Disasters classification in a compound event perspective: insights from existing databases

Natural disasters often result from compound event dynamics, in which multiple interacting drivers converge across spatial and temporal scales, significantly amplifying their impacts. The concept of compound events has gained increasing attention in recent literature, offering opportunities to enhance disaster understanding, while also presenting challenges and open issues for modern risk assessment frameworks. This study investigates the capability of existing disasters/extreme events databases (Emergency Events Database EM-DAT, Severe Weather Data Inventory SWDI, and Canadian Disaster Database CDD) to capture compound event dynamics, and assess the accuracy of reported impacts. We found that SWDI, a national dataset for the USA, reports a high number of compound events versus single events, always higher than 50%, except for wildfires, and its structure allows for accurately identify spatially compounding events. This percentage in EM-DAT, a global dataset, is always lower than 50%, except for storms. A good match in events occurrences can be observed between the three databases, however the agreement in terms of deaths and injures varies depending on the databases compared. Finally, the work highlights the limitations of existing databases in representing the multidimensional nature of risks, and the cascading impacts that emerge from compound hazards. Reclassifying disasters from a compound event perspective not only enriches our knowledge of hazard dynamics, but also provides actionable pathways for improving risk assessment, informing adaptive policies, and enhancing resilience to the growing complexity of environmental challenges.