Enhancing Exposure Indicators in Coastal Disaster Risk Assessment under Climate Change Scenarios

The Republic of Korea has developed a national Coastal Disaster Risk Assessment (CDRA) framework led by the Ministry of Oceans and Fisheries and the Korea Hydrographic and Oceanographic Agency. This framework was developed in accordance with the IPCC Sixth Assessment Report (AR6) and has initially focused on present-climate coastal risk assessment based on observational data. More recently, continuous efforts have been made to evaluate future coastal disaster risks by incorporating climate change scenario–based hazard forcing.

However, to date, climate change scenarios have been applied primarily to hazard components—such as Precipitation, Wind Speed, Wave Height, Storm Surge, and Sea Level Rise—while exposure and vulnerability components have been assessed using static, present-day datasets. This structural limitation restricts the ability of current assessments to adequately reflect long-term changes in population distribution and socio-spatial structures driven by climate change. Given that CDRA results are increasingly used to inform mid- to long-term coastal management plans and climate change adaptation policies, it is essential to account for these long-term demographic and spatial dynamics.

This study aims to advance the CDRA framework by proposing a methodology that integrates climate change scenarios into the population indicator within the exposure component. To this end, we combine global 1 km–resolution population projections based on the Shared Socioeconomic Pathways (SSPs) for the period 2020–2100 with administrative-level population projections provided by Statistics Korea at the city, county, and district scale. This approach enables the construction of a future population exposure assessment framework that maintains consistency with official national statistics while incorporating high-resolution spatial information.

Specifically, the proposed method preserves the relative spatial distribution patterns and temporal dynamics inherent in the SSP-based gridded population datasets, while using observed coastal population distributions for the year 2025 and official administrative population projections as anchoring references. Adjustment factors are derived at the city, county, and district level and subsequently redistributed to the 1 km grid, resulting in a hybrid calibration approach. Through this process, future population exposure indicators are produced that simultaneously reflect the reliability of administrative statistics and the spatial variability associated with climate change scenarios.

By linking long-term changes in terrestrial population distributions to coastal spaces, the methodology proposed in this study provides a foundation for consistently integrating climate change scenarios not only into hazard components but also into exposure and vulnerability elements of coastal disaster risk assessment. This approach is expected to enhance the reliability and applicability of scenario-based comparisons of future disaster risks, thereby supporting mid- to long-term coastal management planning and climate change adaptation policy development.