Decoding Multi-Hazard Disasters: A Forensic Meta-Analysis using the PARATUS Forensic Analysis Framework

While forensic methodologies for disaster analysis have been proposed and applied for more than a decade, a structured meta-analysis of multi-hazard events—revealing patterns and existing gaps across the disaster risk management cycle—remains significantly underexplored. This study presents a comparative analysis of five major multi-hazards events using the PARATUS approach, which integrates disaster analysis (Forensic analysis) with risk analysis (Impact Chains), specifically we compared: the 2017 Hurricane Irma (Sint Maarten), the 2018 Vaia Windstorm (Italian Alps), Gloria Storm 2020 (Catalonia), the 2021 La Soufrière volcanic eruption (Saint Vincent), and the 2023 Kahramanmaras earthquakes (Türkiye).

Beyond variations in compound and cascaded hazard combinations, these events encompass a range of geophysical and environmental conditions across areas with distinct socio-economic patterns. The PARATUS framework was selected for its structured, temporal alignment with disaster phases: pre-disaster conditions, hazard and impact analysis, recovery, and resilience building and the use of innovative conceptualisation tools such as impact chains.

By categorising multi-hazard events according to Tilloy et al. (2019), the meta-analysis provides evidence that these events amplify impacts and hinder response. Evidence for this amplification is found across the following categories: independent hazards (e.g., concurrent volcanic, pandemic, and disease events), triggering hazards (e.g., an earthquake cascading into landslides and liquefaction), and compound hazards (e.g., consecutive severe storms).

The meta-analysis underscore the relevance of investigating the social dimension of risk to formulate effective long-term risk-reduction and mitigation strategies. This is evident across the sections of Paratus' forensic framework: first, the pre-disaster conditions are shaped by institutional, social, economic, and environmental vulnerabilities, often driven by unplanned development, poverty, and weak governance. Subsequently, during events, response and early warning systems are frequently hindered by poor coordination and inadequate communication with marginalised groups. Furthermore, post-disaster recovery, while focused on restoring infrastructure and finance, often adopts top-down approaches that neglect community engagement and long-term equity.

Despite significant progress in hazard and risk understanding and the identification of necessary risk management measures, this advanced knowledge has not yet been fully translated into consistent application, updated regulations, or comprehensive resilience planning. Consequently, critical resilience gaps persist, including unaddressed infrastructure vulnerabilities, insufficient community preparedness, fragmented emergency coordination, and a lack of financial risk-transfer mechanisms.

Finally, the forensic analysis framework is well-suited to meta-analysis due to its comprehensive, methodical structure, which ensures consistent, multidimensional data synthesis across diverse disaster events.