Agricultural land expansion is a solution to address global food security challenges in the context of climate change. However, the sustainability of expansion in arid countries is difficult because of scarce surface water resources, groundwater salinity, and the health of salt-affected soil. Developing expansion and sustainability plans for agriculture requires systems thinking, considering the complex feedback interactions between saline groundwater, salt-affected soil, plant growth, freshwater mixing with saline groundwater, irrigation systems, and the application of soil amendments to alleviate the salinity impacts. This study presents an extensive literature review on the effects of salinity on soil and plant health, the constraints and opportunities for sustainable agriculture in Egypt, and a systems thinking approach to the feedback interactions between saline water, salt-affected soil, and the application of soil amendments to achieve required crop yields. Insights and strategies are discussed, including a system-dynamics-based decision model, irrigation systems with diversified and decentralized water sources, urban water demand management, energy availability, smart irrigation systems, and active participation of stakeholders to achieve sustainable agriculture under climate and socioeconomic changes. The insights are expected to encourage stakeholders and academic communities in the water, agriculture, and related food security sectors to develop a quantitative and systematic decision-making framework for sustainable agriculture systems in arid regions.

Transference of the embedded water, so-called virtual water, in the trade of crops among regions within a country is often neglected, leading to no information about the impacts on the water resources of exporting regions, especially if those regions are water-stressed or, worse, water-scarce. Virtual water trade, if not considered through the lens of sustainability, could lead to adverse effects on the water resources of an exporting region. Previous related studies have quantified virtual water trade among the states in the United States providing valuable insights; however, information for specific crop trade among counties, its water footprint (WF) at the county scale, the resultant virtual water flow among counties, and the sustainability assessment of those virtual water flows are lacking. In this study, we calculate the green and blue WF of cereal and milled grain products at the county level and then, using trade data, calculate the virtual water flows among the counties. Then, we assess the sustainability of the import by introducing unsustainable import fraction (UIF), which is the ratio of virtual water imported from water-scarce counties to that of total virtual water imported in the form of cereal and milled grains. Finally, we quantify the change in UIF from the 2007–2017 period. A few of the significant insights discovered through this analysis include: (i) most of the cereal and milled grains trade is occurring among neighboring counties; ii) one-third of US counties import 75% or more virtual water from water scarce regions; (iii) in 2017, Texas and Missouri were the largest importer and exporter, respectively; and (iv) the number of counties importing cereals and milled grains from water-scarce counties increased from 2007 to 2017. Recommendations on alleviating the negative effects of the unsustainable import of cereal and milled grain are provided toward the end of the discussion.

Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.

High seasonality and interannual climate patterns drive the western U.S.’s water supply and demand variability. While the mean and variance of supply and demand drivers are changing with climate and urbanization, the metrics of reliability, resilience, and vulnerability (RRV) that guide urban water systems (UWS) seasonal management and operations tend to be built on assumptions of stationarity. In this research, we use documented performance of a real-world UWS as a testbed to investigate how RRV metrics – and therefore UWS planning and operations guidance – change in response to demands modeled with and without assumptions of stationarity during dry, average, and wet hydroclimate conditions. The results indicate an assumption of stationary demands leads to large differences between simulated and observed RRV metrics for all supply scenarios, especially in supply-limiting conditions when the peak severity is 129% from the observed. The management implications of relying on stationary demands are severe: if seasonal operational decisions were made on these model results, managers might over-estimate seasonal out-of-district water requests by 50%. In contrast, when using non-stationary demands, one can expect system performance error reduction between 30% to 60% for average and dry climate conditions, respectively, and accurate RRV metrics. Our results further indicate that this UWS is more sensitive to percent changes in per-capita demand relative to percent changes in supply, but because the supply variability is so much greater (158% vs. demand range of 28%), we suggest further work to examine the combined (and coupled) influence of both factors in overall system performance.