Abstract
A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture. This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. Here we demonstrate that these strategies err on the side of excess complexity, as the values reported by FAO and outputted by Global Models are largely conditioned by irrigated areas and their uncertainty. Modelling irrigation water withdrawals as a function of irrigated areas yields almost the same results in a much parsimonious way, while permitting the exploration of all model uncertainties. Our work offers a robust and more transparent approach to estimate one of the most important indicators guiding our policies on water security worldwide.
Highlights
A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture
Irrigated areas in Global Models (GMs) are parametrised with the Global Map of Irrigated Areas (FAO-GMIA)[39], a gridded product that documents the extension of irrigation at a 5 arcmin resolution
A linear trend between the areas reported by the Food and Agriculture Organization (FAO)-GMIA and the irrigation water withdrawals simulated by GMs is apparent at the country level from 1900 up to 2005–2010, the last period for which there is systematic data available for both variables
Summary
A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. The reliance on multi-model ensembles of GMs allows for the obtainment of probabilistic estimates, yet it exacerbates the computational, opacity and uncertainty-related problems mentioned above[28] Such flaws limit the utility of global irrigation water withdrawal estimates in the policy realm, where stakeholders and non-experts alike should be able to swiftly replicate the results or, at least, understand the main assumptions upon which the analysis is based[29,30,31]
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