We test and evaluate the results of the 2020 European Seismic Hazard Model (ESHM20) against observations spanning a few centuries at 12 cities in Romania. The full distributions of the hazard curves at the given locations were considered, and the testing was performed for two relevant peak ground acceleration (PGA) values. Our analysis suggests that the observed exceedance rates for the selected PGA levels are consistent with ESHM20 estimates.

The present work, developed within the EU H2020 European Coastal Flood Awareness System (ECFAS) project, presents an approach used to estimate direct impacts of coastal flood on population, buildings, and roads along European coasts. The findings demonstrate that the ECFAS impact approach offers valuable estimates for affected populations, reliable damage assessments for buildings and roads, and improved accuracy compared to traditional grid-based approaches.

Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep learning (DL) has emerged as a powerful alternative to conventional nowcasting technologies, but it still struggles to adequately predict impact-relevant heavy rainfall. We think that DL could do much better if the training tasks were defined more specifically and that such specification presents an opportunity to better align the output of nowcasting models with actual user requirements.