The delusive accuracy of global irrigation water withdrawal estimates

Indicator metrics and temporal aggregations introduce ambiguities in water scarcity estimates

Water scarcity is a global challenge affecting billions of people worldwide. This study systematically assesses differences in the estimation of the global population exposed to water scarcity based on 7 water scarcity indicators and 11 Environmental Flow Requirements (EFR) evaluated at various spatial and temporal resolutions. All indicators show an increase in water scarcity since 1901. However, considering monthly average water scarcity estimates spatially aggregated at the basin scale found 35% less population exposed than estimates based on a distributed grid over the landscape. Estimates temporally disaggregated to consider water scarcity for at least one month a year found 50% (tenfold) larger population exposed compared to average monthly (annual) estimates. The study illustrates that estimates of the impacts of water scarcity are an artifact of how water scarcity is defined and calculated. This suggests caution is needed when relying on a single method and emphasizes the importance of considering the diversity of factors that can influence estimates of impact when assessing water scarcity.

Assessing nitrate groundwater hotspots in Europe reveals an inadequate designation of Nitrate Vulnerable Zones

Monitoring networks show that the European Union Nitrates Directive (ND) has had mixed success in reducing nitrate concentrations in groundwater. By combining machine learning and monitored nitrate concentrations (1992-2019), we estimate the total area of nitrate hotspots in Europe to be 401,000 km2, with 47% occurring outside of Nitrate Vulnerable Zones (NVZs). We also found contrasting increasing or decreasing trends, varying per country and time periods. We estimate that only 5% of the 122,000 km2 of hotspots in 2019 will meet nitrate quality standards by 2040 and that these may be offset by the appearance of new hotspots. Our results reveal that the effectiveness of the ND is limited by both time-lags between the implementation of good practices and pollution reduction and an inadequate designation of NVZs. Substantial improvements in the designation and regulation of NVZs are necessary, as well as in the quality of monitoring stations in terms of spatial density and information available concerning sampling depth, if the objectives of EU legislation to protect groundwater are to be achieved.

Field-scale crop water consumption estimates reveal potential water savings in California agriculture

Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture's hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California's Central Valley. We find that switching to lower water intensity crops can reduce consumption by up to 93%, but this requires adopting uncommon crop types. Northern counties have substantially lower irrigation efficiencies than southern counties, suggesting another potential source of water savings. Other practices that do not alter land cover can save up to 11% of water consumption. These results reveal diverse approaches for achieving sustainable water use, emphasizing the potential of sub-field scale crop water consumption maps to guide water management in California and beyond.

What can mathematical modelling contribute to a sociology of quantification?

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.

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Anthropogenic loading of nitrogen to river systems can pose serious health hazards and create critical environmental threats. Quantification of the magnitude and impact of freshwater nitrogen requires identifying key controls of nitrogen dynamics and analyzing both the past and present patterns of nitrogen flows. To tackle this challenge, we adopted a machine learning (ML) approach and built an ML-driven representation that captures spatiotemporal variability in nitrogen concentrations at global scale. Our model uses random forests to regress a large sample of monthly measured stream nitrogen concentrations onto a set of 17 predictors with a spatial resolution of 0.5-degree over the 1990-2013, including observations within the pixel and upstream drivers. The model was validated with data from rivers outside the training dataset and was used to predict nitrogen concentrations in 520 major river basins of the world, including many with scarce or no observations. We predicted that the regions with highest median nitrogen concentrations in their rivers (in 2013) were: United States (Mississippi), Pakistan, Bangladesh, India (Indus, Ganges), China (Yellow, Yangtze, Yongding, Huai), and most of Europe (Rhine, Danube, Vistula, Thames, Trent, Severn). Other major hotspots were the river basins of the Sebou (Morroco), Nakdong (South Korea), Kitakami (Japan), and Egypt's Nile Delta. Our analysis showed that the rate of increase in nitrogen concentration between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, Pakistan, mainland southeast Asia, and south-eastern Australia. Using a new grouped variable importance measure, we also found that temporality (month of the year and cumulative month count) is the most influential predictor, followed by factors representing hydroclimatic conditions, diffuse nutrient emissions from agriculture, and topographic features. Our model can be further applied to assess strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.

Models with higher effective dimensions tend to produce more uncertain estimates

Mathematical models are getting increasingly detailed to better predict phenomena or gain more accurate insights into the dynamics of a system of interest, even when there are no validation or training data available. Here, we show through ANOVA and statistical theory that this practice promotes fuzzier estimates because it generally increases the model's effective dimensions, i.e., the number of influential parameters and the weight of high-order interactions. By tracking the evolution of the effective dimensions and the output uncertainty at each model upgrade stage, modelers can better ponder whether the addition of detail truly matches the model's purpose and the quality of the data fed into it.

Models with higher effective dimensions tend to produce more uncertain estimates

Mathematical models are getting increasingly detailed to better predict phenomena or gain more accurate insights into the dynamics of a system of interest, even when there are no validation or training data available. Here, we show through ANOVA and statistical theory that this practice promotes fuzzier estimates because it generally increases the model's effective dimensions, i.e., the number of influential parameters and the weight of high-order interactions. By tracking the evolution of the effective dimensions and the output uncertainty at each model upgrade stage, modelers can better ponder whether the addition of detail truly matches the model's purpose and the quality of the data fed into it.