Exploitation of satellite data to evaluate Digital Twins of Coastal Urban Areas

Extreme weather events, sea level rise and coastal erosion are major issues that need to be urgently addressed in European coastalcities. Smart technologies can support Coastal Cities Living Lab (CCLLs) in detecting potential risky conditions in the study area and co-designing adaptation solutions, like Nature Based Solutions (NBS) and Ecosystem-Based Adaptation (EBA). A GIS-based Early-WarningSupport (EWS) system and Digital Twin (DT) platform presented here can be deployed in the CCLLs to assist both the planning of long-term climate resilience strategies and decision making in case of risky events. We show how the the effectiveness of these solutions isevaluated by exploiting multi-temporal flood maps obtained from satellite in the coastal area of Massa (Italy). Specifically, we aredeveloping a general analysis tool for testing outputs of hydraulic and hydrological models included in the DT exploiting synthetic apertureradar (SAR) images such ad Sentinel-1 or COSMO-SkyMED, or optical imagery like Sentinel-2 available for major flood events in thestudy area in the last decade. Information on real case studies are retrieved by municipality report archives, Floods Directive maps, localnewspapers and exploration of regional agencies website and Copernicus Services. Flood hazards maps in terms of flood extent and,wherever possible, water depth or flow velocities distributions, simulated by the models implemented in the DT will be thus compared toreal flood events mapping and validated. Evaluation of the effectiveness of the procedure and solutions and their possible incorporation inenvironmental monitoring, early-warning systems, and decision-making processes at different CCLLs will be then assessed throughmeetings with relevant users. This work is supported by the project SCORE (Smart Control of the Climate Resilience in European CoastalCities), funded by the European Commission’s Horizon 2020 research and innovation programme under grant agreement No. 101003534.