Articles | Volume 20, issue 10
Research article
18 Oct 2016
Research article |  | 18 Oct 2016

Analysis of the characteristics of the global virtual water trade network using degree and eigenvector centrality, with a focus on food and feed crops

Sang-Hyun Lee, Rabi H. Mohtar, Jin-Yong Choi, and Seung-Hwan Yoo

Abstract. This study aims to analyze the characteristics of global virtual water trade (GVWT), such as the connectivity of each trader, vulnerable importers, and influential countries, using degree and eigenvector centrality during the period 2006–2010. The degree centrality was used to measure the connectivity, and eigenvector centrality was used to measure the influence on the entire GVWT network. Mexico, Egypt, China, the Republic of Korea, and Japan were classified as vulnerable importers, because they imported large quantities of virtual water with low connectivity. In particular, Egypt had a 15.3 Gm3 year−1 blue water saving effect through GVWT: the vulnerable structure could cause a water shortage problem for the importer. The entire GVWT network could be changed by a few countries, termed "influential traders". We used eigenvector centrality to identify those influential traders. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high eigenvector centrality with large volumes of green water trade. In the case of blue water trade, western Asia, Pakistan, and India had high eigenvector centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used high proportions of internal water resources for virtual water export (32.9 and 25.1 %); thus other traders should carefully consider water resource management in these exporters.

Short summary
Virtual water trade (VWT) embedded in crop trade is an important component of water management. Vulnerable importers in VWT were classified through connectivity and volume of VWT using degree centrality of a VWT network. Influential traders on entire VWT were classified through eigenvector centrality of a VWT network.