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A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture. This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. Here we demonstrate that these strategies err on the side of excess complexity, as the values reported by FAO and outputted by Global Models are largely conditioned by irrigated areas and their uncertainty. Modelling irrigation water withdrawals as a function of irrigated areas yields almost the same results in a much parsimonious way, while permitting the exploration of all model uncertainties. Our work offers a robust and more transparent approach to estimate one of the most important indicators guiding our policies on water security worldwide. The global water demands of irrigated agriculture are estimated through country surveys or through hydrological models, but both approaches are taxing. Here, the authors show that they can simply be estimated as a function of irrigated areas.

Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements.

Sociology of quantification has spent relatively less energies investigating mathematical modelling than it has on other forms of quantification such as statistics, metrics, or algorithms based on artificial intelligence. Here we investigate whether concepts and approaches from mathematical modelling can provide sociology of quantification with nuanced tools to ensure the methodological soundness, normative adequacy and fairness of numbers. We suggest that methodological adequacy can be upheld by techniques in the field of sensitivity analysis, while normative adequacy and fairness are targeted by the different dimensions of sensitivity auditing. We also investigate in which ways modelling can inform other instances of quantification as to promote political agency.

An assessment of the human impact on the global water cycle requires estimating the volume of water withdrawn for irrigated agriculture. A key parameter in this calculation is the irrigation efficiency, which corrects for the fraction of water lost between irrigation withdrawals and the crop due to management, distribution or conveyance losses. Here we show that the irrigation efficiency used in global irrigation models is flawed for it overlooks key ambiguities in partial efficiencies, irrigation technologies, the definition of ‘large-scale’ irrigated areas or managerial factors. Once accounted for, these uncertainties can make irrigation withdrawal estimates fluctuate by more than one order of magnitude at the country level. Such variability is larger and leads to more extreme values than that caused by the uncertainties related with climate change. Our results highlight the need to embrace deep uncertainties in irrigation efficiency to prevent the design of shortsighted policies at the river basin-water-agricultural interface.

In this contribution, we present an innovative data-driven model to reconstruct a reliable temporal pattern for time-lagged statistical monetary figures. Our research cuts across several domains regarding the production of robust economic inferences and the bridging of top-down aggregated information from central databases with disaggregated information obtained from local sources or national statistical offices. Our test bed case study is the European Regional Development Fund (ERDF). The application we discuss deals with the reported time lag between the local expenditures of ERDF by beneficiaries in Italian regions and the corresponding payments reported in the European Commission database. Our model reconstructs the timing of these local expenditures by back-dating the observed European Commission reimbursements. The inferred estimates are then validated against the expenditures reported from the Italian National Managing Authorities (NMAs) in terms of cumulative monetary difference. The lower cumulative yearly distance of our modelled expenditures compared to the official European Commission payments confirms the robustness of our model. Using sensitivity analysis, we also analyse the relative importance of the modelling parameters on the cumulative distance between the modelled and reported expenditures. The parameters with the greatest influence on the uncertainty of this distance are the following: first, how the non-clearly regionalised expenditures are attributed to individual regions; and second, the number of backward years that the residuals of the yearly payments are spread onto. In general, the distance between the modelled and reported expenditures can be further reduced by fixing these parameters. However, the gain is only marginal for some regions. The present study paves the way for modelling exercises that are aimed at more reliable estimates of the expenditures on the ground by the ultimate beneficiaries of European funds. Additionally, the output databases can contribute to enhancing the reliability of econometric studies on the effectiveness of European Union (EU) funds.

Unfolding regularities between population and irrigated agriculture might increase our capacity to predict their coevolution and better ensure food security and environmental welfare. Here I use three different data sets with detailed information at the national level for ~70% of the countries of Africa, Asia, the Americas, and Europe between 1950 and 2017 to show that irrigated areas might grow disproportionally for a given increase in population, e.g., with β > 1. The results are robust across continents, time series, population cut-offs, and variations in the area accounted for irrigation by official institutions and independent scholars. This systematic pattern suggests the existence of an underlying law driving the growth rate of irrigated areas that transcends local particularities and can be well approximated by a power function of population, specially in the case of the Americas, Asia, and Europe. Nonlinearities derived from the open-ended growth rate of irrigated areas should be taken into consideration when designing irrigation policies in order to avoid unexpected environmental costs.

To gain insights on long-term social-ecological resilience, we examined adaptive responses of small-scale societies to dryland-related hazards in different regions and chronological periods, spanning from the mid-Holocene to the present. Based on evidence from Africa (Sahara and Sahel), Asia (south margin of the Thar desert), and Europe (South Spain), we discuss key traits and coping practices of small-scale societies that are potentially relevant for building resilience. The selected case studies illustrate four main coping mechanisms: mobility and migration, storage, commoning, and collective action driven by religious beliefs. Ultimately, the study of resilience in the context of drylands emphasizes the importance of adaptive traits and practices that are distinctive of small-scale societies: a strong social-ecological coupling, a solid body of traditional ecological knowledge, and a high degree of internal cohesion and self-organization. adaptation; climate change; coping mechanisms; drylands; resilience; social-ecological systems; sustainability; traditional ecological knowledge

Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame. Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame.

Many global water models contain an irrigation module that simulates irrigation water withdrawals based on several assumptions on climate conditions, crop management, soil moisture, irrigation practices or water source. However, we do not know how many of these assumptions are grounded on empirical data and how many are pragmatic; that is, based on practical considerations rather than on observational evidence. Given that pragmatic assumptions are more flexible and can be altered, replaced or removed without compromising the model's representational capacity, this knowledge gap constrains our ability to delineate the uncertainties in these models and assess how reliable their results are. Here we address this issue through the lens of sensitivity auditing and philosophy of science and analyze 50 studies across nine global irrigation models (GIM), identifying a total of 102 model assumptions. Our results suggest that 70% of these assumptions are pragmatic, 35% are shared by multiple models and that most of these in turn are pragmatic. This indicates that the uncertainty space of GIMs may be larger than currently addressed using traditional uncertainty analyses. Our findings underscore the need for systematic appraisal of model assumptions to enhance transparency and improve the robustness of GIMs for decision‐making in water resource management and policy.