Author(s): Antonio Annis; Noemi Gonzales; Fernando Nardi; Fabio Castelli
Linked Author(s): Fabio Castelli
Keywords: Data Assimilation; 2D-Hydraulic modelling; Flood forecasting; Flood mapping
Abstract: The intensification of flood-related damages and fatalities is challenging Early Warning Systems (EWS) to always better perform in predicting flood levels allowing decision makers to take the most effective decisions for mitigating the impact of extreme events. EWS require hydrologic and hydraulic modelling that are usually affected by uncertainties that can be extremely significant in data scarce regions. This work presents the implementation and application of a Data Assimilation (DA) framework, based on the Ensemble Kalman Filter, integrating the hydraulic model FLO-2D and geospatial algorithms for data post-processing and mapping. The hydraulic model is forced by both flow gages and simulated flow data produced by a simplified GIS-based hydrologic modelling for flood wave analysis tailored for small ungauged basins. The hydraulic code is adapted to assimilate different observation data types: flow measurements taken along the channel, water level observations captured within the floodplain, such as water signs on vegetation and buildings pictures by human sensors, and inundation extents obtained by processing satellite images. This DA framework required the development of significant novelties for incorporating the 2D hydraulic model and for integrating the different types of measurements considering the heterogeneous specifications and uncertainty of the various assimilated data types. Advanced GIS algorithms are implemented for improving the real time flood mapping taking advantage of the distributed output provided by the 2D inundation model. Results show improved model performances in terms of water level simulations and reduced uncertainties. The integrated hydraulic and geospatial modelling allows to empower the water levels correction on the flood extension prediction. Additionally, the capability of using the different available observations, from satellite images to crowdsourced data, is promising for the development of a flexible and scalable flood EWS model overcoming the limitations of standard DA working generally with 1D hydraulic models and traditional sensors.