Most of the human appropriation of freshwater resources is for agriculture. Water availability is a major constraint to mankind's ability to produce food. The notion of virtual water content (VWC), also known as crop water footprint, provides an effective tool to investigate the linkage between food and water resources as a function of climate, soil, and agricultural practices. The spatial variability in the virtual water content of crops is here explored, disentangling its dependency on climate and crop yields and assessing the sensitivity of VWC estimates to parameter variability and uncertainty. Here we calculate the virtual water content of four staple crops (i.e., wheat, rice, maize, and soybean) for the entire world developing a high‐resolution (5 × 5 arc min) model, and we evaluate the VWC sensitivity to input parameters. We find that food production almost entirely depends on green water (>90%), but, when applied, irrigation makes crop production more water efficient, thus requiring less water. The spatial variability of the VWC is mostly controlled by the spatial patterns of crop yields with an average correlation coefficient of 0.83. The results of the sensitivity analysis show that wheat is most sensitive to the length of the growing period, rice to reference evapotranspiration, maize and soybean to the crop planting date. The VWC sensitivity varies not only among crops, but also across the harvested areas of the world, even at the subnational scale.
The increasing global demand for farmland products is placing unprecedented pressure on the global agricultural system and its water resources. Many regions of the world, that are affected by a chronic water scarcity relative to their population, strongly depend on the import of agricultural commodities and associated embodied (or virtual) water. The globalization of water through virtual water trade (VWT) is leading to a displacement of water use and a disconnection between human populations and the water resources they rely on. Despite the recognized importance of these phenomena in reshaping the patterns of water dependence through teleconnections between consumers and producers, their effect on global and regional water resources has just started to be quantified. This review investigates the global spatiotemporal dynamics, drivers, and impacts of VWT through an integrated analysis of surface water, groundwater, and root-zone soil moisture consumption for agricultural production; it evaluates how virtual water flows compare to the major 'physical water fluxes' in the Earth System; and provides a new reconceptualization of the hydrologic cycle to account also for the role of water redistribution by the hidden 'virtual water cycle'.
This paper introduces a modeling framework for the analysis of real and virtual water flows at national scale. The framework has two components: (1) a national water model that simulates agricultural, industrial and municipal water uses, and available water and land resources; and (2) an international virtual water trade model that captures national virtual water exports and imports related to trade in crops and animal products. This National Water, Food & Trade (NWFT) modeling framework is applied to Egypt, a water-poor country and the world's largest importer of wheat. Egypt's food and water gaps and the country's food (virtual water) imports are estimated over a baseline period (1986–2013) and projected up to 2050 based on four scenarios. Egypt's food and water gaps are growing rapidly as a result of steep population growth and limited water resources. The NWFT modeling framework shows the nexus of the population dynamics, water uses for different sectors, and their compounding effects on Egypt's food gap and water self-sufficiency. The sensitivity analysis reveals that for solving Egypt's water and food problem non-water-based solutions like educational, health, and awareness programs aimed at lowering population growth will be an essential addition to the traditional water resources development solution. Both the national and the global models project similar trends of Egypt's food gap. The NWFT modeling framework can be easily adapted to other nations and regions.
Agriculture overexploits water resources in many regions, as water stress metrics highlight. Tracing back the causes of water overuses and separately accounting for soil water, surface water and groundwater resources is an open challenge to monitor the sustainability of agricultural water use. We introduce the “water debt repayment time” indicator, measuring the time required to replenish water resources used for annual crop production. This indicator disentangles source‐ and crop‐specific water overuses at a high spatial resolution. Globally, we find that wheat and rice production critically overuses groundwater resources and cotton production overuses both surface water and groundwater. Locally, unsustainable production is found over the Sabarmati Basin and in the Chao Phraya Basin, where the repayment time exceeds 5 years in many cultivated areas. Critical overuses are also found over the High Plain and Indo‐Gangetic Plains, where the repayment times reach 50 years. Unsustainable irrigation is often a consequence of growing crops during local dry seasons.
Abstract. To support national and global assessments of water use in agriculture, we build a comprehensive database of country-specific water footprint and virtual water trade (VWT) data for 370 agricultural goods. The water footprint, indicating the water needed for the production of a good including rainwater and water from surface water and groundwater bodies, is expressed as a volume per unit weight of the good (or unit water footprint, uWF) and is here estimated at the country scale for every year in the period 1961–2016. The uWF is also differentiated, where possible, between production and supply, referring to local production and to a weighted mean of local production and import, respectively. The VWT data, representing the amount of water needed for the production of a good and virtually exchanged with the international trade, are provided for each commodity as bilateral trade matrices, between origin and destination countries, for every year in the period 1986–2016. The database, developed within the CWASI project, improves upon earlier datasets because it takes into account the annual variability of the uWF of crops, it accounts for both produced and imported goods in the definition of the supply-side uWF, and it traces goods across the international trade up to the origin of goods' production. The CWASI database is available on the Zenodo repository at https://doi.org/10.5281/zenodo.4606794 (Tamea et al., 2020), and it welcomes contributions and improvements from the research community to enable analyses specifically accounting for the temporal evolution of the uWF.
Link prediction algorithms can help to understand the structure and dynamics of complex systems, to reconstruct networks from incomplete data sets and to forecast future interactions in evolving networks. Available algorithms based on similarity between nodes are bounded by the limited amount of links present in these networks. In this work, we reduce this latter intrinsic limitation and show that different kind of relational data can be exploited to improve the prediction of new links. To this aim, we propose a novel link prediction algorithm by generalizing the Adamic-Adar method to multiplex networks composed by an arbitrary number of layers, that encode diverse forms of interactions. We show that the new metric outperforms the classical single-layered Adamic-Adar score and other state-of-the-art methods, across several social, biological and technological systems. As a byproduct, the coefficients that maximize the Multiplex Adamic-Adar metric indicate how the information structured in a multiplex network can be optimized for the link prediction task, revealing which layers are redundant. Interestingly, this effect can be asymmetric with respect to predictions in different layers. Our work paves the way for a deeper understanding of the role of different relational data in predicting new interactions and provides a new algorithm for link prediction in multiplex networks that can be applied to a plethora of systems.
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