Modeling the Dynamics of Social Preparedness with Regional Interactions to Explore Efficient Flood Early Warning Systems

Abstract Flood early warning systems (FEWS) are essential in mitigating flood damage. To optimize their effectiveness, it is important to understand how people respond to warnings and prepare for flooding events. The key factors influencing social preparedness include 1) direct and 2) indirect experiences of floods and 3) trust in warnings. However, existing sociohydrological models do not incorporate all these elements. To include these elements for social preparedness, we propose an idealized model that allows multiple regions to influence one another (i.e., regional interactions). We investigate the dynamics of social preparedness in a society composed of regions with varying infrastructure levels (e.g., levee heights) and explore strategies for developing a socially efficient FEWS. Numerical analyses reveal that in a society that has a region characterized by a low infrastructure level (i.e., a region with frequent floods), regional interactions lead to a pronounced cry wolf effect due to false alarms from other regions, diminishing social preparedness in the low-infrastructure region. These interactions also prevent a warning strategy that optimizes the natural-science-based index (i.e., threat score) from maximizing social efficiency. Conversely, in a society that has a region characterized by a high infrastructure level (i.e., a region with infrequent floods), regional interactions enhance the efficiency of FEWS by improving social preparedness through indirect experiences with floods. These findings suggest that as regional heterogeneity increases, it becomes increasingly vital for forecasters to consider social aspects (e.g., people’s experiences, trust, and interactions) when establishing a socially efficient FEWS. The refined model will be valuable to forecasters in designing effective FEWS in real-world situations.