Search

The Grain for Green project (GGP), initialized by the Chinese government in 1999, has achieved substantial achievements accompanied by a decrease in surface runoff on the Loess Plateau, but the impacts of large-scale afforestation on regional water resources are uncertain. Hence, the objective of this study was to explore the impact of land use change on generalized water resources and ecological water stress using the blue and green water concepts, taking the Yanhe River Basin as the case study. The Soil and Water Assessment Tool (SWAT) was applied to quantify the green water and blue water, which are defined as generalized water resources. The ecological water requirement of vegetation (forest and grass), agricultural water footprint and virtual water flow are considered regional water requirements. The land use types of 1980 (Scenario I) and 2017 (Scenario II) were entered into the SWAT model while keeping the other parameters constant to isolate the influence of land use changes. The results show that the average annual differences in blue, green and generalized water resources were -72.08 million m3, 24.34 million m3, and -47.74 million m3, respectively, when the simulation results of Scenario II were subtracted from those of Scenario I, which shows that land use change caused by the GGP led to a decrease in blue and generalized water resources and an increase in green water resources. Surface runoff in Scenario I was more than that in Scenario II in all of the years of the study period from 1980–2017, and green water storage in Scenario I was more than that in Scenario II in all of the years of the study period except in 1998; although lateral flow in Scenario I was less than that in Scenario II except in 2000 and 2015, as was groundwater runoff in 1992, 2000 and 2015, and green water flow in 1998. Blue water flow, green water storage and green water flow in Scenario II were less than those in Scenario I in the whole basin, 12.89 percent of the basin and 99.21 percent of the basin, respectively. The total water footprint increased from 1995 to 2010 because the forest water footprint increased significantly in this period, although the agricultural water footprint and grass water footprint decreased. The ecological water stress index values had no obvious temporal change trends in either land use scenario, but the ecological water stress index in Scenario II was greater than that in Scenario I, which illustrates that the GGP led to an increase in ecological water stress from the perspective of generalized water resources. [ABSTRACT FROM AUTHOR]

Climate change can impact the yield and water footprint of crops. Therefore, assessing such impacts carries great significance for regional water and food security. This study validated and verified the variety parameters of winter wheat for the Decision Support System for Agrotechnology Transfer (DSSAT) model, using the long-term (1993–2013) growth and yield data observed from six agricultural experiment stations in the Haihe River Basin (HRB), China. The growth process was simulated under three representative concentration pathways (RCPs), named RCP2.6, RCP4.5, and RCP8.5—climate scenarios driven by the HadGEM2-ES model. The variety parameters of winter wheat showed high accuracy in the simulation of the anthesis and maturity dates, and could be used for long-term prediction of the growth process. The trends of climate change had positive impacts on the water footprint of winter wheat but adverse impacts on the yield. The growing period was shortened by 3.6 days, 4.7 days, and 5.0 days per decade in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, due to the rapid accumulation of heat. The yield would be increased in lower emissions scenarios (17% in RCP2.6), but decreased in high-emissions scenarios due to high temperatures, which may restrict the growth of wheat. The water footprint was decreased by 10%, 11%, and 13% in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, indicating that the water-use efficiency could be improved in the future. The results showed broad application prospects of the DSSAT model in simulating the response of crop growth to climate change. [ABSTRACT FROM AUTHOR]