Climatic Change

, Volume 136, Issue 2, pp 217–231 | Cite as

Balancing global water availability and use at basin scale in an integrated assessment model

  • Son H. KimEmail author
  • Mohamad Hejazi
  • Lu Liu
  • Katherine Calvin
  • Leon Clarke
  • Jae Edmonds
  • Page Kyle
  • Pralit Patel
  • Marshall Wise
  • Evan Davies


Water is essential for the world’s food supply, for energy production, including bioenergy and hydroelectric power, and for power system cooling. Water is already scarce in many regions of the world and could present a critical constraint as society attempts simultaneously to mitigate climate forcing and adapt to climate change, and to provide for a larger and more prosperous human population. Numerous studies have pointed to growing pressures on the world’s scarce fresh water resources from population and economic growth, and climate change. This study goes further. We use the Global Change Assessment Model to analyze interactions between population, economic growth, energy, land, and water resources simultaneously in a dynamically evolving system where competing claims on water resources from all claimants—energy, land, and economy—are reconciled with water resource availability—from renewable water, non-renewable groundwater and desalinated water sources —across 14 geopolitical regions, 151 agriculture-ecological zones, and 235 major river basins. We find that previous estimates of global water withdrawal projections are overestimated. Model simulations show that it is more economical in some basins to alter agricultural and energy activities rather than utilize non-renewable groundwater or desalinated water. This study highlights the importance of accounting for water as a binding factor in agriculture, energy and land use decisions in integrated assessment models and implications for global responses to water scarcity, particularly in the trade of agricultural commodities and land-use decisions.


Water Demand Water Scarcity Baseline Scenario Water Price Renewable Water 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the Office of Science of the U.S. Department of Energy through the Integrated Assessment Research Program. PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830.

Supplementary material

10584_2016_1604_MOESM1_ESM.docx (1.7 mb)
ESM 1 (DOCX 1754 kb)


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Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2016

Authors and Affiliations

  • Son H. Kim
    • 1
    Email author
  • Mohamad Hejazi
    • 1
  • Lu Liu
    • 1
  • Katherine Calvin
    • 1
  • Leon Clarke
    • 1
  • Jae Edmonds
    • 1
  • Page Kyle
    • 1
  • Pralit Patel
    • 1
  • Marshall Wise
    • 1
  • Evan Davies
    • 2
  1. 1.Joint Global Research Institute, Pacific Northwest National Laboratory5825 University Research CourtCollege ParkUSA
  2. 2.University of AlbertaEdmontonCanada

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