Constructing Global-Scale Spatial Population Scenarios. Bryan Jones, CGD-IAM, NCAR Boulder, CO August 19, 2013

Transcription

1 Constructing Global-Scale Spatial Population Scenarios Bryan Jones, CGD-IAM, NCAR Boulder, CO August 19, 2013

2 NCAR Community Demographic Model (CDM) Urbanization Projection (National) Household Projection ( 31 regions, pop in hh by age, size, and rural/urban) Emission Mitigation Analysis Population Projection (31 regions, age, sex, rural/urban) Spatial Downscaling (grid cell, rural/urban) Impacts, Adaptation, Vulnerability Analysis

3 Fast Track NCAR Global Projections ** The spatial population model require historical data ** GPW/GRUMP-based Calibrate to observed change ( ) Base-year distribution = GRUMP 2000 Apply existing methodology on a country-by-country basis Targeted for completion by end of year Refined Currently compiling improved historical data Consider methodological refinements Test cases: Brazil, India, China (2013) Global projections in 2014 National population and urbanization projections from shared socioeconomic pathways (SSPs)

4 Fast-Track Projections: Process 1) Construct GRUMP-based distributions of urban and rural population for 1990 and ) Fit the model to observed changed ( ) to generate urban and rural parameter estimates. 3) Construct a geospatial mask to limit allocation of population to areas not suitable for development. 4) Produce 100-year scenarios for each of the SSPs.

5 Constructing 7.5 gridded distributions from GRUMP Population count, 30 ( 1km) grid cells. Overlay 7.5 (1/4 ) grid cells, allocate population from points to cells. Raster-to-point (population from cell assigned to point). Overlay urban extents; population at points within urban boundaries classified as urban.

6 Historical Data

7 Historical Data: Fitting the Model v i Adjustment Factor = a l i Spatial Mask i m j= 1 Population Parameter P α j Population e Distance Parameter βd ij Distance Parameter Estimates Rural Urban Alpha Beta Estimated 2000 distribution (from 1990) with an average error of 11%.

8 Scenarios Urban Rural Total Population Population (Millions) SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP5 SSP1 SSP2 SSP3 SSP4 SSP Scenario 2100 Population Urban Urban Rural Total Share Base-Year 18,945,013 41,971,428 60,916, SSP1 37,778,827 4,961,754 42,740, SSP2 40,063,239 14,599,260 54,662, SSP3 37,998,043 39,293,449 77,291, SSP4 34,909,643 4,584,924 39,494, SSP5 40,077,144 5,264,121 45,341,

9 Results

10 SSP Change Urban 18,945,013 37,778, % Rural 41,971,428 4,961, % Total 60,916,441 42,740, %

11 SSP Change Urban 18,945,013 40,063, % Rural 41,971,428 14,599, % Total 60,916,441 54,662, %

12 SSP Change Urban 18,945,013 37,998, % Rural 41,971,428 39,293, % Total 60,916,441 77,291, %

13 Conclusions Improvement over proportional scaling and trend extrapolations Historically grounded Historical data test encouraging (albeit over a short time period) Static urban extents in the historic data could be problematic

14 Extra Slides

15 SSP Change Urban 18,945,013 34,909, % Rural 41,971,428 4,584, % Total 60,916,441 39,494, %

16 SSP Change Urban 18,945,013 40,077, % Rural 41,971,428 5,264, % Total 60,916,441 45,341, %

17 The Modified Potential-Allocation Downscaling Model Calculate Urban Potential Allocate Projected Urban Population Change Base-Year Distribution New Population Distribution Calculate Rural Potential Allocate Projected Rural Population Change Final Population Distribution t(100) Urban Parameters Rural Parameters Spatial Mask Adjustment Factor National/Regional Population Projection Historical Data Elevation Slope Surface Water Protected Status

18 The Modified Potential Model Traditional potential model: v i = m j= 1 P D j 2 ij Modified potential model: Adjustment Factor m v = a l P α e ij Allocation occurs i as proportional i i to the inverse j of potential during projected j= 1 Population Parameter Urban and rural populations Spatial Mask coexist within grid Population cells Distance Parameter βd Potential is calculated for each cell over a window of 100km. periods of population loss Distance

19 Potential-Based Spatial Downscaling Model v i = m j= 1 P D Projected US Population: IIASA A2 Scenario j 2 ij Assumptions: Spatial choice informed by accessibility Population agglomeration is a proxy for the socio-economic characteristics of attractive places New Population Potential Distribution Surface Replicates two commonly observed patterns of spatial development : Urban sprawl Development of urban corridors

20 Gravity-Based Models Gravity I = ij P i D P 2 ij j Flows 2 directions Spatial interaction Migration and transportation Potential v i = n j= 1 P D j 2 ij Influence 1 direction Spatial allocation Accessibility or attractiveness