Crowdsourcing, Remote sensing and hydrogeomorphic modelling for supporting Data Assimilation for near real time flood forecasting in data scarce regions

The increasing availability of remotely sensed and social media crowdsourcing is bringing new distributed georeferenced data and opportunities for better monitoring and managing natural disasters. On the other hand, hydrogeomorphic models demonstrated their ability to parsimoniously delineate floodplain domains and assess river basin hydrologic forcing. In this contribution we present and test a Data Assimilation (DA) framework for real time flood forecasting supported by hydrogeomorphic floodplain terrain processing. The presented novel DA framework seeks to optimize the computational domain of a 2D hydraulic model, improving rapid flood detection using satellite images, and filtering geotagged crowdsourced data for flood monitoring. Different cases studies are presented where both traditional and innovative measurements are adopted, jointly and separately, for analyzing in comparative terms the performance of flood forecasting models with specific focus on the computational efficiency the ability to cope with data scarcity in ungauged river basins.