Testing the performance of a near-real time flood mapping framework jointly assimilating water levels from river gauges and satellite flood maps

High resolution flood forecasting models integrated in Early Warning Systems (EWSs) can be supported by traditional (e.g., stage gauges) or innovative (e.g., Earth Observation – EO - data) sensors as inputs or observations for model calibration/validation or data assimilation. Stage gauges provide information only in specific points along the river network and could fail during extreme events. On the other hand, EO data could have strong limitations due by their spatial and temporal resolution, especially at medium-small scales. Therefore, multiple sources of distributed flood observations could represent a solution for managing uncertainties of flood models and lack of information left by each sensor.

In this work, a flood modelling approach is proposed for the joint assimilation of both water level observations and EO-derived flood extents. The assimilation approach implements a Ensemble Kalman Filter, whose forecasting model includes a parsimonious geomorphic rainfall-runoff algorithm (WFIUH) and a Quasi-2D hydraulic algorithm. To overcome stability issues related to the updating of the Quasi-2D hydraulic model, novel approaches are proposed to both assimilate multiple stage gauge observations and retrieve distributed observed water depths from satellite images. The flood modelling chain is tested both separately and jointly assimilating stage gauges and satellite derived flood extents on a flood event for the Tiber river basin in central Italy. Results reveal that the assimilation of observations from static sensors and satellite images led to an overall improvement of the simulation performances in terms of Nash-Sutcliffe efficiency Pearson correlation and Bias to the Open Loop simulation. Moreover, the joint assimilation of the abovementioned observations allowed to reduce the flood extent uncertainty as respect to the disjoint assimilation simulations for several hours after the satellite image acquisition.