Integration of climate change impacts and agent-based model to flood risk evaluation in an urbanized area

Abstract

This study uses an agent-based model (ABM) to assess how household and government actions influence flood risk in an urban area under climate change. Seventeen regional climate models are evaluated, and projections from the best-performing model are used in hydrologic and coupled 1D/2D hydraulic models to simulate hydrographs and corresponding inundation maps for different return period events. A survey is conducted to more accurately represent households’ initial flood risk and coping perceptions. A flood event is randomly assigned to each year within the simulation period (i.e., 2025–2100) in accordance with the exceedance probabilities associated with different return period events to generate a realization. An ensemble of 100 realizations is generated to capture stochastic variability in flood occurrences over 2025–2100. Results show that using survey data leads to higher estimated economic damages than those obtained with randomly initialized risk and coping perceptions, highlighting the importance of site-specific data. Government actions are generally found to be more effective than household actions. For extreme realizations, such as the occurrence of two 500-year flood events within the simulation period, only proactive government actions can significantly reduce economic damages by up to two-thirds or more. Similarly, prevention-based household measures are more effective than response-based ones triggered by extreme floods, especially when the triggering extreme flood(s) occur later in the simulation period.