Global gridded monthly sectoral water use dataset for 1971-2010: v2
Creators
- 1. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- 2. Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
- 3. Pacific Northwest National Laboratory, Richland, WA, USA
- 4. Institute of Physical Geography, Goethe University Frankfurt, Frankfurt am Main, Germany
- 5. Center for Environmental Systems Research, University of Kassel, Kassel, Germany
- 6. Research Domain of Earth System Analysis, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
- 7. Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
- 8. International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Description
About:
The dataset is the new version of water use data in https://doi.org/10.5281/zenodo.897933 with some revision in the coordinate in the dataset. The dataset constitutes the first reconstructed global water use data product at sub-annual and sub-national/gridded resolution that is derived from different models and data sources; it was generated by spatially and temporally downscaling country-scale estimates of sectoral water withdrawals from FAO AQUASTAT (and state-scale estimates of USGS for the US). In addition, the industrial sector was disaggregated into manufacturing, mining and cooling of thermal power plants by using historical estimates from GCAM. Downscaling was performed using the output of various models and new modeling approaches, which includes the spatial and temporal downscaling methodologies for water withdrawal in previous studies (Wada et al., 2011; Voisin et al., 2013; Hejazi et al., 2014). For the consumptive water use, irrigation water consumption is reconstructed based on estimates by 4 GHMs and consumptive water use efficiency (the proportion of water consumption to water withdrawal), which is calculated based on simulation of Flörke et al (2013) and USGS estimates, is used to generated global consumptive water use for the remaining sector. Therefore, a global monthly gridded (0.5 degree) sectoral water use dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing, was reconstructed. The detailed descriptions for this dataset are presented in Huang et al (2018).
Contact: For further questions please contact Zhongwei Huang ( huangzw@nuist.edu.cn).
Files
Files
(823.1 MB)
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Additional details
Related works
- Is new version of
- 10.5281/zenodo.897933 (DOI)
- Is supplement to
- 10.5194/hess-2017-551 (DOI)
References
- Huang, Z., Hejazi, M., Li, X., Tang, Q., Leng, G., Liu, Y., Döll, P., Eisner, S., Gerten, D., Hanasaki, N., and Wada, Y.: Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-551, in review, 2017.
- Wada, Y., Van Beek, L., Viviroli, D., Dürr, H. H., Weingartner, R., and Bierkens, M. F.: Global monthly water stress: 2. Water demand and severity of water stress, Water Resources Research, 47, 2011.
- Voisin, N., Liu, L., Hejazi, M., Tesfa, T., Li, H., Huang, M., Liu, Y., and Leung, L.: One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest, Hydrology and Earth System Sciences, 17, 4555-4575, 2013.
- Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., and Alcamo, J.: Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study, Global Environmental Change, 23, 144-156, 2013.
- Hejazi, M., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J., and Calvin, K.: Longterm global water projections using six socioeconomic scenarios in an integrated assessment modeling framework, Technological Forecasting and Social Change, 81, 205-226, 2014.