Modelling the Dynamic Impacts of Consecutive Natural Hazards: A Framework for Time-Evolving Vulnerability and Recovery Analysis

In an increasingly complex hazard landscape, understanding how physical damage evolves under multiple, interacting natural hazards has become a critical challenge for disaster risk assessment. When hazards occur simultaneously or in close succession, their combined impacts can severely compromise the physical integrity of exposed assets, often amplifying damage beyond what would be expected if each event were considered independently. Although the importance of these interactions is now widely recognised, existing approaches remain largely static, lacking the capacity to fully integrate hazards, temporal overlap, and recovery processes into unified frameworks. This research seeks to bridge these gaps by developing a generalised mathematical framework capable of quantifying direct physical damage in both concurrent and consecutive hazard scenarios. By modelling damage as a timedependent function linked to hazard intensity, asset vulnerability, and recovery dynamics, the framework captures both the amplification effects induced by simultaneous hazards and the evolving vulnerability resulting from incomplete recovery following sequential events. This dynamic treatment of damage and vulnerability enables a more realistic and temporally sensitive estimation of risk. The framework was operationalised through a modular Python environment and applied to a real-world multi-hazard case study in Puerto Rico. The analysis reveals that neglecting the residual effects of previous events, such as hurricane-induced damage, can lead to significant underestimations in assessing the impacts of subsequent events, such as earthquakes. Building on this physical damage modelling, the research introduces the Recovery Gap Index (RGI), a novel composite indicator designed to quantify the misalignment between cumulative damage and recovery capacity. The RGI offers a practical diagnostic tool to identify territories where delayed or insufficient recovery may exacerbate latent vulnerability over time. By capturing the temporal, systemic, and cumulative nature of disaster impacts, this thesis contributes an integrated approach to multihazard risk assessment. It supports both the advancement of scientific knowledge and the development of more informed, time-aware risk reduction strategies for complex, dynamic hazard environments.