Improving the representation of the spatial distribution of population for coastal impact and vulnerability assessments

Broad-scale impact and vulnerability assessments are essential for informing decisions on longterm adaptation planning at the national, regional, or global level. These assessments rely on population data for quantifying exposure to different types of hazards. Existing population datasets covering the entire globe at resolutions of 2.5 arc-minutes to 30 arc-seconds are based on information available at administrative-unit level and implicitly assume uniform population densities within these units. This assumption can lead to errors in impact assessments and particularly in coastal areas that are densely populated. This study proposes and compares simple approaches to regionalize population within administrative units in the German Baltic Sea region using solely information on urban extent from the Global Urban Footprint (GUF). Our results show that approaches using GUF can reduce the error in predicting population totals of municipalities by factor 2 to 3. When assessing exposed population, we find that the assumption of uniform population densities leads to an overestimation of 120 % to 140 %. Using GUF to regionalise population within administrative units reduce these errors by up to 50 %. Our results suggest that the proposed simple modelling approaches can result in significantly improved distribution of population within administrative units and substantially improve the results of exposure analyses.