This thesis explores how floods and pandemics - two low-probability, high-impact, threat-driven disasters - can be compared through a structured risk-based lens. Both hazards challenge societies due to their rarity and large-scale consequences, but also allow for intervention before impact due to their forecastable nature. Despite extensive disaster literature, cross-hazard comparisons remain limited. This study fills that gap by asking: What can be learned from a structured risk-based comparison of floods and pandemics?

Focusing on the Netherlands, the research adopts a semi-qualitative, multi-method approach structured around three sub-questions: comparing risk systems (SQ1), case timelines (SQ2), and intervention effects (SQ3). First, bowtie analysis revealed four components shaping disaster risk: risk mechanisms, intervention mechanisms, enabling processes, and risk dynamics. Second, timeline analysis showed that floods are more sudden and measurable, while pandemics are prolonged and uncertain, complicating early decision-making. Third, modeling demonstrated that prevention is most effective for both hazards, though interventions differ in complexity and cost.

Key findings suggest that while disaster dynamics vary, shared governance strategies - such as early warning, scenario planning, and defining acceptable risk - can strengthen preparedness. Cross-hazard learning, particularly between structured flood management and adaptive pandemic response, offers valuable lessons for enhancing disaster governance.