Extended peer community finds limited epistemic justification for key assumptions in global irrigation models

Estimates of irrigation water withdrawals (IWW) by global irrigation models (GIM) depend on assumptions about which features (irrigated lands, crop types, schedules and water sources) are represented and how they are formalised. While uncertainty and sensitivity analyses (UA/SA) routinely interrogate parameter uncertainty, many assumptions are qualitative or pragmatic, resist numerical characterisation and thus lie beyond conventional quantitative approaches.

We addressed this gap by subjecting 100 irrigation modelling assumptions, drawn from c. 50 papers, to an expert elicitation process involving eleven scientists and five irrigators. Experts were asked to rank the assumptions in terms of influence and then assess the first ten based on their situational limitations, plausibility, choice space, peer agreement and influence on model outputs.

Scientists identified irrigated area, irrigation efficiency and water availability, often represented by single datasets, as primary drivers of global IWW estimates. Both scientists and irrigators judged these assumptions to be highly influential with weak pedigree (quality of the knowledge base), exhibiting limited empirical support, derivation under practical constraints, multiple plausible alternatives, and low peer agreement, and thus placing them in the NUSAP "danger zone". 

By linking assumption influence with epistemic strength, this study extends conventional UA/SA, demonstrating that extended peer engagement can reveal overlooked uncertainties, enhance transparency and strengthen the robustness of global IWW assessments under deep uncertainty.