These scripts are used to generate the event-based probalistic framework to capture flood spatial dependence for assessing coastal flood risk at the global scale. For details, see the manuscript Li, H., Eilander, D., Haer, T., & Ward, P. J. (2024). Improving global-scale coastal risk assessments by considering spatial dependence. In review WRR. The preprint can be accessed at: 10.22541/essoar.172641608.83190937/v1. Input dataset The improved version of the global dataset of spatially dependent extreme sea level (esl) events (Li et al., 2024), extended from Li et al. (2023). The new dataset is freely accessed at: https://doi.org/10.5281/zenodo.12722313 The GLOFRIS coastal inundation maps from Mortensen et al. (2024), which are publicly available: https://doi.org/10.5281/zenodo.10637089 Scripts Hazard inundation.py-->constructing inundation maps for synthetic events using the esl dataset and the GLOFRIS inundation maps source_station.py-->developing a specific coastal segment for each station (or assigning the inundated cells to their sourcing station) source_station_basin.py-->developing a source station map for the entire basin merge_source_station_globe.py-->merging basin source station maps into a global source station map Impact linking_damage_to_location.py-->making damage maps for each location dmg_individual_station.py-->calculating the damage cells caused by a given station (i.e. for its coastal segment) damage_percentage.py-->calculating the damage percentage for segments which are in multiple subnational basins add_damage_perc.py-->spliting the damages for segments which are in mupltiple subnational basins based on the damage percentages agg_dmg_annual_damage.py-->aggregating annual damages across different basins Plotting (for producing figures in the manuscript Li et al., 2024) risk_curve_plotting_bootstrap.py-->plotting national aggregated risk curves ead_map.py-->plotting a global map showing the expected annual damage (EAD) esimate difference dmg_rp200_map.py-->plotting a global map showing the RP200 damage esimate difference coastline_length_diff_plot.py-->plotting the risk estimate differences with different coastline lengths worst_year_plotting.py-->plotting continetal maps showing the flood damages in the year with the highest combined annual damages Scripts are also made available on Github: https://github.com/Huazhi-Li/Event-based-flood-risk References Li, H., Eilander, D., Haer, T., & Ward, P. J. (2024). Improving global-scale coastal risk assessments by considering spatial dependence. In review. Li, H., Haer, T., Couasnon, A., Enríquez, A. R., Muis, S., & Ward, P. J. (2023). A spatially-dependent synthetic global dataset of extreme sea level events. Weather and Climate Extremes, 41, 100596. https://doi.org/10.1016/j.wace.2023.100596 Mortensen, E., Tiggeloven, T., Haer, T., van Bemmel, B., Le Bars, D., Muis, S., Eilander, D., Sperna Weiland, F., Bouwman, A., Ligtvoet, W., & Ward, P. J. (2024). The potential of global coastal flood risk reduction using 690 various DRR measures. Natural Hazards and Earth System Sciences, 24(4), 1381–1400. https://doi.org/10.5194/nhess-24-1381-2024 Contact These scripts are developed at: Institute for Environmental Studies Vrije Universiteit Amsterdam, Faculty of Science De Boelelaan 1111, 1081 HV Amsterdam Please send any questions or requests to huazhi.li@vu.nl