Virtual water trade patterns in relation to environmental and socioeconomic factors: A case study for Tunisia

Hatem Chouchane; Maarten S Krol; Arjen Y Hoekstra

doi:10.1016/j.scitotenv.2017.09.032

Virtual water trade patterns in relation to environmental and socioeconomic factors: A case study for Tunisia

Sci Total Environ. 2018 Feb 1:613-614:287-297. doi: 10.1016/j.scitotenv.2017.09.032. Epub 2017 Sep 14.

Authors

Hatem Chouchane¹, Maarten S Krol², Arjen Y Hoekstra³

Affiliations

¹ Water Engineering & Management, University of Twente, Enschede, The Netherlands. Electronic address: hatemchouchane1@gmail.com.
² Water Engineering & Management, University of Twente, Enschede, The Netherlands.
³ Water Engineering & Management, University of Twente, Enschede, The Netherlands; Institute of Water Policy, Lee Kuan Yew School of Public Policy, National University of Singapore, 259770, Singapore.

PMID: 28917167
DOI: 10.1016/j.scitotenv.2017.09.032

Abstract

Growing water demands put increasing pressure on local water resources, especially in water-short countries. Virtual water trade can play a key role in filling the gap between local demand and supply of water-intensive commodities. This study aims to analyse the dynamics in virtual water trade of Tunisia in relation to environmental and socio-economic factors such as GDP, irrigated land, precipitation, population and water scarcity. The water footprint of crop production is estimated using AquaCrop for six crops over the period 1981-2010. Net virtual water import (NVWI) is quantified at yearly basis. Regression models are used to investigate dynamics in NVWI in relation to the selected factors. The results show that NVWI during the study period for the selected crops is not influenced by blue water scarcity. NVWI correlates in two alternative models to either population and precipitation (model I) or to GDP and irrigated area (model II). The models are better in explaining NVWI of staple crops (wheat, barley, potatoes) than NVWI of cash crops (dates, olives, tomatoes). Using model I, we are able to explain both trends and inter-annual variability for rain-fed crops. Model II performs better for irrigated crops and is able to explain trends significantly; no significant relation is found, however, with variables hypothesized to represent inter-annual variability.