Illuminating Deep Uncertainties in the Estimation of Irrigation Water Withdrawals

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The volume of water globally withdrawn for irrigation agriculture is a measure of the human impact on freshwater resources. Although scientists have attempted to produce an accurate quantification of irrigation water withdrawals through increasingly detailed mathematical models, their estimates do not seem to converge. This discrepancy points towards the existence of deep uncertainties in our conceptualization of irrigation water withdrawals that evade the development of finer-grained algorithms. Without thoroughly exposing and understanding the relevance of these uncertainties, our knowledge of the influence that humans have on the hydrological cycle will remain on fragile grounds.DAWN will assemble a five-member team to unfold, examine and assimilate the deep uncertainties that condition our understanding of irrigation water withdrawals. Firstly, DAWN will unravel the underlying assumptions of all global irrigation water withdrawal models, ponder their effect on the estimations and assess the solidity of the main belief systems grounding the simulations. Secondly, DAWN will retrieve insights on irrigation withdrawals from traditional irrigators, and compare their understandings of the premises that govern irrigation water use with the scientific knowledge of irrigation embedded in global models. Thirdly, DAWN will combine the knowledge systems of scientists and traditional irrigators and develop cost-effective uncertainty/sensitivity analysis methods to explore how their ambiguities impact the modeling of global irrigation water withdrawals. By merging approaches from hydrology, statistics, philosophy and anthropology, DAWN proposes ground-breaking research to dramatically robustify our comprehension of irrigation withdrawals. This will ultimately enhance our capacity to design model-based irrigation policies that deliver under irreducible ambiguities.