Storyline climate attribution for compound flooding from tropical cyclone Idai in Mozambique.

It is widely recognized that climate change is altering the likelihood and intensity of extreme weather events globally, including hydrological extremes such as floods. Compound flooding is driven by fluvial, pluvial and coastal flooding occurring simultaneously, resulting in a potentially larger impact when co-occurring than the sum of the univariate drivers happening separately. Identifying and communicating the effect of climate change on compound flooding remains challenging. A method to quantify the effect of climate change on these events is through climate attribution assessments. 

This research assesses how existing climate attribution methods can be applied to compound events instead of univariate events. An event-based storyline attribution approach for compound flooding from historical tropical cyclones (TCs) in Mozambique is used to examine the effect of climate change on multiple flood drivers propagated to impact. 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 a highly destructive compound flood event. 

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, linked to a fast impact assessment tool Delft-FIAT to calculate the flood impact, here the direct economic damages. The 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 counterfactual scenarios. The compound flooding is modelled for the multiple factual and counterfactual scenarios, adjusting the separate drivers individually and simultaneously.  

This approach enables the attribution of climate change effects on compound flooding from TCs while identifying potential changes in the contributions of individual flood drivers. Next steps include attribution uncertainty partitioning, comparing multiple climate attribution approaches for these events, assessing regional differences with relation to climate change effects on compound flood impact and comparing this methodology for multiple TCs in the same region, which may have different driver contributions.