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GEOPHYSICAL RESEARCH LETTERS,
VOL. 35,
L24408,
5 PP., 2008
doi:10.1029/2008GL035296
Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, USA
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, USA
Department of Environmental, Earth and Ocean Sciences, University of Massachusetts-Boston, Boston, Massachusetts, USA
Department of Civil Engineering and NOAA-CREST, City College of New York, City University of New York, New York, New York, USA
Department of Civil Engineering and NOAA-CREST, City College of New York, City University of New York, New York, New York, USA
Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr-Universität Bochum, Bochum, Germany
Agricultural water use accounts for around 70% of the total water that is withdrawn from surface water and groundwater. We use a new, gridded, global-scale water balance model to estimate interannual variability in global irrigation water demand arising from climate data sets and uncertainties arising from agricultural and climate data sets. We used contemporary maps of irrigation and crop distribution, and so do not account for variability or trends in irrigation area or cropping. We used two different global maps of irrigation and two different reconstructions of daily weather 1963–2002. Simulated global irrigation water demand varied by ∼30%, depending on irrigation map or weather data. The combined effect of irrigation map and weather data generated a global irrigation water use range of 2200 to 3800 km3 a−1. Weather driven variability in global irrigation was generally less than ±300 km3 a−1, globally (<∼10%), but could be as large as ±70% at the national scale.
Received 9 July 2008; accepted 5 November 2008; published 31 December 2008.
Citation: (2008), Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets, Geophys. Res. Lett., 35, L24408, doi:10.1029/2008GL035296.
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