Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways

Abstract

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km−2) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km−2) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

Downscaling population and urban land use for socio-economic scenarios in the UK

“…We analysed uncertainties associated with the interpretation of these narratives (Rounsevell et al 2021), showing how results vary when parameters settings per UK-SSP are changed. Similar to Reimann et al (2021), we find that population allocation in urban areas is more certain than in rural areas. This reflects that rural LADs are larger than urban LADs, and therefore, the number of different possible urban land use and population allocations is larger in rural areas.…”

“…The underlying basis is a Kumaraswamy density function, transposed to make beta endogenous. With this framework, we could capture UK-SSP-specific sprawl assumptions about the extent of leapfrogging, which cannot be modelled with the negative exponential function that is usually used in gravity-based downscaling (Reimann et al 2021). Our framework provides the possibility for greenspace or agriculture to develop between new urban cells, and controls the scatter of urban development (Caruso et al 2005).…”

“…To create finer resolution SSP projections, various downscaling methodologies have been developed and applied (van Vuuren et al 2010). These include simple proportional scaling techniques (Boke-Olén et al 2017;Yidan Chen et al 2020a, b;Gaffin et al 2004;Gao 2017Gao , 2020, gravity-based approaches of intermediate complexity (Grübler et al 2007;Jones & O'Neill 2016;Reimann et al 2021) and other approaches ranging from qualitative stakeholder inputbased methods (Rickebusch et al 2011) to machine learning methods with resolutions as fine as 100-m grids (Yimin Chen et al 2020a, b;Wolff et al 2020). Each downscaling methodology comes with its own advantages and disadvantages (van Vuuren et al 2010).…”

“…Parameters for these functions are calibrated from historical data, usually based on observed changes between 1990 and 2000. A limitation of using gravity models to determine population allocation is that they naturally lead to agglomeration, as attractiveness increases with density (Grübler et al 2007;Reimann et al 2021).…”

“…In order to follow the UK-SSP narratives as closely as possible, we apply a gravity-based approach with a neighbourhood function that allows for leapfrogging (Caruso et al 2005). As a result, we avoid enforced urban agglomeration, which has been identified as a limitation in earlier gravity-based downscaling studies (Reimann et al 2021). Given the unique and unobserved nature of the future UK-SSPs, we make no attempt to replicate historical patterns of change.…”