The role of spatial dependence in global-scale coastal flood risk assessment

Abstract

Coastal flooding is among the world’s deadliest and costliest natural hazards. Its impacts can be particularly high when an event affects a large spatial area. Current large-scale flood risk studies assume that the probabilities of water levels during such events do not vary in space. This failure to capture flood spatial dependence can lead to large misestimates of the hazard and associated risk, and therefore potentially misinform the risk management community. In this contribution, we assess the effects of spatial dependence on coastal flood risk estimation at the global scale. To this end, we compare the assessments using two spatial dependence scenarios: 1) complete dependence and 2) modelled dependence of water level return periods. For the first scenario, we use the existing risk information calculated by the GLOFRIS global risk modelling framework. To estimate the spatially-dependent risks, we use an event-based approach and consider 10,000-year extreme coastal flood events from the global synthetic dataset of spatially-dependent extreme sea levels. These spatially-coherent return periods are then combined with the GLOFRIS spatially-constant inundation layers to create the spatially-dependent inundation map. These maps are further overlaid with exposure layers and vulnerability information to assess the coastal flood impacts. The flood risk is estimated empirically and presented in terms of expected annual population and expected annual damage. This study will provide improved risk estimation at the global scale, which could be used to enhance flood risk management through better wide-area planning decisions, more accurate insurance coverage, and better emergency response.