The Role of Gridded Population Data in Shaping Future Exposure Estimates

The impact of climate change on human populations is already being felt around the world. Both its effects, and the human populations affected, are unevenly distributed, driving differential exposure and vulnerabilities. To better understand, plan for, and respond to climate change impacts, mapped estimates of population projected under future SSP (Shared Socioeconomic Pathway) scenarios have been developed.

With a growing number of SSP-consistent gridded population datasets being developed - over thirty to date - the comparability of these datasets needs to be understood. If these datasets are used in hazard exposure analyses or vulnerability assessments, the choice of gridded population dataset potentially has a considerable impact on the population estimated to be at risk. Research on the impact of dataset choice in such analyses has been very limited. In this work, we start to address these knowledge gaps. Firstly, we introduce results of a comparative review of existing gridded future population estimates. We explore how differences in: (i) SSP database versions, (ii) downscaling methods, and (iii) classification of built settlement and urban areas, translate into variability at the grid cell level. The results of our comparative analysis show that fundamental differences exist between the various SSP-consistent future gridded population datasets.

Secondly, we focus on the challenges that differences in gridded population dataset bring for downstream data users, with an example of assessing future population exposure to flood hazards in parts of China and Italy. Using flood extents, derived from a high-resolution hydrodynamic flood model, for four time points (2020, 2050, 2070 and 2100), we calculate an estimate of exposed population based on each gridded population dataset. Preliminary results show that flooding exposure estimates vary considerably depending on which gridded population dataset is used. Our results underscore the critical role that accurate future small area population estimates have in robust exposure and vulnerability analyses.