Dynamic forecasting of agricultural water footprint based on Markov Chain-a case study of the Heihe River Basin

Water footprint forecasting is essential to measuring the embodied water resource consumption and achieving the sustainable water governance. Agricultural sector is conventionally a water intensive sector and accounts for large amount of water consumption in the river basins. In this paper, a system dynamics model is combined with Markov Chain, considering economic development, agriculture water consumption, population and agricultural ecosystem, to forecast the total agricultural water footprint (AWF) as well as its pressure on the freshwater ecosystem. Wheat, coin, potato, alfalfa, vegetables and flax are chosen as representative crops for AWF accounting in the integrated model. A case study of the Heihe River Basin in China during 2010–2030 shows that, the AWFs are 9.67 × 10⁸ m³, 1.02 × 10⁹ m³, 1.05 × 10⁹ m³ and 9.27 × 10⁸ m³ under Baseline Scenario, Moderate Risk Scenario, High Risk Scenario and Sustainable Scenario, respectively. It is concluded that the improvement on agricultural water efficiency may decrease the AWF, which can be achieved by agricultural water conservation, irrigation canal construction, maintenance funding and investments, agricultural planting adjustment, and virtual water strategies.