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GLASS Model: Utilizing Population Data for Global Environmental and Security Modeling

Explore the integration of population data into the GLASS Model to assess environment-human security linkages, crisis probability, susceptible populations, and stressor impact. Learn about the importance of gridded population data, calculation of crisis probability, and more detailed vulnerable population assessments. Understand the significance of population density, urban/rural distribution, and resource access in global modeling.

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GLASS Model: Utilizing Population Data for Global Environmental and Security Modeling

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  1. The GLASS Model:The Use of Population Data in Global Modeling of Environment and Security Marcel B. Endejan Center for Environmental Systems Research University of Kassel, Germany Workshop on Gridded Population Data 2-3 May 2000

  2. Overview • Introduction • The GLASS Model • Crisis Probability • Susceptible / Potentially Affected Population • Need for gridded population data • Specification for population data • Conclusion

  3. Introduction • Many aspects of • environment • human security • Extreme climate events • Long term changes • Response of society • Quantification • Integrated Model environment social natural human being political economical

  4. GLASS Model

  5. Security Diagram • Three Concepts • Degree of a certain stressor • Susceptibility to this kind of stressor • Crisis Crisis Occurrence e.g. Ethiopia 1973 No Crisis Occured Environmental e.g. Sudan 1953 Stressor Boundary: High Probability of Crisis Boundary: Low Probability of Crisis State Susceptibility

  6. Security Diagram - Indicators • StressorFraction of country area with water availability substantially below normal • SusceptibilityNormalized GDP per capGDP’ = 1 - (GDP/100,000 US$) • Crisis‘Droughts’ from EM-DAT (~600)

  7. Security Diagram - Example 1 0.8 Stressor 0.2 0 8000 US$/cap 0 US$/cap 0.936 0.952 0.968 0.984 1 0.92 Susceptibility (GDP’) country year reported drought

  8. Security Diagram - Crisis Probability Boundary: high & low crisis probability [0.800;1.000[ [0.600;0.800[ Water Stressor [0.400;0.600[ Crisis Probability [%] [0.200;0.400[ 10-15 5-10 0-5 [0.000;0.200[ [0.936;0.952[ [0.952;0.968[ [0.968;0.984[ [0.920;0.936[ [0.984;1.000[ Susceptibility [1-GDPnorm]

  9. Susceptible Population • Young (<14) and old (> 60) people with GDP/cap below poverty line • Poverty line according to World Bank • Developing countries: 1 US$/day (365$/a) • Latin America: 2 US$/day (730) • Eastern Europe+CIS: 4 US$/day (1460) • Industrialized Countries: 14.4 US$/day (5256) • Income distribution estimated using GINI index

  10. Susceptible Populationselected countries

  11. Need for Gridded Population Data To improve • Concept of potentially affected population • more detailed calculation of susceptible population • Affected population = susceptible population in affected areas • stressor concept • more detailed calculation of affected areas • take water use into consideration

  12. Specification for Population Data • Gridded population data for different time intervals needed about • population density per grid cell • urban/rural fraction per grid cell • access to water, food, and other resources • Currently available • population density per grid cell

  13. Population Data - Used • Calculation of domestic water withdrawals

  14. Population Data - Used

  15. Conclusion • GLASS: Model to quantify the linkage between environment and human security • Population data needed to calculate • water use, food demand (stressor/crisis probability) • susceptible / potentially affected population • Gridded population data (time intervals) • population density • urban/rural fraction • access to water, food, and other resources

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