The study illustrates the methodology developed for flood risk assessment for road and railway infrastructures. Through the creation of a detailed database, using different data sources, and the definition of a risk matrix, a risk level (High, Medium, Low and Null) is assigned to each section, considering the physical and functional characteristics of the infrastructure, as well as its relevance and the magnitude of the expected event.

INSYDE 2.0, a tool for flood damage modelling  to residential buildings. By incorporating ultra-detailed survey and desk-based data, it improves the reliability and informativeness of damage assessments while addressing input data uncertainties.

This paper presents a conceptual model for the estimation of flood damage to power grids and reviews the available methodologies, to better understand current modelling approaches, challenges, and limitations. The model adopts an interdisciplinary and multi-scale evaluation approach to handle the complex damage mechanisms and capture the cascading effects. In doing so, it adapts to different geographical and economic contexts, allowing stakeholders to implement comprehensive damage assessments.

The paper discusses challenges (and solutions) emerged during a collaboration among practitioners, stakeholders, and scientists in the definition of flood damage maps in the Po River District. Social aspects were proven to be fundamental components of the risk assessment; variety of competences in the working group was key in finding solutions and revealing weaknesses of intermediate proposals. This paper finally highlights the need of duplicating such an experience at a broader European level.

This study presents a replicable procedure for the adaptation of synthetic, multi-variable flood damage models among countries that may have different hazard and vulnerability features. The procedure is exemplified here for the case of adaptation to the Belgian context of a flood damage model, INSYDE, for the residential sector, originally developed for Italy. The study describes necessary changes in model assumptions and input parameters to properly represent the new context of implementation.