Spatiotemporal variations of agricultural water footprint and socioeconomic matching evaluation from the perspective of ecological function zone
Introduction
Water resources are one of the most indispensable natural resources and an important basis for the healthy and sustainable development of the regional human and natural systems (Zhang et al., 2020). With the rapid development of the global economy, the explosive population growth, and the acceleration of urbanization, the scale and intensity of human activities continue to increase, leading to an unprecedented increase of water demand in socioeconomic systems (Chouchane et al., 2018). More and more countries and regions are facing severe challenges of water shortage. In all water consumption sectors, agriculture production is the major responsible for water use and water stress, which accounts for more than 70% of total freshwater consumption worldwide (Lovarelli et al., 2016). Therefore, with the impact of climate change on the distribution of water resources and the growth cycle of crops (Bhave et al., 2018), how to achieve the conservation of agricultural water use based on ensuring food security has become a major issue that needs to be solved globally in the 21st century (Li et al., 2019, Piao et al., 2010, Zhao et al., 2017). Especially in countries and regions where water resources are severely lacking and agriculture is highly dependent on irrigation.
To measure the actual consumption of water condensed in products and services in human activities, virtual water was first introduced by Allan et al. (1998). On this basis, for a better understanding of the appropriation of natural capital in terms of the water volumes consumed by various human activities, the concept of water footprint (WF) was proposed by Hoekstra (Hoekstra and Chapagain, 2003). Due to the combination of physical water and virtual water, the water footprint provides a comprehensive and effective approach for evaluating the utilization of freshwater resources, especially in agriculture production (Bazrafshan et al., 2019, Chu et al., 2017, Tuninetti et al., 2015). According to the meaning and role of the water footprint, it contains three components: blue, green, and gray water footprint (Hoekstra and Zhuo, 2017, Zeng et al., 2012). Blue water footprint refers to the consumption of surface and groundwater during the entire production process; green water footprint refers to the consumption of rainwater that does not form runoff; gray water footprint refers to the amount of water used to dilute the concentration of pollutants for achieving the natural concentration of pollutants in the water body or the maximum pollutant discharge concentration specified by local governments (Chapagain and Hoekstra, 2011). However, studies on agricultural water footprint focus more on blue and green water footprint than on gray water footprint because of the differences in definition and nature (Cao et al., 2018, Zhang et al., 2019).
In general, there are two main methods of water footprint research (Lovarelli et al., 2016): the first one is the water footprint network (WFN), which is the original approach developed and employed by Hoekstra and Hung (2002); the second one is the life cycle approach (LCA), which is similar to other LCA studies (Ma et al., 2020a). The most significant difference between them is that the LCA method focuses more on products, while the WFN method focuses more on water resources management (Vanham, 2016). However, according to some scholars’ research (Manzardo et al., 2016, Pfister et al., 2017), the water footprint of a product, producer, consumer, or nation estimated based on two methods has coherent results.
In the past two decades, research on agricultural water footprint has experienced changes from global and national scales (Bulsink et al., 2010, Chapagain et al., 2006, Hoekstra and Hung, 2002, Huang et al., 2019, Mekonnen and Hoekstra, 2018) to regional and watershed scales (Chu et al., 2017, Zeng et al., 2012, Zhuo et al., 2014). Most studies focused too much on virtual water transfer, that is, reducing the consumption of local water resources by importing agricultural products (Zhang et al., 2017). However, in the context of food security also facing severe challenges globally, it is impossible for all regions to solve water scarcity through food imports. Especially in areas where agricultural production plays a great role in economic growth, and rural residents’ income is heavily dependent on agriculture (Su et al., 2020). The optimization of water resources management should be closely integrated with the resources and environmental conditions of regional natural ecosystems and implement targeted strategies that are suitable for regional characteristics, this is why the ecological function zoning theory is constantly being accepted worldwide (Chen et al., 2016, Faheem et al., 2019, Ibidhi and Ben Salem, 2018).
The ecological function zoning is a way to divide a region into areas with different ecological characteristics according to the pattern of the ecological system, ecological environment sensitivity, and the spatial differentiation of ecosystem service functions (Zhai et al., 2016). Its purpose is to identify the types and functions of different ecosystems in the region and the driving factors that cause such differences, which is a prerequisite for the formulation of specific development plans and ecological environmental protection measures suitable for each type of ecological zone (Chen et al., 2016). However, few studies have considered local ecological function planning when analyzing agricultural water footprint and its spatial distribution characteristics, especially in combination with socioeconomic factors, which is a key to achieving sustainable development of regional social ecosystems (Ibidhi and Ben Salem, 2018, Langarudi et al., 2019). Therefore, the research purpose of this study is to take Zhangjiakou City as an example to make up for this gap.
Zhangjiakou City, located in Hebei Province, northwestern China, is a vital water resource and ecological function area in the Beijing-Tianjin-Hebei region. With the development of Beijing-Tianjin-Hebei integration, the deterioration of the ecological environment and the decline of water conservation functions in the region have become increasingly significant, especially the shortage of water resources, which has seriously restricted the sustainable development of the socioeconomic system (Ma et al., 2020b). Based on natural resources and geographic characteristics, all of the counties (districts) are classified into six ecological zones in Zhangjiakou City. The objectives of this study are: (1) to estimate and evaluate the agricultural water footprint of each county (district) in Zhangjiakou City in 2005 and 2015; (2) to analyze the distribution and matching characteristics of agricultural water footprint and socioeconomic factors (planting area, population, and agricultural GDP) in each county (district) using mathematical models, i.e., Gini coefficient and imbalance index firstly; (3) to propose suitable measures and policies for sustainable agricultural development in counties (districts) based on the ecological zone to which they belong.
Section snippets
Crop water footprint
Because the gray water footprint has no effect on crop growth, only blue and green water was taken into consideration for calculation. Water requirements for crop growth are mainly related to the meteorological environment, crop types, soil conditions, crop types, and harvest times, and are usually estimated using the CropWat 8.0 model recommended by the Food and Agriculture Organization (FAO) of the United Nations (Zeng et al., 2012). In CropWat 8.0, first of all, the required information of
Case study
Zhangjiakou City is located in the northwest of Hebei Province in the North China Plain (Fig. 1), around 180 km from the center of the capital Beijing, with a total area of 37,000 km2. It belongs to the transition zone of the North China Plain and the Inner Mongolia Plateau, with elevations increasing from southeast to northwest, which divides Zhangjiakou City into two parts with different geographical features: Bashang Plateau and Baxia Basin. The altitude of Bashang area is 1300–1600 m, while
General characteristics of the agricultural water footprint
The total agricultural water footprint of Zhangjiakou City increased from 3.61 × 109 m3 in 2005 to 5.30 × 109 m3 in 2015, an increase of 1.69 × 109 m3, of which the crop water footprint increased from 1.42 × 109 m3 to 1.52 × 109 m3, an increase of only 9.80 × 107 m3, and the water footprint of animal products increased from 2.19 × 109 m3 to 3.78 × 109 m3, an increase of 1.59 × 109 m3 (Fig. 2). As a result, the contribution rate of crop water footprint dropped from 39% in 2005 to 29% in 2015.
Recommendations for sustainable development
Cultivated land and grassland are the main types of land use in counties of ecological zone I, which account for 42% and 47% in total in Zhangjiakou City, respectively. From 2005 to 2015, the water footprint of crops decreased from 3.40 × 108 m3 to 2.50 × 108 m3. This is because the government has vigorously implemented conversation measures to return farmland to forests and grasslands to restore the ecological environment. The planting area has been reduced from 1.74 × 105 ha to 1.50 × 105 ha.
Conclusions
Zhangjiakou City is in a critical period of economic transformation and development. The shortage of water resources has become a serious factor restricting the sustainable development of society and the economy. The agricultural water footprint of Zhangjiakou City increased from 3.61 × 109 m3 to 5.30 × 109 m3, an increase of 1.69 × 109 m3, of which the water footprint of crops increased by only 9.80 × 107 m3, while the water footprint of animal products increased by 1.59 × 109 m3. Therefore,
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This research was funded by the National Major Science and Technology Program for Water Pollution Control and Treatment, China [grant number 2017ZX07101001], the National Natural Science Foundation of China [grant numbers 41690142, 41601600], the Fundamental Research Funds for the Central Universities, China [grant number SWU019047], and the China Scholarship Council [grant number 201704910850].
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