Tropical cyclone exposure - 2022 - National

This dataset includes an estimation of the annual physical exposition to tropical cyclones. It is based on two sources: 1) Estimation of global tropical cyclone wind speed probabilities using the STORM dataset. Nadia Bloemendaal, Hans de Moel, Sanne Muis, Ivan D. Haigh & Jeroen C. J. H. Aerts Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments. 2) Population density map (HRSL-GSHL) 2022. The layer integrates data from the High Resolution Settlement Layer (HRSL) - META (originally Facebook), the Global Human Settlement Layer (GHSL) - JRC, and the national population count for 2018 reported on the World Population Prospects 2019. Pixel counts are recalculated for 2022 based on the country population data reported for 2022 by the World Population Prospects 2019.