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To sustain growing food demand and increasing standard of living, global water use increased by nearly 6 times during the last 100 years, and continues to grow. As water demands get closer and closer to the water availability in many regions, each drop of water becomes increasingly valuable and water must be managed more efficiently and intensively. However, soaring water use worsens water scarcity conditions already prevalent in semi-arid and arid regions, increasing uncertainty for sustainable food production and economic development. Planning for future development and investments requires that we prepare water projections for the future. However, estimations are complicated because the future of the world's waters will be influenced by a combination of environmental, social, economic, and political factors, and there is only limited knowledge and data available about freshwater resources and how they are being used. The Water Futures and Solutions (WFaS) initiative coordinates its work with other ongoing scenario efforts for the sake of establishing a consistent set of new global water scenarios based on the shared socio-economic pathways (SSPs) and the representative concentration pathways (RCPs). The WFaS \"fast-track\" assessment uses three global water models, namely H08, PCR-GLOBWB, and WaterGAP. This study assesses the state of the art for estimating and projecting water use regionally and globally in a consistent manner. It provides an overview of different approaches, the uncertainty, strengths and weaknesses of the various estimation methods, types of management and policy decisions for which the current estimation methods are useful. We also discuss additional information most needed to be able to improve water use estimates and be able to assess a greater range of management options across the water–energy–climate nexus.

We present a novel conceptual framework and methodology for studying virtual water trade. We utilize complex network theory to analyze the structure of the global virtual water trade associated with the international food trade. In the global virtual water trade network, the nations that participate in the international food trade correspond to the nodes, and the links represent the flows of virtual water associated with the trade of food from the country of export to the country of import. We find that the number of trade connections follows an exponential distribution, except for the case of import trade relationships, while the volume of water that each nation trades compares well with a stretched exponential distribution, indicating high heterogeneity of flows between nations. There is a power law relationship between the volume of virtual water traded and the number of trade connections of each nation. Highly connected nations are preferentially linked to poorly connected nations and exhibit low levels of clustering. However, when the volume of virtual water traded is taken into account, this structure breaks down. This indicates a global hierarchy, in which nations that trade large volumes of water are more likely to link to and cluster with other nations that trade large volumes of water, particularly when the direction of trade is considered. Nations that play a critical role in maintaining the global network architecture are highlighted. Our analysis provides the necessary framework for the development of a model of global virtual water trade aimed at applications ranging from network optimization to climate change impact evaluations. Key Points The network approach is a novel way to analyze virtual water trade Key features of the global structure are highlighted Roles of countries and continents in virtual water trade are quantified

A global water scarcity assessment for the 21st century was conducted under the latest socio-economic scenario for global change studies, namely Shared Socio-economic Pathways (SSPs). SSPs depict five global situations with substantially different socio-economic conditions. In the accompanying paper, a water use scenario compatible with the SSPs was developed. This scenario considers not only quantitative socio-economic factors such as population and electricity production but also qualitative ones such as the degree of technological change and overall environmental consciousness. In this paper, water availability and water scarcity were assessed using a global hydrological model called H08. H08 simulates both the natural water cycle and major human activities such as water abstraction and reservoir operation. It simulates water availability and use at daily time intervals at a spatial resolution of 0.5° × 0.5°. A series of global hydrological simulations were conducted under the SSPs, taking into account different climate policy options and the results of climate models. Water scarcity was assessed using an index termed the Cumulative Abstraction to Demand ratio, which is expressed as the accumulation of daily water abstraction from a river divided by the daily consumption-based potential water demand. This index can be used to express whether renewable water resources are available from rivers when required. The results suggested that by 2071–2100 the population living under severely water-stressed conditions for SSP1-5 will reach 2588–2793 × 106 (39–42% of total population), 3966–4298 × 106 (46–50%), 5334–5643 × 106 (52–55%), 3427–3786 × 106 (40–45%), 3164–3379 × 106 (46–49%) respectively, if climate policies are not adopted. Even in SSP1 (the scenario with least change in water use and climate) global water scarcity increases considerably, as compared to the present-day. This is mainly due to the growth in population and economic activity in developing countries, and partly due to hydrological changes induced by global warming.

The intensity, duration, and geographic extent of floods in Bangladesh mostly depend on the combined influences of three river systems, the Ganges, Brahmaputra and Meghna (GBM). In addition, climate change is likely to have significant effects on the hydrology and water resources of the GBM basin and may ultimately lead to more serious floods in Bangladesh. However, the assessment of climate change impacts on the basin-scale hydrology by using well-calibrated hydrologic modeling has seldom been conducted in the GBM basin due to the lack of observed data for calibration and validation. In this study, a macroscale hydrologic model H08 has been applied over the basin at a relatively fine grid resolution (10 km) by integrating the fine-resolution DEM (digital elevation model) data for accurate river networks delineation. The model has been calibrated via the analysis of model parameter sensitivity and validated based on long-term observed daily streamflow data. The impacts of climate change (considering a high-emissions path) on runoff, evapotranspiration, and soil moisture are assessed by using five CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs (global circulation models) through three time-slice experiments; the present-day (1979–2003), the near-future (2015–2039), and the far-future (2075–2099) periods. Results show that, by the end of 21st century, (a) the entire GBM basin is projected to be warmed by ~4.3 °C; (b) the changes of mean precipitation (runoff) are projected to be +16.3% (+16.2%), +19.8% (+33.1%), and +29.6% (+39.7%) in the Brahmaputra, Ganges, and Meghna, respectively; and (c) evapotranspiration is projected to increase for the entire GBM (Brahmaputra: +16.4%, Ganges: +13.6%, Meghna: +12.9%) due to increased net radiation as well as warmer temperature. Future changes of hydrologic variables are larger in the dry season (November–April) than in the wet season (May–October). Amongst the three basins, the Meghna shows the highest increase in runoff, indicating higher possibility of flood occurrence. The uncertainty due to the specification of key model parameters in model predictions is found to be low for estimated runoff, evapotranspiration and net radiation. However, the uncertainty in estimated soil moisture is rather large with the coefficient of variation ranging from 14.4 to 31% among the three basins.

A novel global water scarcity assessment for the 21st century is presented in a two-part paper. In this first paper, water use scenarios are presented for the latest global hydrological models. The scenarios are compatible with the socio-economic scenarios of the Shared Socio-economic Pathways (SSPs), which are a part of the latest set of scenarios on global change developed by the integrated assessment, the IAV (climate change impact, adaptation, and vulnerability assessment), and the climate modeling community. The SSPs depict five global situations based on substantially different socio-economic conditions during the 21st century. Water use scenarios were developed to reflect not only quantitative socio-economic factors, such as population and electricity production, but also key qualitative concepts such as the degree of technological change and overall environmental consciousness. Each scenario consists of five factors: irrigated area, crop intensity, irrigation efficiency, and withdrawal-based potential industrial and municipal water demands. The first three factors are used to estimate the potential irrigation water demand. All factors were developed using simple models based on a literature review and analysis of historical records. The factors are grid-based at a spatial resolution of 0.5° × 0.5° and cover the whole 21st century in five-year intervals. Each factor shows wide variation among the different global situations depicted: the irrigated area in 2085 varies between 2.7 × 106 and 4.5 × 106 km2, withdrawal-based potential industrial water demand between 246 and 1714 km3 yr−1, and municipal water between 573 and 1280 km3 yr−1. The water use scenarios can be used for global water scarcity assessments that identify the regions vulnerable to water scarcity and analyze the timing and magnitude of scarcity conditions.

The international trade of food commodities links water and food systems, with important implications for both water and food security. The embodied water resources associated with food trade are referred to as \"virtual water trade\". We present the first study of the impact of climate change on global virtual water trade flows and associated savings for the year 2030. In order to project virtual water trade and savings under climate change, it is essential to obtain projections of both bilateral crop trade and the virtual water content of crops in each country of production. We use the Global Trade Analysis Project model to estimate bilateral crop trade under changes in agricultural productivity for rice, soy, and wheat. We use the H08 global hydrologic model to determine the impact of climatic changes to crop evapotranspiration for rice, soy, and wheat in each country of production. Then, we combine projections of bilateral crop trade with estimates of virtual water content to obtain virtual water trade flows under climate change. We find that the total volume of virtual water trade is likely to go down under climate change, due to decreased crop trade from higher crop prices under scenarios of declining crop yields and due to decreased virtual water content under high agricultural productivity scenarios. However, the staple food trade is projected to save more water across most climate change scenarios, largely because the wheat trade re-organizes into a structure where large volumes of wheat are traded from relatively water-efficient exporters to less efficient importers.

Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1(from 60 000 to 85 000 km³ yr−1), and simulated runoff ranges from 290 to 457 mm yr−1(from 42 000 to 66 000 km³ yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degreeday approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

Global food security increasingly relies on the trade of food commodities. Freshwater resources are essential to agricultural production and are thus embodied in the trade of food commodities, referred to as “virtual water trade.” Agricultural production predominantly relies on rainwater (i.e., “green water”), though irrigation (i.e., “blue water”) does play an important role. These different sources of water have distinctly different opportunity costs, which may be reflected in the way these resources are traded. Thus, the temporal dynamics of the virtual water trade networks from these distinct water sources require characterization. We find that 42 × 109 m3 blue and 310 × 109 m3 green water was traded in 1986, growing to 78 × 109 m3 blue and 594 × 109 m3 green water traded in 2008. Three nations dominate the export of green water resources: the USA, Argentina, and Brazil. As a country increases its export trade partners it tends to export relatively more blue water. However, as a country increases its import trade partners it does not preferentially import water from a specific source. The amount of virtual water that a country imports by increasing its import trade partners has been decreasing over time, with the exception of the soy trade. Both blue and green virtual water networks are efficient: 119 × 109 m3 blue and 105 × 109 m3 green water were saved in 2008. Importantly, trade has been increasingly saving water over time, due to the intensification of crop trade on more water‐efficient links. Key Points Blue and green water are traded differently due to distinct opportunity costs Green water exports oscillate over time, while imports steadily increase Both blue and green networks have been increasingly saving water over time

China is the largest electricity producer in the world and more than 70% of its electricity is from coal-fired power plants where water is an indispensable input, primarily for cooling purposes. Water shortages could hamper coal-fired power plants productions and result in economic losses. In this study, we simulate monthly river flows in China on a 0.5° × 0.5° spatial resolution using a calibrated physically based hydrological model, H08, that incorporates human interventions during the current (1981–2014) and future period 2050s (2035–2065) under two carbon emission scenarios Representative Concentration Pathway 2.6 and 8.5. Water demands by individual power plants are calculated based on plant-level data. We define power plants as facing low-flow water risks when the monthly 10 year return low flow is projected to be below the plant’s water withdrawal requirement. We find that around 10% of China’s coal-fired power capacities face low-flow water risks from July to October (the monsoon peak in the eastern Asia), and 20% the rest of the year. Particularly in the North Grid, around 35% to 60% of its regional coal-fired power capacity is at such risks from December to June. Under climate change, low-flow amounts are expected to increase in the current dry northern China except decreasing in the northwest, which is expected to alleviate the low flow water risks facing coal power plants in China except in the Northwest Inland River Basin. In the East and South Grids, if their growing electricity demands continue depending on coal, increasing utilization rate of coal power facilities can lead to heightened demand-driven water risks.

Climate change and socio‐economic development place an increasing pressure on essential natural resources, such as arable land and freshwater. The international food trade can save water globally by redistributing commodities produced relatively more water‐efficiently. We focus on the global virtual water trade network associated with international staple food trade from 1986–2008. This study aims to determine which variables control the network's structure and temporal evolution, and to estimate changes in the network under future scenarios. Our fitness model reproduces both the topological and weighted characteristics of the network for the whole period. Undirected and directed network properties are well reproduced in each year, assuming as sole controls each nation's GDP, mean annual rainfall, agricultural area and population. The future structure of the network is estimated using climate and socio‐economic projections, showing that volumes of virtual water traded will become increasingly heterogeneous and the importance of dominant importing nations will further strengthen. Key Points Simple national variables reproduce the complete network's structure over time Population is used as a new control to reproduce directed virtual water flows The heterogeneity of virtual water trade flows is expected to increase by 2030