Unpacking and navigating complexity in disaster risk reduction

Disaster risk reduction (DRR) is a highly complex undertaking, carried out within dynamic and interconnected human-environment systems. This high level of complexity poses persistent challenges for planning, coordination, and implementation of DRR efforts. Although widely acknowledged, the broad notion of “DRR complexity” remains under-specified, limiting a harmonized and comprehensive understanding of the topic across disciplines and sectors. Drawing on disaster research scholarship, we propose a conceptual lens consisting of five tangible and recurrent complexity dimensions: 1) system interdependency; 2) multi-level governance; 3) functional overlap; 4) uncertainty in data, models and predictions; and 5) political volatility. Using an integrative approach, we empirically evaluate these dimensions across 58 case studies worldwide and demonstrate that all five consistently shape DRR processes. These dimensions frequently co-occur and interact, creating challenges practitioners must routinely navigate. Yet our analysis also show that complexity can sometimes trigger rapid institutional adaptation and improved coordination, depending on factors like perceived urgency of the impacts. Rather than a barrier, complexity should be acknowledged as a reality to be continuously engaged with. Embracing the inherent complexity of DRR may transform it into an innovation and continuous learning engine, provided that long-term resources support these adaptive efforts.