Fresh water is consumed during agricultural production. With the shortage of
water resources, assessing the water use efficiency is crucial to effectively
manage agricultural water resources. The water footprint is an improved index
for water use evaluation, and it can reflect the quantity and types of water
usage during crop growth. This study aims to establish a method for
calculating the regional-scale water footprint of crop production based on
hydrological processes, and the water footprint is quantified in terms of
blue and green water. This method analyses the water-use process during the
growth of crops, which includes irrigation, precipitation, groundwater,
evapotranspiration, and drainage, and it ensures a more credible evaluation
of water use. As illustrated by the case of the Hetao irrigation
district (HID), China, the water footprint of wheat, corn and sunflowers were
calculated using this method. The results show that canal water loss and
evapotranspiration were responsible for most of the water consumption and
accounted for 47.9 % and 41.8 % of the total consumption, respectively.
The total water footprint of wheat, corn and sunflowers were 1380–2888, 942–1774 and
2095–4855 m
Human activities and climate change have serious effects on the availability of water resources (Nijssen et al., 2001; Haddeland et al., 2014). Agricultural production is major consumer of global water resources and accounts for 85 % of the global blue water (surface or groundwater) consumption (Shiklomanov, 2000; Vörösmarty et al., 2010). In China, 63 % of all water is used for agricultural production each year, and the area of irrigated farmland is 39.6 % of the total arable land. Irrigation is the key to ensuring agricultural production (NBSC, 2016). With the rapid development of China's economy, the demand for water has increased in industrial production and in the lives of residents (Duh et al., 2008; Liu et al., 2008; Bao and Fang, 2012). Environmental pollution reduces water availability (Jiang, 2009; Schwarzenbach et al., 2010), and these changes place great pressure on regional water resources (Piao et al., 2010; Wang et al., 2014); meanwhile, climate change aggravates the situation (Elliott et al., 2014). With limited water resources, economic demand for water will inevitably and gradually take up the agricultural water use, which is a challenge for maintaining steady agricultural production (Chen, 2007; Khan et al., 2009), especially in the dry areas of northern China (Deng et al., 2006; Du et al., 2014). Strengthening agricultural water management and improving water use efficiency are significant aspects of handling water scarcity, and a reasonable evaluation of the water resource for crop production is the premise for developing an agricultural water management plan and implementing water-saving measures. Therefore, precisely evaluating the effective utilization ratio of current agricultural water use, improving the utilization efficiency and reducing the negative impact of the reduction of available agricultural water on agriculture production are important issues that all countries need to address globally, which are also of vital importance for ensuring food production and reducing the pressure on water resources. The water footprint theory provides new insights and ideas to solve these problems (Hoekstra, 2003). The water footprint is an indicator of freshwater use and can be used to quantify water consumption throughout the production supply chain. It reflects the amount of green, blue and grey water that is consumed (Hoekstra et al., 2011). In the agricultural sector, it can also be used to evaluate whether a crop's water footprint is reasonable and whether it varies regionally. Since green water can be used in agricultural production, some measures can be taken to reduce the water footprint of crop production, especially reducing the consumption of blue water, thereby easing the demand for blue water in agriculture. The accurate and precise quantification of the crop production water footprint is the premise to achieving the above goals.
Currently, based on two main methods proposed by Hoekstra et al. (2011), many
scholars have quantified various levels of crop production water footprint,
on a global level (Mekonnen and Hoekstra, 2011), a national level, such as
Europe (Vanham and Bidoglio, 2013) and China (Zhao et al., 2009), and a
regional level, such as Beijing (Sun et al., 2013a), Cremona province
(Bocchiola, 2015) and Hetao (Luan et al., 2018). The first is the crop water
requirement method (Cao et al., 2014; Sun et al., 2013c). This method
simulates the actual evapotranspiration (ET) of crops under optimal
conditions with the potential ET calculated by the Penman–Monteith Equation
(Allen et al., 1998) and the effective precipitation calculation provided by
the US Department of Agriculture Soil Conservation Service (USDA SCS) (Doll
and Siebert, 2002). The green water consumption is the smaller value of total
crop actual ET and effective precipitation. The blue water consumption is
obtained from the difference between the total crops actual ET and effective
precipitation. Finally, when combined with crop yields, the crop blue and
green water footprint (m
These methods can simulate actual ET throughout the crop growing period according to the soil water balance under optimal or suboptimal conditions. The blue water consumption is the smaller value of net irrigation water and the net irrigation water requirement. The green water consumption is equal to the total actual ET minus blue water. Both of the above methods are based on empirical formulas. A few scholars have attempted to calculate the regional-scale water footprint; for example, Sun et al. (2013b) used the difference between diversion and drainage to calculate the water footprint of crop production in irrigated areas. However, these methods have certain shortages, which are as follows:
First, the applicability of the empirical method has not been determined, that is, whether the method is applicable to the field scale or regional scale of the water footprint calculation needs further study. These methods calculated the field-scale water footprint with net irrigation water considered as irrigation water and without considering water loss during transport, which definitely serves crop growth. Therefore, these methods are field-scale methods, whereas a regional-scale method should include the aforementioned two losses. At present, irrigation water mainly refers to the net irrigation water used by crops in the field. Current irrigation water analysis methods have not considered water loss during water delivery and drainage. Therefore, the calculation of the water footprint at the field scale cannot be accurately applied to irrigated agriculture. However, there are still few methods to calculate the water footprint on the regional scale.
Second, the irrigation data in these methods are simulation values and not based on the actual irrigation time and irrigation quota (the amount of water demanded for crop irrigation); therefore, these data cannot reflect the real situation of the local water usage due to the incomplete simulation data. At the same time, the traditional method does not completely analyse the water footprint components of water resources in the process of water diversion, water transfer, irrigation and drainage.
Third, the current regional-scale method has not been appropriately established. The method that Sun et al. (2013b) used had the aforementioned limitations. It included all of the water consumption, but it could not distinguish the specific source of blue water from canal loss, field actual ET or groundwater. Due to its low spatial resolution, only the water footprint of the entire irrigated area could be calculated instead of the difference inside this area.
Location of the Hetao irrigation district (HID) in China.
Currently, most studies focus on the field scale and lack a systematic evaluation of the whole process of water consumption during crop growth. To overcome this problem, this study put forward an improved regional-scale calculation method of the crop water footprint based on a hydrological process analysis and used it to quantify the crop water footprint in the Hetao irrigation district (HID). This method simulated the hydrological cycle of the region based on a physical hydrological model using the soil and water assessment tool (SWAT). Based on the method, this study analysed the water input and output during crop production, and calculated the water consumption in crop growth, field drainage and water loss during canal water transport. Combined with crop yields, the water footprint of crop production at the regional scale was quantified. This method will provide comprehensive information for the analysis of water consumption during crop production process and improve the spatial resolution of the regional distribution of the water footprint of crop production.
The Hetao irrigation district (HID) is located in the middle of the Yellow
River basin in western Inner Mongolia (Fig. 1) and is one of the three
largest irrigation districts in China. The HID has a continental monsoon
climate with the lowest temperatures in January (average
Irrigation water is diverted from the Yellow River. The irrigation and drainage systems in the HID are composed of irrigation canals and drainage ditches; the irrigation system has a general main canal (228.9 km) and 12 main canals (total 755 km), and the drainage system has a general main ditch (227 km) and 12 main ditches (total 523 km). The main crops include wheat, corn and sunflowers (Fig. 1).
Data used in the study and the resources.
The SWAT model is a semi-distributed physical hydrological model. The model was
developed by the USDA Agricultural Research Center, and it uses climate,
soil, topography, plants and land management practises to simulate the
hydrologic, sediment, crop growth and nutrient cycle. The model partitions a
watershed into sub-basins by topography and then partitions the sub-basins
into hydrologic response units (HRU) based on soil type and land use to
assess soil erosion, non-point pollution and hydrologic processes (Haverkamp
et al., 2002). The water balance equation governed by the hydrologic
component of the SWAT model (Neitsch et al., 2011) is as follows:
Sub-basins and study areas.
The data required by the SWAT model includes a digital elevation model (DEM), soil data, land use and hydrological and climate data (Table 1). The climate data were obtained from five weather stations in the HID.
The water efficiency of the canal system in this model was obtained from local agricultural administrations (AHID, 2015). To divide the sub-basins, we defined the drainage ditch as the stream (AHID, 2015) and burn-in into the DEM, and the simulation results were verified by the discharge of the drainage ditch.
The model generated five outlets and 73 sub basins, and the measured data of the first outlet in the study area were obtained (Fig. 2). Therefore, this study chose the area controlled by this outlet as the study area. The crops' yields (wheat, corn and sunflowers) required for the calculation of the water footprint were obtained from the statistical yearbook of the local agricultural administrations (AHID, 2015).
The sequential uncertainty fitting (SUFI-2) algorithm in SWAT Calibration and Uncertainty Programs (SWAT-CUP) was applied for calibration and validation (Abbaspour et al., 2007; Abbaspour, 2012) by comparing the simulated stream discharge from the model with the measured discharge data. The global sensitivity analysis integrated in SUFI-2 was used to evaluate the hydrologic parameters for the discharge simulation, and then the optimal simulation was established by adjusting the sensitivity parameters and through multiple iterations. The calibration period was from 2006 to 2009, and the validation period was from 2010 to 2012. The result of the SWAT calibration and validation process is satisfactory, and the detailed process is available in the Supplement.
Based on the water footprint theory framework provided by Hoekstra et al. (2011), this study suggests a new way of quantifying the regional-scale water footprint of crop production (Fig. 3).
In this study, green water consumption is the effective precipitation during the crop growth process. Blue water consumption includes canal water loss during delivery, the ET produced by consumption of irrigation water and groundwater for crops growth, and the drainage in the fields. To calculate the canal water loss, an extra model needs to be established according to the HID situation, and the other can be simulated and obtained by the SWAT model.
The flow chart for calculating the regional-scale water footprint.
Water consumption in the fields consists of four parts, including the actual ET
of precipitation, irrigation water, groundwater utilised by crops and field
drainage. This study set up two scenarios and calculated the above water
consumption by changing the sources of water in the SWAT model. In scenario 1 (S1),
crop water consumption was derived from precipitation and irrigation
water (irrigation systems and irrigation quotas are based on local
irrigation methods), i.e. the actual situation of crop water use. In
scenario 2 (S2), crop water consumption was only derived from precipitation
without irrigation. The S2 was used to calculate the consumption of green
water. In this study area (HID), because of less rainfall, the effective
precipitation formed by precipitation events is all used for crop growth.
Therefore, the consumption of green water for crops is equal to the
effective precipitation, which means that green water is reflected by
calculating the effective precipitation stored in soil by the SWAT model. The
calculation formula is as follows.
Water transfer loss is a kind of water loss in the process of channel water delivery, and it is an important part of blue water consumption in crop production. For a piece of cultivated land, the water loss during the process of the crop production includes the loss of water from the water source to the field, flowing through the canal system. In the Hetao irrigation district, the irrigation canal is composed of seven grades (general main canal, main canal, sub-main canal, branch canals, lateral canals, field canals and sub-lateral canals) because of the complex distribution of canal system and the lack of hydrological data in irrigation districts (the lack of an effective utilization coefficient of canal water below the main canal). Therefore, in calculating the water loss of canal system during crop production process, we generalised the Hetao irrigation district into a model similar to the histogram (Fig. 4).
We divide the total water loss of canal system into two parts. Part A is the
loss of the main canal and canal, and Part B is the loss of the remaining
canal system (the water loss of the sub-main canal and its sub-channels at
all levels). The calculation of water loss in Part A is as follows; first,
the water loss of each section is calculated by dividing the main canal into
equal distances (10 km). Then the water transfer loss of each section of the
canal is allocated to each field downstream (Eq. 10), thereby obtaining
the water transfer loss in the crop production process on the field block.
Therefore, the actual water loss caused by irrigation in a field is the sum
of the water loss of the transfer canal and the canal in the upstream. We
assign the actual water loss of the field by irrigation (
Due to the lack of the effective utilization coefficient of canal water and
the distribution map of the canals at all levels and below, the calculation
process of the water loss in Part B is as follows; the remaining canal loss
in each irrigation canal is divided by the main canal irrigation and the
unit area loss of the canal control area is obtained. Then, the amount of
water loss per unit area within the control range of each main canal in the
irrigation area (
Model for the calculation of water loss in the canal system.
Note that
Figure 5 shows the average water input and consumption of the study area in
the process of water diversion, transportation, irrigation and drainage from 2006
to 2012. In HID, the water input for irrigation for the three crops in
the study area was
The amount of water during crop growth (
Green water is the precipitation used for crop growth; therefore, the green
water footprint is highly correlated with precipitation in its growth
period. Wheat's growth period is from April to July, whereas that of corn
and sunflowers is from May to September. During the growth period of wheat,
the mean precipitation from 2006 to 2012 was 108.9 mm, and for corn and
sunflowers, the corresponding mean precipitation was 176.1 mm. The green
footprint of wheat during the growth period was lower than that of corn and
sunflowers because of the lower mean precipitation in the wheat growth
period. The green water consumption of corn was close to the value of
the sunflower. The average green water consumption of wheat, corn and sunflowers
were 895, 1441 and 1419 m
Blue water is the surface water used for crop growth in this study. In blue
water consumption, the farther away from the watershed inlets, the longer the
canal was and the larger the water loss of the three crops. Northeast of the
irrigation area (parts of Wuyuan and Wulate Qianqi) and due to the far
distance from watershed inlets, canal water loss in these places was much
higher than that in other areas, and the maximum canal water loss of wheat,
corn and sunflowers reached 8977, 8929 and 9951 m
The actual ET and the discharge of the three crops was higher in the east than in the west, which was due to the higher evaporation in the east than in the west. Meanwhile, Fig. 6 shows that the actual ET in the field was complementary with discharge. The higher the actual ET, the smaller the discharge and vice versa.
The spatial difference of the green water footprint of wheat, corn and
sunflowers in HID was obvious (Fig. 7). It can be seen from the figure that
the overall distribution of the green water footprint of the three crops was
higher in the east than it was in the west. However, the distribution of
green water footprint was somewhat different for each crop. Wheat had the
largest green water footprint in Wuyuan (197 m
The blue water footprint of the crops is produced by blue water that is
consumed during crop growth. The blue water consumption during crop growth
mainly includes the loss during transportation, actual ET and field
drainage. Figure 8 shows the spatial variability of wheat, corn and sunflowers
in HID. The overall distribution of the total water footprint of the three
crops was higher in the east than in the west and higher in the north than
in the south. However, the specific distribution was somewhat different for
each crop. Wheat had the largest blue water footprint in Wulate Qianqi
(2714 m
Spatial distribution of the different water consumption of three crops
(m
The spatial distribution of the green water footprint of crop
production in the HID (m
The spatial distribution of the blue water footprint of crop
production in the HID (m
The total water footprint of crop production consists of both blue and green
water footprints during the crop growth period. Figure 9 shows the total water
footprint of crop production and spatial variability of wheat, corn, and
sunflowers in HID. The overall distribution of the total water footprint of
the three crops was higher in the east (Wulate Qianqi and Wuyuan) than it was
in the west (Dengkou), followed by the central region (Hangjin Houqi and
Linhe), and it was higher in the north than in the south. However, the specific
distribution was somewhat different for each crop. Wheat had the largest
total water footprint in the east (Wulate Qianqi, 2888 m
The spatial distribution of the total water footprint of crop
production in the HID (m
In this paper, the calculation method for calculating the crop production water footprint is divided into the field-scale and regional-scale method, according to the calculation boundary of water consumption in the crop growth process. The field-scale water footprint is composed of the transpiration of crops and the evaporation of soil, and the water loss during transportation is not included. The regional-scale water footprint calculation method considers all of the water consumption related to crop growth from the water source to the field. It not only includes the ET from the field but also the water loss during transportation in the canal system and the water loss discharged out of the region.
Currently, irrigated farmland occupies 39.6 % of the total arable land in
China (NBSC, 2016). Globally, irrigated area account for 20.6 % of all
arable land (FAO, 2016). Overall, the yields of irrigation agriculture are
much higher than those of rain-fed agriculture. Figure 10 illustrates the
water sources and use conditions of two types of agriculture. In irrigated
agriculture, water (blue water) goes through the following processes from
water source to field; these are water diversion, water transportation (canal
system or pipeline) and different methods (surface irrigation, sprinkler
irrigation, drip irrigation, etc.) to irrigate crops and excess water
discharged from the field. In irrigated agricultural production, especially
in areas where water is transported through channels for irrigation, a large
amount of water is lost (canal leakage or water evaporation) during the
transportation process, which is indirectly used for crop production. The
transportation process generates large costs (energy, machinery, facilities,
management, etc. for water diversion). Therefore, this water loss is also a
part of the crop production water footprint. In China, the irrigation water
consumption was
Irrigation agriculture and rain-fed agriculture.
The different scales of calculating water footprint.
For the field-scale method, the calculated value was less than the actual value, because it did not consider the loss of water during transportation or discharge, and the actual water footprint of irrigation agriculture cannot be precisely assessed. At present, most studies use the field-scale method (e.g. CROPWAT model) (Lovarelli et al., 2016), so these studies mainly focus on agricultural water use at field scale, lacking an analysis of the entire process of agricultural production water use, which is also the shortcoming of the current research on the crop production water footprint. Therefore, using the regional-scale method to calculate the crop water footprint, especially in irrigation agriculture, is the basis for a comprehensive and accurate evaluation of a crop production water footprint in China and other regions or countries.
In HID, the water footprint of three crops (wheat, corn and sunflowers)
calculated by the regional-scale method were 1380–2888,
942–1774, and 2095–4855 m
This method also has limitations. The method requires more data types (e.g. DEM, land use, soil data, climate data, hydrological data and crop management data), and higher data resolution. Therefore, the method is not applicable to areas where the above data are lacking.
The water footprint of crop production is affected by crop species. Different crops have different water use characteristics and different growth periods. Therefore, adjusting the crop planting structure can change the water supply in the region (Fasakhodi et al., 2010), which in turn affects the water footprint of crop production. At the same time, changing the crop pattern and planting crops whose growth periods are consistent with the precipitation period can increase the utilization of green water, reduce the consumption of blue water and reduce the pressure on local water resources (Liu et al., 2018). This study found that in the HID, the growth period of sunflowers is basically the same as the precipitation period. Consequently, expanding the planting area of sunflowers can make better use of local precipitation resources and reduce the use of blue water.
The crop yield is an important factor affecting the water footprint of crop production. Selecting crop varieties with high yields and improving agricultural management measures play an important role in increasing crop yields. Sun et al. (2013b) found that improving agricultural management measures is an important factor in increasing crop yield and reducing the water footprint of crop production. Liu et al. (2014, 2015) discussed the water use situation and virtual water flow in the Hetao irrigation district and found that crop yield had an important impact on the water footprint of crop production, and with the increasing of the crop yield per unit area, the water footprint of crop production had declined.
The efficiency of the irrigation system is affected by the way of water transportation, the condition of the canal system, the irrigation technology and so on. Therefore, the water use efficiency of the regional irrigation system can be improved by changing the water delivery method (from the channel to the pipeline) and the irrigation method (such as the dropper, sprinkler and other advanced irrigation technologies). For the study area, the results show that more than half of the water resources were lost during the process of canal water transport and irrigation. Therefore, adopting anti-seepage measures to reduce the leakage of the canal system and adopting advanced irrigation technology to reduce the amount of irrigation water will help to reduce the water footprint of crop production in this region.
In this study, we proposed an improved regional-scale method for calculating the crop production water footprint. This method was based on the hydrological model (SWAT model), combined the irrigation parameters of the irrigation area (water conveyance efficiency of the canal) and calculated the crop production water footprint.
The method provided a whole hydrological processes analysis for water use
during crop production, including water diversion, irrigation and precipitation,
field evapotranspiration and drainage. Therefore, the method contributed to
the establishment of a more comprehensive calculation of water consumption during the
crop growth period and a more precisely quantification of the crop production water
footprint. The method can be applied to calculate the crop production water
footprint at both the field and regional scale. In the HID, the main water
consumption occurs during the crop growth period; the canal water loss was
The regional climate, the condition of the irrigation system and the crop yield are the main factors that affect the water footprint of crop production. The area with higher crop yield per unit area, higher efficiency of irrigation water use, less irrigation water loss and closer to source of water has a lower crop production water footprint. Water loss during transportation increased with the increasing distance of the canals, and the farther away from the watershed inlets they were, the more water was lost; the values were higher in the east than they were in the west in the study area.
Due to special climatic conditions, crops in the Hetao irrigation district mainly depend on irrigation water in the production process. Overall, in the composition of the water footprint in the Hetao irrigation district, the blue water footprint accounts for more than 86 %. Therefore, applying water-saving irrigation technology, increasing the channel lining rate and reducing the loss of irrigation water are the main ways to adjust and control the water footprint of crop production in this area.
Based on the SWAT model, this paper analysed the utilization and consumption of water resources during crop production in irrigated areas, which provided a hydrological mechanism for quantifying the water footprint of crop production. However, the SWAT model does not consider the relation of groundwater flow between different sub basins. At the same time, the shallow groundwater evaporation is based on the soil as a medium, and directly into the atmosphere, the model cannot accurately quantify the recharge of shallow groundwater to soil water. Consequently, the SWAT model cannot accurately simulate the shallow groundwater consumption of crops. Therefore, combining the groundwater model, analysing the flow of water in the process of regional agricultural production and then quantifying the water footprint of crop production is the direction of further research.
The DEM data used in this study were obtained from the
Geospatial Data Cloud site, Computer Network Information Center, Chinese
Academy of Sciences (
The supplement related to this article is available online at:
PTW, SKS and YBW designed the study. XBL, YLY, XRG and JL did the literature search and data collection. XBL, SKS and YLY managed and analysed the data. XBL and SKS drew the figures and wrote the paper. All authors discussed and commented on the manuscript.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Integration of Earth observations and models for global water resource assessment”. It is not associated with a conference.
This work is jointly supported by the National Natural Science Foundation of China (51409218; 51609063), Innovative Talents Promotion Project in Shaanxi Province of China (2018KJXX-053), the Open Research Fund of the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin at the China Institute of Water Resources and Hydropower Research (IWHR-SKL-201601) and the Young Scholar Project of the Cyrus Tang Foundation. Edited by: Martina Floerke Reviewed by: two anonymous referees