Global projections of future landslide susceptibility under climate change

Abstract

Landslides pose a significant threat to both human society and environmental sustainability, yet, their spatiotemporal evolution and impacts on global scales in the context of a warming climate remain poorly understood. In this study, we projected global landslide susceptibility under four shared socioeconomic pathways (SSPs) from 2021 to 2100, utilizing multiple machine learning models based on precipitation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and static metrics. Our results indicate an overall upward trend in global landslide susceptibility under the SSPs compared to the baseline period (2001-2020), with the most significant increase of about 1% in the very far future (2081-2100) under the high emissions scenario (SSP5-8.5). Currently, approximately 13% of the world's land area is at very high risk of landslide, mainly in the Cordillera of the Americas and the Andes in South America, the Alps in Europe, the Ethiopian Highlands in Africa, the Himalayas in Asia, and the countries of East and South-East Asia. Notably, India is the country most adversely affected by climate change, particularly during 2081-2100 under SSP3-7.0, with approximately 590 million people-23 times the global average-living in areas categorized as having very high susceptibility.

Keywords

Landslide susceptibility / CMIP6 / Climate change / Spatiotemporal analysis / Ensemble modeling

CRediT authorship contribution statement

Yu Duan: Writing - original draft, Methodology, Data curation, Conceptualization. Mingtao Ding: Writing - review & editing, Supervision, Project administration, Funding acquisition, Concep-tualization. Yufeng He: Methodology, Investigation. Hao Zheng: Validation, Resources. Ricardo Delgado-Téllez: Resources, Methodology. Sergey Sokratov: Writing - review & editing. Fran-cisco Dourado: Methodology. Sven Fuchs: Writing - review & editing, Methodology.

Declaration of competing interest

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

Acknowledgments

This study is supported by the project of National Natural Science Foundation of China (Grant No. 42371203 and U21A2032), the project of Sichuan Provincial Science and Technol-ogy Department Program Funding (Grant No. 2025YFHZ0010), and the project of the Science and Technology Program of Aba City (Grant NO. R24YYJSYJ0001). The authors are grateful to these supports.

Code availability

The five machine learning models used in this study can be found in https://zenodo.org/records/14053287.

Data availability

The CMIP6 data are available for the WCRP Earth System Grid Federation (ESGF) (https://aims2.llnl.gov/search/cmip6/), and the climate station data were collected from the National Cen-ters for Environmental Information (NCEI) (https://www.ncei.noaa.gov/maps-and-geospatial-products). The DEM data and slope data were come from SRTM (http://srtm.csi.cgiar.org/srtmdata/), while the land use data and NDVI from National Aeronautics and Space Administration (NASA). The river and road database were available in OpenStreetMap, soil type data were from Food and Agriculture Organization (FAO) (http://www.fao.org/), and lithol-ogy data were from PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.788537).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102074.

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