Storyline impact attribution of climate and exposure drivers of compound flood impact from tropical cyclone Idai in Mozambique

While climate change continues to exacerbate extreme weather events worldwide, their impacts can be further amplified or dampened by socio-economic drivers that influence local exposure and vulnerability. These drivers, such as population growth and economic development, are dynamic and influence the impact of extreme events on global and local scales. For example, population growth in flood-prone areas can heighten exposure, whereas economic development can increase economic losses while improving the capacity to cope with a disaster and thus reduce vulnerability. A novel method to quantify the effect of climate change and socio-economic drivers on the impact of these events is through impact attribution assessments.

This research expands a storyline attribution framework for quantifying the effects on climate change on the hazard and impact of compound flooding to also include the effect of socio-economic drivers. An event-based approach for compound flooding from tropical cyclone (TC) Idai in Mozambique is used to disentangle the effect of historical climate and population change. TC Idai hit Mozambique in 2019 and caused over 600 fatalities, affected over 1.8 million people, resulting in $3 billion in damages. Idai is used as a case study, representing an extremely destructive compound flood event in a underrepresented, data-poor, and highly vulnerable region.

Compound flooding is modelled using a state-of-the-art hydrodynamic modelling chain that combines the Super-Fast INundation for coastS (SFINCS) model with the hydrodynamic model Delft3D Flexible Mesh and hydrological model wflow. The climate drivers of compound flooding from TCs that are known to be affected by climate change, such as precipitation, wind and sea-level rise, are adjusted to create scenarios with the climate trend removed. Present-day and historical population scenarios are used as exposure data to assess the effect of population change based on a novel harmonized global population dataset (Paprotny, 2025). By modelling multiple factual and counterfactual scenarios, in which drivers are adjusted individually and jointly, we disentangle their respective contributions to the affected population and fatalities.

This approach advances event-based impact attribution by incorporating non-climatic drivers into assessments of compound flood impacts from TCs. The framework relies solely on global datasets and open-source software which allows worldwide applications, including highly impacted but data-scarce and often underrepresented regions.