Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change.

At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties. Plain Language Summary: As climate change continues, extreme rainfall is expected to become more frequent and intense. This can lead to changes in landslide risks and how population is exposed to them. While previous studies have mainly focused on how the landslide likelihood may change in time, few have paid attention to how dynamic socioeconomic factors play a role in determining landslide risk. In this study, we used multiple global climate models and different population data sets to investigate how different sources of population information can affect estimates of future landslide risks in China. Overall, we found that landslide hazard is likely to increase in the future. However, when we incorporated different population data into our models, we found divergent results in terms of the number of people exposed to these phenomena. Depending on the population data used, the estimates ranged from a decrease of about 64% to an increase of 48%. These different outcomes were mainly due to variations in population data we used, although climate models also had an impact. These conflicting estimates highlight the need to consider changes in elements at risk when assessing disaster risks and developing targeted measures to adapt to and mitigate the effects of climate change. Key Points: Future landslide exposure is assessed by combining landslide model with spatially explicit population and global climate model dataPotential impact areas and frequency of landslides in China under climate change are projected to increase by 0.4%–2.7% and 4.7%–20.1%, respectivelyVarying sourced population projections may induce divergent signals (−64% to +48%) of landslide‐exposed population under climate change [ABSTRACT FROM AUTHOR]