RETURN – Abacus of Measures

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Authors: Daniela Molinari, Francesco Airoldi, Panagiotis Asaridis, Anna Rita Balingit, Tommaso Bastiani, Martina Bosone, Diana Caporale, Fabio Castelli, Luca Cetara, Emilia Corradi, Cassandra Cozza, Pasquale De Toro
+11 more Paola Fontanella Pisa, Camillo Frattari, Filippo Fraschini, Daniela Mele, Simona Muratori, Daria Ottonelli, Gloria Padovan,  Federica Romagnoli, Alessandro Rubino, Francesca Vigotti, Lorenza Petrini
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Abstract

An operational tool for supporting risk reduction in multi-hazard contexts, known as the Abacus of Measures, which characterizes the most relevant existing structural and non-structural risk reduction measures. It considers five natural hazards: floods, landslides, droughts, volcanic activity, and earthquakes, and aims to assess whether a measure designed to mitigate a specific risk may also have additional effects on other co-existing risks, either reducing or increasing them. The Abacus of Measures has been developed within the RETURN project (multi-Risk sciEnce for resilienT commUnities undeR a changiNg climate). Data Description The Abacus of Measures is provided in a .xlsx file. Each row of the abacus represents a class of measures supplying the same function (e.g., discharge reduction or physical vulnerability reduction). The columns describe the main attributes used to characterize the measures, including their spatial and temporal scales of effectiveness, and their effects on the different natural risks. The last column lists the reference literature supporting the characterization of each measure. The rows are grouped according to the risk for which the measures are primarily designed (target hazard) and the phase of the natural risk management cycle to which they correspond (i.e., mitigation, preparedness, response, and recovery).

Fields of Study & Subjects

Disaster Management and Resilience Flood Risk Assessment and Management Infrastructure Resilience and Vulnerability Analysis

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
26 January 2026
Language
EN
Document type
Dataset