Amid an increasing water scarcity in many parts of the world, virtual water trade as both a policy instrument and practical means to balance the local, national and global water budget has received much attention in recent years. Building upon the knowledge of virtual water accounting in the literature, this study assesses the efficiency of water use embodied in the international food trade from the perspectives of exporting and importing countries and at the global and country levels. The investigation reveals that the virtual water flows primarily from countries of high crop water productivity to countries of low crop water productivity, generating a global saving in water use. Meanwhile, the total virtual water trade is dominated by green virtual water, which constitutes a low opportunity cost of water use as opposed to blue virtual water. A sensitivity analysis, however, suggests high uncertainties in the virtual water accounting and the estimation of the scale of water saving. The study also raises awareness of the limited effect of water scarcity on the global virtual water trade and the negative implications of the global water saving for the water use efficiency and food security in importing countries and the environment in exporting countries. The analysis shows the complexity in evaluating the efficiency gains in the international virtual water trade. The findings of the study, nevertheless, call for a greater emphasis on rainfed agriculture to improve the global food security and environmental sustainability.

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT’s database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75–80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

Knowing the impact of land-use and land-cover (LULC) changes on the distribution of water yield (WY) is essential for water resource management. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we investigated the spatial-temporal variations of WY from 1990 to 2015 in China’s northern semi-arid region of Beijing–Tianjin–Hebei (Jing-Jin-Ji). We quantified the combined effects of LULC dynamics and climatic variation on WY. Furthermore, we identified the relative contribution of main LULC types to WY. For our study region, the built-up area increased by 35.66% (5380 km²) during the study period. In the meantime, cropland, grassland, and wetland decreased continuously. The expansion of built-up area and decline of vegetated land led to an increase of 1047 million m³ (5.1%) in total WY. The impacts of LULC changes on WY were mainly determined by the biophysical characteristics of LULC composition. Vegetated land has relatively lower WY coefficients due to higher rates of evapotranspiration and water infiltration. Built-up areas and bare land have higher WY coefficients as a result of their impermeable surface. The spatial-temporal analysis of WY with specification of WY coefficients by LULC types can facilitate integrated land-use planning and water resource management.

Studies using Drought Hazard Indices (DHIs) have been performed at various scales, but few studies associated DHIs of different drought types with climate change scenarios. To highlight the regional differences in droughts at meteorological, hydrological, and agricultural levels, we utilized historic and future DHIs derived from the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Water Index (SSWI), respectively. To calculate SPI, SRI, and SSWI, we used a calibrated Soil and Water Assessment Tool (SWAT) for the Karkheh River Basin (KRB) in Iran. Five bias-corrected Global Circulation Models (GCMs) under two Intergovernmental Panel on Climate Change (IPCC) scenarios projected future climate. For each drought type, we aggregated drought severity and occurrence probability rate of each index into a unique DHI. Five historic droughts were identified with different characteristics in each type. Future projections indicated a higher probability of severe and extreme drought intensities for all three types. The duration and frequency of droughts were predicted to decrease in precipitation-based SPI. However, due to the impact of rising temperature, the duration and frequency of SRI and SSWI were predicted to intensify. The DHI maps of KRB illustrated the highest agricultural drought exposures. Our analyses provide a comprehensive way to monitor multilevel droughts complementing the existing approaches.