Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping

Hydro-meteo hazard early warning systems (EWSs) are operating in many regions of the world to mitigate nuisance effects of floods. EWS performances are majorly impacted by the computational burden and complexity affecting flood prediction tools, especially for ungauged catchments that lack adequate river flow gauging stations. Earth observation (EO) systems may integrate the lack of fluvial monitoring systems supporting the setting up of affordable EWSs. But, EO data, constrained by spatial and temporal resolution limitations, are not sufficient alone, especially at medium-small scales. Multiple sources of distributed flood observations need to be used for managing uncertainties of flood models, but this is not a trivial task for EWSs. In this work, a near-real-time flood modelling approach is developed and tested for the simultaneous assimilation of both water level observations and EO-derived flood extents. An integrated physically based flood wave generation and propagation modelling approach, that implements an ensemble Kalman filter, a parsimonious geomorphic rainfall-runoff algorithm (width function instantaneous unit hydrograph, WFIUH) and a quasi-2D hydraulic algorithm, is proposed. An approach for assimilating multiple stage gauge observations is proposed to overcome stability issues related to the updating of the quasi-2D hydraulic model states. Furthermore, a methodology to retrieve distributed observed water depths from satellite images to update 2D hydraulic modelling state variables is implemented. Performances of the proposed approach are tested on a flood event for the Tiber River basin in central Italy. The selected case study shows varying performances depending on whether local and distributed observations are separately or simultaneously assimilated. Results suggest that the injection of multiple data sources into a flexible data assimilation framework constitutes an effective and viable advancement for flood mitigation to tackle EWS uncertainty and numerical stability issues. Specifically, our findings reveal that the simultaneous assimilation of observations from static sensors and satellite images led to an overall improvement of the Nash-Sutcliffe efficiency (NSE) between 5g% and 40g%, the Pearson correlation up to 12g% and bias reduction up to 80g% with respect to the open-loop simulation. Moreover, this combined assimilation allows us to reduce the flood extent uncertainty with respect to the disjoint assimilation simulations for several hours after the satellite image acquisition