Multi-hazard and cascades
Background
The UNISDR (United Nations Office for Disaster Risk Reduction, 2016) defines multi-hazards as “events [that] may occur simultaneously, cascadingly, or cumulatively over time, considering the potential interrelated effects.” Due to difficulties in recognizing, understanding, and defining the interrelationships between hazards, as well as the lack of data on their co-dependence, most "multi-hazard risk" models merely overlay individual hazards without accounting for their interactions. Even when hazard–hazard interactions are included in risk models, there remains a lack of comprehensive approaches that capture the complex interactions between hazards, exposure, and vulnerability beyond simple spatial overlaps. These interactions are critical because risks may cluster in both space and time or amplify each other. However, hazard–exposure relationships, changes in exposure over time, and vulnerability are essential for fully characterizing multi-risks. This complexity means that multi-hazard risk modeling can be computationally expensive and demand high-quality input data. Additionally, multi-hazard models may be constrained by the diversity of hazard types that can be incorporated, mismatches in spatial and temporal scales, and complex data requirements.
Another challenge is the increasing need for risk assessments at national, regional, or global scales to understand potential impact patterns, provide science-based evidence for disaster risk reduction, and enable coordinated planning. At the same time, data availability is improving, with higher spatial and temporal resolution information on populations, building stock, topography, and hazard drivers such as rainfall forecasts or observed precipitation. While this progress is valuable, it also results in higher computational demands due to the need for larger spatial scales and finer spatio-temporal resolution data. Addressing this challenge of balancing scale with resolution is an ongoing issue for multi-hazard risk assessment. Furthermore, the distribution of risk may be highly uneven, with exposed elements concentrated in specific areas. This spatial imbalance means that grid-based or GIS-based approaches to risk modeling may require significant computational resources for areas with low or negligible risk.
Key scientific questions
- Where do clusters and hotspots of multi-hazards occur in a given region?
- What are the different spatial and temporal interdependencies between the different hazards?
- What are the implications of our multi-hazard methodologies on supporting disaster risk reduction, management, and response?
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