All models rely on assumptions; that is, on decisions about what to represent and how. A study led by Seth N. Linga shows that 70% of the assumptions ingrained in global irrigation models are pragmatic (choices made for convenience rather than based on empirical data). Since these assumptions can be modified without altering the models' representational capacity, these results suggests that the uncertainty space of global irrigation models is much larger than previously thought. Paper out today in Water Resources Research.

For more than 50 years, the view of irrigated agriculture as a cornerstone of global food and water security has relied on the assumption that it produces 40% of the world’s crop output while accounting for 70% of all freshwater withdrawals. Yet these figures rest on weak empirical evidence and have been perpetuated largely through poor citation practices. The actual contribution of irrigation to food and water security is far more uncertain. Our paper on this issue is out today in PNAS Nexus

A paper led by the DAWN team shows that water modelling exhibits a degree of linguistic assertiveness akin to physics and even surpasses thermodynamics in substantiating claims with numbers. These results raise questions about whether this level of assertiveness and quantification is epistemically justified or a sign of overconfidence and "mathiness". Paper out today in iScience.

A group of eleven experts from various fields (including Dr Arnald Puy from the University of Birmingham) join forces to criticize the creation of a digital twin (DT) of the Earth, including its oceans, atmosphere, biosphere, and human inhabitants. They argue that while DTs aim to integrate data from various sources to aid decision-making, the project has significant flaws and is a poor use of resources. Check the link below for more information.

Arnald Puy and Bruce Lankford critically review the Global Commission on the Economics of Water's claim that society has crossed the upper planetary boundary for water, leading to a global water crisis. Through sensitivity auditing and uncertainty and sensitivity analysis, they suggest that this claim stems from a sweeping narrative marked by disregard for uncertainty and the production of fragile numbers.

Arnald Puy, Pamphile Roy and Andrea Saltelli propose a simple, easy to understand and computationally efficient approach for global sensitivity analysis based on the concept of discrepancy (the deviation of the distribution of points in a multi-dimensional space from the uniform distribution). The contribution provides modellers with a straightforward tool to conduct global sensitivity analysis.

Arnald Puy and Andrea Saltelli reflect on the philosophical foundations that ground the quest towards ever more detailed models, and identify four practical dangers derived from this pursuit: an explosion of the model’s uncertainty space, model black-boxing, computational exhaustion, and model attachment. The book is edited by Andrea Saltelli and Monica Di Fiore and is published by Oxford University Press.

In this Comment, Arnald Puy, Michela Massimi, Bruce Lankford and Andrea Saltelli discuss three epistemological obstacles that prevent irrigation models from providing accurate and "objective" estimates: the models’ elusive tie to reality, model plurality and indeterminacy of the target system.

Several modellers, irrigation scientists and experts in uncertainty show in Nature Communications that previous studies on global irrigation water withdrawals might have missed uncertainties spanning two orders of magnitude already at the grid cell level. Current estimates thus present us with a mirage of accuracy.