A spatially-dependent synthetic global dataset of extreme sea level events

Current coastal flood risk assessments fail to capture flood spatial dependence at large scales. In this paper, we develop the first global synthetic dataset of spatially-dependent extreme sea level events, by applying an existing conditional multivariate statistical model to 40-year global reanalysis sea levels. The resulting dataset contains 10,000 years of extreme events with realistic spatial dependence under current climate conditions. The benchmarking against reanalysis data demonstrates a high agreement, with a coefficient of determination (R2) of 0.96 for the mean event footprint sizes and a mean bias of -0.04 m for 1 in 50-year water levels. By comparing wellknown historic events, we show that our approach can produce events with similar spatial characteristics. Our dataset enables the future development of spatially-dependent flood hazard maps for deriving accurate largescale risk profiles, which can help yield new insights into the spatial patterns of coastal flooding and support coastal communities in devising effective management plans.