Global sensitivity of high‐resolution estimates of crop water footprint

Abstract

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.

Benchmark levels for the consumptive water footprint of crop production for different environmental conditions: a case study for winter wheat in China

“…The consumptive WF of a crop in m 3 t −1 most strongly depends on the crop yield in t ha −1 and much less on the evapotranspiration from the crop over the growing period in m 3 ha −1 (Tuninetti et al, 2015;Mekonnen and Hoekstra, 2011). The simulated consumptive WFs of winter wheat in China have been based on modelling under a hypothetical condition without effects of managerial factors on crop growth.…”

“…Setting WF benchmarks for different products, particularly water-intensive products like crops, is fundamental for wise water allocation and fair sharing of water resources among different sectors and users (Hoekstra, 2013). WF benchmarks of crop production could be global, but would preferably be context-specific, given the fact that the WF of growing a crop varies as a function of environmental factors such as climate and soil (Mekonnen and Hoekstra, 2011;Siebert and Döll, 2010;Tuninetti et al, 2015).…”

Past, current and future water footprints, water scarcity and virtual water flows in China

“…Global green and blue WFs of crop production have been mapped at a spatial resolution of 30 by 30 arc minute (Rost et al, 2008;Liu et al, 2009;Fader et al, 2010;Hanasaki et al, 2010;) and even at 5 by 5 arc minute (Mekonnen and Hoekstra, 2010;Siebert and Doll, 2010;Mekonnen and Hoekstra, 2011;Tuninetti et al, 2015). The global grey WF related to anthropogenic nitrogen (N) loads from croplands to groundwater and surface water has been mapped at 5 by 5 arc min level by Mekonnen and Hoekstra (2011), assuming simple leaching-runoff ratios, and the additional grey WF related to N loads from the domestic and industrial sectors has been estimated by .…”

“…climate, soil) and management conditions (e.g. irrigation technology, mulching practice) (Zwart et al, 2010;Mekonnen and Hoekstra, 2011;Tuninetti et al, 2015). WF benchmarks can serve as reference and reduction target for producers who have WFs above the benchmark (Hoekstra, 2013;.…”

“…However, there are only a few studies on the sensitivities in WF accounting. Examples include the assessment of the sensitivity of the WF of maize to climate variables in the Po Vally in Northern Italy by Bocchiola et al (2013), the assessment of the sensitivity of the WF of fresh algae cultivation to changes in evaporation estimation method by Guieysse et al (2013), and the global sensitivity analysis to explore the sensitivity of the WF of crops to input parameter variability by Tuninetti et al (2015).…”