Multi-hazard risk assessment in data-scarce cases: A new publication presenting the case of Burundi

Hazard risk assessments are crucial for understanding disaster risk and generating knowledge required for disaster risk reduction. Hereby, a major request to research is to untangle the interactions and cascades between different hazards considering their interdependencies, spatial and temporal overlaps, and compounding effects.

A particular challenge arises when such comprehensive analyses are attempted in regions where data are scarce. The case study of Burundi exemplifies this difficulty. The country is highly exposed to a range of natural hazards—including floods, torrential rainfall, landslides, earthquakes and strong winds—yet the availability and quality of observational and statistical data are limited, fragmented, and often inconsistent.

To address these limitations, the study adopts a pragmatic and integrative methodological framework that combines multiple data sources and modelling approaches. Local datasets are complemented with global and satellite-derived information to construct a consistent exposure database. Hazard-specific models are developed, including statistical susceptibility modelling for landslides and climate-based analyses for hydro-meteorological hazards. These are complemented with a socioeconomic vulnerability assessment – under the lead of the GLOMOS team, allowing the researchers to move beyond purely physical risk and capture the societal dimension of disaster impacts.

A key element of the methodology is the harmonisation of heterogeneous information into a common risk metric. Using probabilistic approaches, the study estimates risk in terms of Annual Average Loss (AAL), enabling direct comparison between different hazards and across administrative units. This unified framework makes it possible to assess spatial patterns of risk at national scale, despite the underlying data uncertainties.

The results provide the first comprehensive, nationwide multi-hazard risk picture for Burundi of this kind. They reveal how different hazards contribute to overall risk in varying regions, offering an approximate but actionable estimate of potential economic losses. Importantly, the study establishes a baseline for identifying priority areas for disaster risk reduction and for guiding investments in resilience.

Beyond its immediate findings, the publication demonstrates that robust multi-hazard risk assessments are feasible even in data-scarce environments. By transparently combining diverse datasets and explicitly addressing uncertainties, it offers a replicable blueprint for other countries facing similar constraints. In this sense, the study is not only a milestone for Burundi, but also a significant contribution to advancing multi-hazard risk science in underrepresented regions.