The global volume of water withdrawn for irrigation agriculture serves as a key indicator of human impact on freshwater resources. Despite significant efforts to quantify irrigation water withdrawals (IWW) using intricate mathematical models, achieving convergence in estimates remains a challenge (Fig. 1). This discrepancy underscores the existence of profound uncertainties surrounding our understanding of IWW, resisting finer algorithmic solutions. Without coming to terms with these uncertainties, models risk misleading science with unwarranted precision and fostering narrow perspectives in model-driven irrigation policies.

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DAWN proposes disruptive research to unfold and embrace the deep uncertainties behind our understanding of IWW:

1. DAWN delves into the philosophical underpinnings of global IWW models, evaluating the impact of assumptions, path dependencias and lock-in effects, and gauging the robustness of foundational simulation paradigms.

2. DAWN draws insights from traditional irrigators’ perspectives, contrasting them with the scientific premises governing global IWW models.

3. DAWN merges knowledge from scientists and traditional irrigators and develops cost-effective methods for uncertainty and sensitivity analysis, shedding light on how the activation of contrasting epistemological paradigms affects our estimation of IWW.

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By combining philosophy, statistics, and anthropology, DAWN enhances the exploration of global IWW, fortifying our understanding and model design in the face of irreducible, deep uncertainty.

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More information about the DAWN project can be found here.

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