Impact Webs: A novel conceptual modelling approach for characterising and assessing complex risks

Abstract. Identifying, characterising and assessing the complex nature of risks is vital to realise the expected outcome of the Sendai Framework for Disaster Risk Reduction. Over the past two decades, the conceptualization of risk has evolved from a hazard-centric perspective to one that integrates dynamic interactions between hazards, exposure, systems vulnerabilities and response risks. This calls for a need to develop tools and methodologies that can account for such complexity in risk assessments. However, existing risk assessment approaches are hitting limits to tackle such complexity. To this aim, we developed a novel complex risk assessment methodology named ‘Impact Webs’, inspired by a conceptual risk modelling approach named Climate Impact Chains that integrates aspects of various other conceptual models used in risk assessments such as Causal Loop Diagrams and Fuzzy Cognitive Mapping. Impact Webs are developed in a participatory manner with stakeholders and characterise and map interconnections between risks, their underlying hazards, risk drivers, root causes, responses to risks, as well as direct and cascading impacts across multiple systems and at various scales. In this methodological paper, we show how we developed the Impact Web methodology, including which elements we use to populate the model and steps we followed for construction. As proof of concept, we present the results of a complex risk assessments in Guayaquil, Ecuador, which investigated how COVID-19, concurrent hazards and responses propagate risks and impacts across sectors and systems during the pandemic. Reflecting on the utility of Impact Webs, application in case studies demonstrates the methodologies usefulness for understanding complex cause-effect relationships and informing decision-making across different scales. The participatory process of developing Impact Webs promotes stakeholder engagement, uncovers critical elements at risk and trade-offs in decision making, helping to evaluate both positive and negative outcomes of disaster risk management practices.