A Coupled Human And Natural System (CHANS) Framework for Human Mobility during Flood Events

Climate change and rapid urbanisation have intensified the frequency and consequences of extreme flood events. During floods, transportation systems may fail, leading to traffic breakdowns, prolonged exposure, and cascading impacts on emergency response and wider urban functioning. Flood risk is therefore shaped not only by the physical dynamics of inundation, but also by how people perceive, respond to, and adapt their mobility under evolving hazard conditions, exemplifying a Coupled Human And Natural System (CHANS). This tight coupling between hazard evolution and human response makes it essential to represent hazard-human interactions at the event timescale, particularly for reliable flood forecasting, early warning, and emergency preparedness. However, capturing adaptive human mobility under dynamically changing flood conditions remains a major challenge, especially within a CHANS modelling framework.

Agent-based modelling (ABM) has been increasingly applied to represent human behaviour during floods, often coupled with hydrodynamic inundation models. However, most existing implementations rely on offline or weakly coupled co-simulation, in which flood dynamics and human behaviour are computed in separate platforms and synchronised through frequent data exchange. Such data-exchange-driven approaches become increasingly expensive when high-frequency updates are required, limiting their capability to represent real-time feedback between flood evolution and human mobility.

In this study, we present a CHANS modelling framework built upon the GPU-accelerated High Performance Integrated hydrodynamic Modelling System (HiPIMS) for predicting flood hydrodynamics, fully coupled with an agent-based module within the same computational framework to represent human mobility. This enables seamless simulation of interacting flood conditions and human responses. Human mobility is represented by autonomous agents within a unified architecture that supports pedestrians, cyclists, and vehicles. Mobility agents exhibit heterogeneous behavioural attributes, including risk aversion, awareness, compliance, and patience, and interact within a shared, dynamically evolving flood environment.

The framework is demonstrated through an urban case study in Newcastle upon Tyne, with data from the Urban Observatory for model validation. Further simulations are conducted for light, medium, and heavy rainfall scenarios to analyse adaptive transport responses under different flood conditions.

By supporting large numbers of agents and real-time hazard-human interactions within a single computational environment, the proposed framework enables systematic analysis of human adaptive behaviour and system-level disruption during flood events. This work provides a new methodological basis for characterising flood risk in a coupled human and natural systems context, with clear implications for early warning, emergency response planning, and integrated flood forecasting.