynamics, thus offering a framework of greater certainty for the generation of flood risk management products.
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