Exploring the effects of different population projection datasets on global compound drought and heatwave exposure estimates under shared socioeconomic pathways

Abstract

The simultaneous occurrence of both extreme droughts and heatwaves has become more frequent with global warming, resulting in increases in the frequency and potential impact of compound drought and heatwave (CDHW) globally. It is critical to evaluate the impacts of CDHW and assess global socio-economic risks to formulate appropriate risk mitigation strategies. Most studies have focused on projecting the likely variation in the multidimensional hazard of CDHW. However, the discrepancies among global population projection datasets based on shared socioeconomic pathways (SSPs) and their potential impacts on disaster risk assessments remain underexplored. In this study, multiple global high-resolution population projection datasets are used in combination with projected CDHW hazards via the multimodel ensemble from Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate how different sources of population data could affect the assessment of CDHW-exposed populations under SSPs. The results show that at the global scale, the spatial pattern and temporal evolution of the CDHW-exposed population under climate change can be depicted consistently on the basis of different population data. However, at the subcontinental scale, substantial spatial heterogeneity exists in the projected exposure. For regions such as the Mediterranean, South Asia, and western Central Asia, the projections from different datasets are consistent with low uncertainty. In contrast, for regions including the northern hemisphere above 40°N, Oceania, eastern Central Asia, East Asia, the South American monsoon region, western Africa, Central Africa, etc., the uncertainty in the estimated exposed population is higher and is expected to increase from the 2020s to the end of the 21st century. Additional locational socioeconomic data should be collected in these areas to reduce uncertainty in future socioeconomic projections. The findings highlight the critical need to consider different elements-at-risk and choose fit-for-purpose datasets, providing essential guidance for disaster risk assessments that support climate adaptation strategies and sustainable development goals.