Increased population exposures to extreme precipitation in Central Asia under 1.5 ℃ and 2 ℃ global warming scenarios

Abstract

The increase in extreme precipitation (EP) may pose a serious threat to the health and safety of population in arid and semi-arid regions. The current research on the impact of EP on population in Central Asia (CA) is insufficient and there is an urgent need for a comprehensive assessment. Hence, we opted for precipitation and temperature data under two Shared Socioeconomic Pathways (SSP2–4.5 and SSP5–8.5) from ten Global Climate Models (GCMs), which were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). By integrating population data in 2020 and 2050 (SSP2 and SSP5), we investigated the future changes in EP and population exposure in CA under 1.5 °C and 2 °C global warming scenarios (GWSs). Our analysis indicates that EP in CA is projected to increase with global warming. Under the SSP5–8.5, the maximum daily precipitation (Rx1day) exhibits an average response rate to global warming of 3.58 %/K (1.99–4.06 %/K). With rising temperatures, an increasing number of areas and populations in CA will be impacted by EP, especially in the Fergana valley. Approximately 25% of the population (land area) in CA is exposed to Rx1day with increases of more than 8.31% (9.32%) under 1.5 °C GWS and 14.18% (13.25%) under 2 °C GWS. Controlling temperature rise can be effective in reducing population exposures to EP. For instance, limiting the temperature increase to 1.5 °C instead of 2 °C results in a 2.79% (1.75%–4.59%) reduction in population exposure to Rx1day. Finally, we found that climate change serves as the predominant factor influencing the population exposure to EP, while the role of population redistribution, although relatively minor, should not be disregarded. Particularly for prolonged drought, the role of population redistribution manifests negatively.

Keywords

Extreme precipitation / Global warming / Population exposure / Central Asia

Data availability

All data used in this study are publicly available and listed in the manuscript.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study is jointly supported by the Tienshan Talent Program in Xinjiang (Grant No. 2023TSYCLJ0050), the National Natural Science Foundation of China (Grant No. 42122004), and the West Light Foundation of the Chinese Academy of Sciences (Grant No. xbzg-zdsys-202208).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.02.005.

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