News Release

Inaccurate global irrigation models can cause extensive societal harm

Peer-Reviewed Publication

The University of Bergen


image: Humans intervene heavily in the global water cycle, and the greatest impacts are related to irrigated agriculture. view more 

Credit: Colourbox

An international team of researchers from the University of Bergen and other leading institutions expose severe limitations in large-scale hydrological models that produce estimates for water withdrawals worldwide.

In a comment published in Nature Communications today, they argue that miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management and can cause social and environmental harm on a large scale.

The authors argue that the Irrigation Water Withdrawal (IWW) estimates produced by large-scale hydrological models are unreliable because they disregard uncertainties and are exclusively based on the engineering/agronomist conception of irrigation, thus overlooking the values and practices of other relevant collectives such as traditional irrigators. To illustrate the risks of these biases, Puy et al. discuss the example of a water model that left six million people without water insurance payouts in Malawi due to wrong assumptions about the crops actually grown by the farmers.

Since IWW estimates feed into World Water Development Reports, Global Environmental Outlooks and several studies commissioned by the World Bank, they affect policy on the field. Thus, unreliable estimates can potentially lead to grave policy misjudgments that have devastating consequences.

The researchers demonstrate the spurious accuracy of current IWW estimates by submitting the approach of large-scale hydrological models to a systematic uncertainty analysis. They show that, once uncertainties are thoroughly propagated in the estimation of IWW, the amount of water required by crops can vary by up to two orders of magnitude. This means that large-scale hydrological models are ignoring important ambiguities and conveying an “illusion of accuracy” in the estimation of global irrigation water needs. Authors argue that excess certainty is dangerous because it closes the space of policy options and does not account for unexpected scenarios at the water-food interface.

Corresponding author, Arnald Puy from the University of Bergen, says that to his knowledge, this thorough propagation of uncertainties in the estimation of IWW has not been done before.
“There are many reasons for this: it is computationally very expensive, and modellers are sometimes a bit reluctant to run these analyses because they may reveal uncertainties as large as to render the model useless for policy-making. This is precisely what we show can happen.”

What should be done, according to the researchers? In the comment, they suggest three corrective measures: Accepting that uncertainties may not disappear with further research, utilizing computational power to accurately quantify uncertainties, and exposing assumptions that underlie the design and use of the models.

With their research, Puy and his co-authors hope to promote reflection on the way water-related models are designed and used:
“We want to increase awareness of the fact that models are not ‘objective” constructs, but the consequence of non-unique choices made by their designers. Models that aim at guiding policies, but that have not undergone a systematic assessment of uncertainties and embedded value-laden assumptions, should be seen with suspicion.”

Link to the article in Nature Communications:

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