July 25, 2018
Feature

Reconstruction of Global Gridded Monthly Sectoral Water Withdrawals for 1971-2010 and Analysis of Their Spatiotemporal Patterns

A new global data set for understanding sectoral water use at regional and seasonal scales

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This map is an example from the global gridded data set that shows the spatial distribution of annual mean water withdrawal in the six sectors. This work is distributed under the Creative Commons Attribution 4.0 International license(Huang et al. 2018). 

The Science

Human water withdrawal is shown to alter the global water cycle, yet our understanding of its driving forces and patterns is limited primarily to water withdrawal estimates available at annual and country scales.

Researchers at the U.S. Department of Energy's Pacific Northwest National Laboratory reconstructed a global monthly, gridded (0.5 degree), sectoral water withdrawal data set for the period 1971-2010, that distinguishes six water use sectors: irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. The gridded data set constitutes the first reconstructed global water withdrawal data product at seasonal and regional resolution that is derived from different models and data sources.

The Impact

The reconstructed gridded water withdrawal data set is open access, and can be used to compare water withdrawal estimates from global hydrologic models and also to supplement water withdrawal estimates in Earth system models, where domestic and industrial water withdrawal representations are often lacking. The data set is also important for investigating water use-related issues and patterns at fine spatial, temporal, and sectoral scales, which is critical for developing sound water management strategies.

 

Reference: Z. Huang, M. Hejazi, X. Li, Q. Tang, C. Vernon, G. Leng, Y. Liu, P. Döll, S. Eisner, D. Gerten, N. Hanasaki, Y. Wada, "Reconstruction of Global Gridded Monthly Sectoral Water Withdrawals for 1971-2010 and Analysis of Their Spatiotemporal Patterns." Hydrology and Earth System Sciences 22, 2117-2133 (2018). [DOI: 10.5194/hess-22-2117-2018]

Key Capabilities

Advanced Computer Science, Visualization, & Data
Climate Change Science & Atmospheric Science
Earth Systems Science & Engineering
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About PNNL

Pacific Northwest National Laboratory draws on its distinguishing strengths in chemistry, Earth sciences, biology and data science to advance scientific knowledge and address challenges in sustainable energy and national security. Founded in 1965, PNNL is operated by Battelle for the Department of Energy’s Office of Science, which is the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, visit https://www.energy.gov/science/. For more information on PNNL, visit PNNL's News Center. Follow us on Twitter, Facebook, LinkedIn and Instagram.

Published: July 25, 2018

Research Team

Zhongwei Huang, Chinese Academy of Sciences/PNNL (Joint Global Change Research Institute)/University of Chinese Academy of Sciences
Mohamad Hejazi, PNNL (JGCRI)/University of Maryland, College Park
Xinya Li and Chris Vernon, PNNL
Qiuhong Tang, Chinese Academy of Sciences/University of Chinese Academy of Sciences
Guoyong Leng and Yaling Liu, PNNL (JGCRI)
Petra Döll, Goethe University Frankfurt/Senckenberg Biodiversity and Climate Research Centre (Germany)
Stephanie Eisner, University of Kassel (Germany)
Dieter Gerten, Potsdam Institute for Climate Impact Research/Humboldt-Universität zu Berlin (Germany)
Naota Hanasaki, National Institute for Environmental Studies (Japan)
Yoshihide Wada, International Institute for Applied Systems Analysis (Austria)

Research topics

Computing & Analytics
Scientific Discovery