The objective of Snowball project is to develop a support tool for the decision makers in the framework of emergency planning and preparedness enhancing at local, national and international level. To this aim, a theoretical model for cascading effects simulation is developed as methodological framework for modelling and simulation. Snowball theoretical model is based on the refinement of a consolidated methodology built-up in previous and ongoing research projects, where LUPT-PLINIVS played a key role in the problem conceptualization and software modelling implementation.

Simulation models based on Snowball theoretical model methodology are able to produce cascading effects scenarios with different level of detail, depending on the availability of inventory/exposure data for the different categories of elements at risk and hazard/impact models for the various hazard sources.

In this way, the architecture of the simulation model can be conceived as a flexible structure of different building blocks, initially fed – only for a limited number of hazard types – with general data and models from public repositories that can be further detailed by using regional/local datasets and models. Indeed, only this kind of refinement can ensure a higher reliability of output scenarios and data, required to effectively support decision making process in the field of emergency management.

From a theoretical point of view, the probability of occurrence of a chain of cascading effects generated by a certain triggering event is function of the conditional probability that the last event of the chain will occur given the knowledge that the previous event of the chain has already occurred. Other key variables such as exposure, vulnerability and human behaviour are also crucial conditions influencing the impact of cascading effects on the elements at risk identified. Few studies focus on the topic of cascading effects modelling, and a common consolidated framework can be found in literature in the field of multi-risk assessment, where crucial aspects such as the dependencies between different hazards and the identification of transition are taken into account.

Independently from the magnitude of the triggering hazard and the potential cross-border impacts, cascading effects mostly depend on local (i.e. national to regional) hazard proneness and vulnerability conditions (e.g. Fukushima), so the only way to produce reliable and effective hazard/impact scenarios through probabilistic-based simulation tools is to perform at the local level the following steps:

  • hazards characterisation according to the proneness of the area or the preferences of decision makers/end-users (including probabilistic assessment);
  • exposure and vulnerability analysis, according to the elements at risk identified and to specific decision-makers/end-users requirements;
  • identification of probabilities of transition among different hazards, supported by existing literature/studies complemented with Bayesian approach and/ or experts’ elicitation procedures, when such information is not available from previous studies.

Thus, the proposed approach for the SNOWBALL theoretical model is the following:

  1. to provide a “generic” modelling framework based on the definition of a common logic to model the dependencies between the different hazards and the relevant parameters for the “elementary bricks” as defined in WP3 (space, time, hazard, exposure, vulnerability, dynamic vulnerability, damage, human behaviour);
  2. to apply specific models and simulations for the respective use cases, in line with end-users needs and compatible with eventually existing legacy simulation tools, understood as the best approach to provide a decision support tool useful in the context of preparedness to real crises involving cascading effects. This step will in fact provide the needed specialization and customisation of the theoretical level in the context of the different use-cases through the support of Snowball experts, also involving local responsible for civil protection and modelling experts.