This pilot application will exploit the new capacities for designing and delivering innovative services for extreme-scale hydro-meteorological modelling, using Copernicus data and core services directly ingested through the Copernicus Open Access Hub APIS, and the DIAS platform, as well as citizen scientists data, to enable more precise predictions and decision-making support for high impact weather events in urban and peri urban environment. Contributing to the Disaster Resilience SBA, one of the main activities listed in the GEO Space and Security Community Activity is to get maximum benefit from the use of large and heterogeneous datasets to potentially fill in the observational and capability gaps at EU decision making level. To this end, the application proposes also the integration of the datasets and tools made available in the frame of the pilot application (weather, citizen science, hydrological and fire models, RASOR platform, Change Detection Maps based on Sentinel-1 and the BEYOND (www.beyond-eocenter.eu) platform – FIREHUB and FloodHub - in synergy with partner NOA and the pilot for resilient ecosystems) for the assessment of impact of natural hazards over areas of interest with regard to human security issues.
CIMA, EU SATCEN, NOA
Civil Protection Agencies, hydro-meteorological predictions agencies, disaster risk reduction institutions
Citizen scientists data from data sources such as Meteonetwork
Sentinel-1 IW/ SM (GRD/SLC), Sentinel-2 L1C, Sentinel-3 L2
Prediction data for rainfall, temperature, wind, relative humidity, pressure, soil moisture, water level discharge, fire risk, etc.
Statistics on number of people affected by a given natural hazard (flood, wind storm, forest fire)
Statistics on economical impacts/costs by a given natural hazard (flood, wind storm, forest fire)
The pilot will assess for selected case studies the possible added value of the assimilation of in situ observations and Copernicus data for severe hydro-meteorological events over Italy
The e-shape project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 820852