Modeling the water-satisfied degree for production of the main food crops in China

Science of the Total Environment 547 (2016) 215–225

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Modeling the water-satisfied degree for production of the main food crops in China Guangming Yu a,b,⁎, Yumeng Yang b, Zhenfa Tu a,b, Yi Jie a,b, Qiwu Yu b, Xiaoyan Hu b, Hailong Yu b, Ruirui Zhou b, Xiaoxu Chen b, Hongzhi Wang a,b a b

Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province 430079, China College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• We develop a new SDCWR model to assess the satisfying degree of crop water requirement. • CWR for different crop types and growing seasons are assembled in the SDCWR model. • The SDCWR of the main food crops in China is analyzed. • Serious risk of the crop water demand exists in the food production in China. • The strategic measures of water resource management must be carefully chosen in China.

a r t i c l e

i n f o

Article history: Received 6 October 2015 Received in revised form 21 December 2015 Accepted 21 December 2015 Available online xxxx Editor: D. Barcelo Keywords: Food production Crop water requirements Water-satisfied degree Regional water balance Water resource management China

a b s t r a c t Water resources are one of the important factors that influence regional crop production and the food security of humans. Most traditional models of crop water demand analysis are built on the basis of a certain crop or macroscopic analysis, which neglect regional crop allocation and the difference of water demand in different crop growing periods. In this paper, a new assessing model, the satisfied degree of crop water requirement, is developed to assess the impacts of water resources on production of six main food crops in China. The six main food crops are spring wheat, winter wheat, corn, early season rice, middle-season rice and late rice. The results show that: (1) there are serious risks of water shortage in China, even in south China with its abundant precipitation; (2) the satisfied degree of crop water demand represents great temporal–spatial changes. On spatial distribution the risks are high in major bases of food production due to influences of cropping system and crop-combinations. Northwest China is a special interesting case. In seasonal fluctuation water shortage is severe in March and September. These risks seriously restrict food production in China. The results also show that the strategic measures of water resources management must be chosen carefully to deal with food security and regional sustainable development in China. © 2015 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author at: College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China. E-mail address: [email protected] (G. Yu).

http://dx.doi.org/10.1016/j.scitotenv.2015.12.105 0048-9697/© 2015 Elsevier B.V. All rights reserved.

Food security is becoming more and more important with the increase of population around the world (Kang et al., 2009; Ahmad et al., 2014). It is the foundation of sustainable human development. Food security is influenced by natural factors as well as man-made factors. One of the most important natural factors is the decrease of

216

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

water resource availability in Africa, Asia, and parts of Europe in the future (Jalilov et al., 2013; Lal, 2013; Zinyengere et al., 2014). The demands for water and the pressure on available water resources are increasing more and more due to the increase in population and human activities on a global scale, and they increase the challenge of attaining sustainable development and utilization of water resources (Graaf et al., 2014; Araújo et al., 2015). So water scarcity is one of the most concerning environmental issues the world faces today and will continue to face in the near future (FAO, 2013; Fracasso et al., 2015). Water is an essential production factor in agriculture (Kumar et al., 2015) and an important limiting factor for crop growth under the condition of water shortage (Niles et al., 2015). Water shortage may cause more than a 20–30% loss of the main crops in South Asia (Li et al., 2011) and Southeast Asia (Boling et al., 2010) and also seriously affects crop yields in South Africa (Magombeyi and Taigbenu, 2008). Because of their uneven spatial–temporal distribution, all kinds of water resources (e.g. storage, surface water, groundwater …) are needed to be used comprehensively in Asian and African countries (Dile et al., 2013; Kudo et al., 2014). The integrated utilization of water resources provides efficient water management, extends the irrigation period, and increases agricultural production (Liyantono et al., 2013). Moreover, in consideration of the complexity of water resource utilization, an integrated model of long-term water supply needs to be established for the research of regional water resources (Collet et al., 2013, 2015). Similarly, the scenario of water comprehensive utilization has been suggested based on “Multiple use systems” (Penning de Vries, 2007). The relationship between water condition and crop growth is described in many models in different regions (e.g. Aggarwal and Penning de Vries, 1989; Liu, 2009; Sarker et al., 2012), and the growing characteristics of crops are revealed in different moisture conditions (Liang et al., 2007). The CROPWAT model (Doorenbos and Pruitt, 1976; Doorenbos and Pruitt, 1977), the first and still commonly used model, considers crop water consumption and then defines the crop water requirement in terms of the bio-hydrological cycle and water balance in the crop habitat. It is suggested by the FAO (Allen et al., 1998) and used widely (Stancalie et al., 2010; Surendran et al., 2015; Luo et al., 2015). The demand for water varies with crops, climatic conditions, soil composition and soil moisture (Sari et al., 2013; Liu et al., 2013), and water demand models in different spatial scales are related to the spatial combinations of the crops (Heinemann et al., 2002). These models are the basis of water demand analysis in regional agricultural activities (Poussin et al., 2010; Riediger et al., 2016). The Crop Water Productivity (CWP) is suggested to quantitatively measure the impact of water on crop growth (Nana et al., 2014; Cao et al., 2015). It expresses the value or benefit derived from the use of water and includes essential aspects of water management (Singh et al., 2006), and is important for understanding water–food relationships (Liu et al., 2007b). This index is also important in the study of crop production potential (Yan and Wu, 2014). The guarantee rate of annual precipitation and the amount of annual water supply also can been used to express the crop water-satisfied degree (CWSD), but these indexes are too macroscopic to express the detail of relationships between water resources and crop water demand. Temporal aspects are crucial due to the high variability of water availability in some regions (Pfister and Bayer, 2014), and seasonal changes need to be considered in water supply–demand balance of an agricultural system (Salman et al., 2001). Thus, the remote sensing technique is used to monitor crop water demand in real time (Conrad et al., 2013). Combined with meteorological data, the Penman–Monteith formula can be used to calculate monthly water demand (Kawy and El-Magd, 2013). Many models of water production potential emphasize the relationships of crop yield with precipitation, soil moisture and crop transpiration, but ignore the effects form temporal changes of related factors and cropcombinations. Therefore, we are trying to develop a model to express the detail of water supply and demand in different growing seasons.

Most traditional models of water demand evaluation emphasize the macroscopic perspective or certain crops, but neglect regional cropcombinations and the differences of water demand in different growing periods. The purpose of this study is to develop a new assessment model, the satisfied degree of crop water requirement (SDCWR), to assess the impact of water resources on main food crop production in China. China has the largest food demand in the world, as the food demand in China is up to about 600 million t a year (Zhang et al., 2012), and its food production and supply–demand balance should influence on the world food market and food security greatly. Because the Chinese import quantum of food is large, the food price of the world food market is stable if Chinese food supply and balance is balanced, and vice versa. Similar to South and Southeast Asia, China's grain production also is restricted by water resource supply. The total amount of Chinese water resources is abundant, but its spatial–temporal pattern does not match the demands for food production. Not only is water supply insufficient, but also a mismatch of supply and demand is serious in terms of food production. About 81% of water resources are found in the south, while most of China's arable land (64%) is in the north (Khan et al., 2009). Water shortage is one of the important limiting factors to agricultural development and urbanization in China (Loeve et al., 2007). The South–North Water Transfer project was developed to overcome this barrier. This project can transport 40–50 km3 of water per year from the Changjiang (Yangtze River) basin to the North China Plain, benefiting 300–325 million people (Berkoff, 2003). Irrigated agriculture is another measure to cope with the lack of natural water resources. Irrigation systems are essential to minimize water losses and improve crop yield productivity to meet the growth of food demands. China has one of the world's largest irrigated areas (59.3 m ha), which is about half of China's cultivated land and produces about 75% of the grain harvest (Khan et al., 2009). Water is essential not only to maintain the livelihoods of human beings but also to sustain ecosystems (Curmi et al., 2013). Due to the diversity of water ecological services, the balance among functions of water utilization should be considered, such as the water budget for agricultural and aquatic ecosystems (Carrillo-Guerrero et al., 2013). The crisis of water resources for food production in China is serious in terms of the analysis of the supply–demand balance and has attracted wide attention (Zheng, 1994). Thus, China is selected as a case study of SDCWR model in this study. We try to answer three questions: (1) What is the significance of the SDCWR model for agricultural water management?; (2) What is the spatial–temporal distribution of the SDCWR?; and (3) Can the water demand of main food crops be met in China?

2. Theory and methods 2.1. The basic hypothesis The basic hypothesis of this study is: For a region or the entire globe, we cannot simply compare the amount of regional water resources and the total water demand of crop growth to measure the SDCWR. In terms of regional water supply, the amount of water resources changes with the seasons, and it is not completely used to meet the crop demand. Industrial, domestic and other water users also need water, so agricultural water use is only one parts of overall water use. In terms of crop water demand, crop water requirement changes with species and growing periods, and the amount of water demand for a crop is the sum of every growing period. In terms of regional crop water demand, it is the sum of water demand of all the crop species in a period, and changes with the combination of crop growing periods. So the SDCWR model should refer to indicators of the three above components. Based on regional water balance, allocation of water resources and difference of water demand in different growing periods, we build a SDCWR model.

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

averaged in every period. Therefore, the supply of agricultural water resources (Up2) is calculated for a period p as:

2.2. The SDCWR model 2.2.1. Generalized water resources and agricultural water resources Generalized water resources refer to all the water available from the precipitation that replenishes the natural water cycle and is available for artificial systems and natural systems. It can be classified as surface water, soil water and groundwater (Pei et al., 2007). For a region, the generalized water resources Q are the sum of the various types of water Qi:

Q¼

m X

Q i:

ð1Þ

U p2 ¼ γ p ðQ 1 þ Q 2 þ Q 3 Þ−

For a closed basin, only the runoffs and ground water could be used by humans in the traditional evaluation of water resources. So the actual water resources are calculated as follows: Q ¼ Q 1 þ Q 2 –Q 0

ð2Þ

Where Q is the total water resources; Q1 is river runoff; Q2 is the groundwater; Q0 is the repeated amount of mutual transformation between river runoff and groundwater. This formula is the standard of comprehensive planning and evaluation for water resources in China. Because generally the basin is not a closed system and Q0 can be ignored, Eq. (2) can be rewritten as: Q ¼ Q1 þ Q2 þ Q3

ð3Þ

Q3 is the other types of water resources (e. g. the waste water treatment). Assuming utilizations of water resources are sufficient and reasonable in an area, the supply–demand balance of regional water resources can be expressed as follows: n X Q i− U j ¼ 0:

i¼1

ð4Þ

ð8Þ

Z Wi ¼

T

ð9Þ

W t dt: 0

Wt is the crop water demand at a given time, T is the crop growing period. For any growing period (k [tk tk + 1]), the crop water requirement is given as: Z W ki ¼

t kþ1

ð10Þ

W t dt: tk

Supposing Rki as the water demand proportion of crop i in a growing period k, according to Eqs. (9) and (10), it can be expressed as Z Rki ¼

W ki Wi

T

W t dt ¼Z

:

0 t kþ1

ð11Þ

W t dt tk

So the water demands (W ki ) of crop i in any growing period k can also be expressed as follows: W ki ¼ Rki W i :

ð12Þ

The regional water demand (W pi ) of a crop in a period (such as a year, a month, or a time step) p is calculated as follows: W pi ¼

n X

λk W ki :

ð13Þ

k¼1

j¼1

th

Uj is the water demand for the j utilization of water resources. The regional water demand is divided into four major types in this study: industrial water U1, agricultural water U2, domestic water U3 and ecological water U4. The gross water requirement U is:

λk is the proportion of time of growing period k in period p to the whole growing period k; n is the number of growing periods in this period. For a period (p), the total water demand (W p) of n crops is given as: Wp ¼

U ¼ U1 þ U2 þ U3 þ U4:

ð5Þ

By substituting Eqs. (3) and (5) into Eq. (4), the supply of agricultural water resources (U2) is calculated as: U 2 ¼ Q 1 þ Q 2 þ Q 3 −U 1 −U 3 −U 4 :

ð6Þ

In general, regional water resources change with precipitation. Using the mean annual precipitation (Zy) and average period rainfall (Zp), we define γp as the proportion of water resources in p period accounts for annual water resources, γp ¼

1 ðU 1 þ U 3 þ U 4 Þ: 12

2.2.2. The water demand of the main food crops In theory, the water demand (Wi) of a crop i in the whole growing period is given as:

i¼1

m X

217

Zp : Zy

ð7Þ

Because industrial water U1 and domestic water U3 hardly change in different periods in a year and ecological water U4 is estimated on the basis of ecological types, we suppose these water demands can be

n X

W pi Si :

ð14Þ

i¼1

Si is the planting area of crop i, n is the number of main food crops in the area. 2.2.3. The water-satisfying degree (CWSD) of the main food crops In a region not all of the agricultural water resources are used in the production of the main food crops. Ninety percent of agricultural water resources are used for farm irrigation in China, including irrigation of non-food crops. This study defines the water-satisfying degree of the main crops (Aj) as: 8 0 > > > > S < 90%  U p2  i Aj ¼ S n > > > Wp > : 1

U p2 b0 0 b U p2 b W p

ð15Þ

U p2 ≥ W p

Sn is the cultivated area of all crops. Up2 b 0 is an extreme state that no water can be used for crop growth. For example, in drought or extreme

218

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

drought, domestic water and industrial water are prior users of water resources, no water can be used to meet the crop water demand after these prior users, even the U p2 value is negative according to Eq. (8). When Up2 b 0, it means that will take water from crops to meet the prior water users, and it is obviously impossible. So we set Aj = 0 When U p2 b 0. And Up2 ≥ W p is another extreme state such as in a long rainy season, flood period, and non-planting season. 2.3. The data sources 2.3.1. Study region This region in this study is mainland China (including Hainan province), not including Hong Kong, Macao, Taiwan and the islands of the South China Sea. The area is more than 9.3 × 108 km2. Seasonal distribution of regional precipitation is uneven with rainfalls of 9.4%, 84.4%, 4.6% and 1.6% in spring (March to May), summer (June to August), autumn (September to November) and winter (December to February) respectively. Regional food crops are diverse and different in every region. The main food crops are rice, wheat, corn, potato and soybean. Among them, rice, wheat and corn are implemented with wider planting areas and higher yields. 2.3.2. Data sources The data of water resources are extracted from The China Water Resources Bulletin (2003–2012) (MWR, 2013), including surface water, groundwater, industrial and living water, ecological water, etc. The data of crop growth, including the crop water requirements and the start and finish dates of each crop growing periods, are taken from field observation in agro meteorological stations and China's Major Agricultural Climate Resources Atlas (Cui et al., 1984). The data of the planting areas of food crops are extracted from The Statistical Data of New China's Agriculture in 60 Years (MA, 2009), The Statistical Yearbook of China (2003–2012) (NBS, 2013a), The Statistical Yearbook of China Countryside (2003–2012) (NBS, 2013b) and National Center for Agricultural Scientific Data Sharing. 2.4. Methods In the case study, calculating basis and flow chart of the WSDFC model is shown in Fig. 1. The first step is the determination of crop combination i (i = 1, 2, 3, …, n). In China spring wheat, winter wheat, corn, early rice, middle rice and late rice are principal foods, so we select this crop

combination (n = 6) for our study according to Chinese eating habits. Crop combination is one of the important influencing factors in the results of the case study which will be discussed in Section 5.3. The second step is to calculate the water demand (W pi ) of crop i in the typical month p. Our model can be used for annual, monthly and decadal time steps. To explain the effects of regional crop-combinations and water demand in different growing periods, we select four typical months to implement the case study. The main growing periods (k) of the six food crops are determined according to the observational data of crop growing state and periods from the agro meteorological observation stations in every region since 1980 and The ten-day reports of agro meteorology released by the China Meteorological Administration. Growth of the main food crops are fixed on different periods in a year, and precipitation and other water resources outside of these periods have less relationship with crop water demands. So the guaranteed rate of annual precipitation or the total amount of annual water supply use inappropriate indexes to evaluate the water-satisfying degree of food crops. Relatively, the water supply in growing periods can reflect the condition of agricultural water resources better than annual water supply. To assess WSD in growing periods of the main food crops, four months are selected as the typical months (p) according to the temporal combination of six main food crops, that is, March, June, September and December. The water requirements of the six food crops in four typical months is calculated in terms of the percentages of water required in every growing stage, intensity of the daily water requirement in the whole growing period, and the curve of water requirement of the crops (Cheng and Guo, 1993). Sorting these data, we acquired water demand of main food crops (Wi) (see Online Resources 1, Table S1) and the proportion of water demand in each growing period (Rki ) (Table 1) in China. Using the data of i, k, p, Wi, andRki , we calculate the water demand (W pi ) by Eq. (13). The third step is to calculate the water demand (W p) of the crop combination in typical month (p). The planting area (Si) of crop i (i = 1, 2, 3, …, 6) is extracted from the Statistical Yearbook, the ratio (Si/Sn) of Si to total planting area (Sn) in the region is also calculated. The results are shown in Online Resources 1 (Table S2) and spatial distribution shown in Fig. 2. Substituting Si and Wpi in Eq. (14), Wp can be obtained. The fourth step is to calculate the water supply (Up2) of crop growth in typical month (p). From the Statistical Yearbook and the Water Resources Bulletin, we extract the data of industrial water (U1), domestic water (U3), ecological water (U4), river runoff (Q1), groundwater (Q2), and the other types of water resources (Q3), and calculate their averages over the last decade. By the theory of water balance, the supply of agricultural water resources (U2) is calculated by Eq. (6), and the results are shown in Online Resources 1 (Table S3). By the agro meteorological and climate data, the mean monthly and annual precipitations over the last ten years are calculated, then the precipitation ratios (γp) in typical months can be calculated (see Online Resources 1, Table S4). With Ui and γp, Up2 can be calculated by Eq. (8). When the value of Up2 is negative, we set Up2 = 0. The last step is to calculate the SDCWR (Aj) for a region j. According to the above results, Aj can be calculated by Eq. (15). For the

Table 1 The ratios of water demands of main grain crops (Rki , %).

Fig. 1. The basis and flow chart of calculation in the case study.

Growing periods

Spring wheat

Winter wheat

Corn

Rice

Seedling stage Tillering stage Jointing stage Heading stage Filling stage Mature stage

18.6 21.7 16.9 23.4 12.4 7

5 14.4 19.2 25.3 24.3 11.8

9 / 31 33 22 5

16.7 27 22.3 9 8 17

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

219

Fig. 2. The spatial distribution of six main food crops in China (Digits of 1 to 31 are the numbers of regions as shown in Table 2. The histogram shows the ratio of plant area of every province to the whole country).

convenience of regional comparison, SDCWR can be graded into five ranks according to equidistant partition, namely [0,0.2), [0.2,0.4), [0.4,0.6), [0.6,0.8) and [0.8,1]. The high SDCWR (0.8 to 1.0) means that water resources (including water storage, water diversion, water lifts

and shallow freshwater, etc.) can meet the water demand of major food crops in these regions. By aforementioned statement, the SDCWR model can be generalized in Fig. 3.

Fig. 3. The SDCWR model.

220

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

3. Results 3.1. Characteristics of water supply and requirement of main grain crops in four typical months The water demand (Wp) and supply (Up2) of six food crops in typical months in China are shown in Table 2. The results in Table 2 are can be analyzed as follows. The maximum combination of water requirement of crop growing periods lasts from May to September in China. Growing periods of spring wheat, middle rice, late rice, and corn are included in this combination. This combination causes the peaks of agricultural water demands in China. So, there are two peaks of crop water demands in Table 2, Jun. and September. In the spatial pattern, the North China Plain, the Changjiang (Yangtze River) Basin, the Sichuan Basin, the Northeast China Plain, and the Pearl River Delta of China are the main regions of crop water demand and main bases of grain production. The crop water demands are obviously different between south and north China because of crop combinations and planting patterns. These differences can be generalized in two points: (1) As a whole, an equal area of crop water requirement in the north is less than in the south, because dry farming is dominant in the north and rice planting in the south. Water demand of the crop growing seasons in dry farming is less than that of rice planting. (2) In the typical months, the ratio of peak water demand to total crop water demand in the south is less than in the north. Because different crops can grow all year in the south, there is higher crop water demand than the north in winter. A number of crop water demands continue in winter in the south provinces (Table 2). Seasonal change of water resources in China is mostly influenced by two factors: the rainy season caused by monsoon and snow melt water. The rainy season influences change of water resources in east and

Table 2 Actual water requirement (Wp, 109 m3) and supply (Up2, 109 m3) of six main food crops in four typical months. Nos.

Province

March p

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

June

W

Up2

0.09 0.14 2.92 0.91 0.06 0.01 0.01 0.04 0.25 13.04 4.85 17.03 0.42 1.97 0.52 24.30 4.40 0.47 4.60 4.73 0.49 1.55 7.01 1.74 14.75 0.05 1.78 1.70 0.02 0.63 0.67

0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.03 0.23 5.02 0.52 2.73 1.61 3.49 1.97 1.05 1.58 4.61 1.68 2.21 0.07 0.00 0.00 0.17 0.00 0.04 0.00 0.72 0.00 0.96 4.07

p

September

W

Up2

1.52 2.06 46.17 14.49 17.07 21.58 22.20 24.81 0.57 20.35 22.78 14.50 12.18 34.10 9.01 35.15 13.11 41.86 21.11 23.04 3.09 3.78 11.08 5.72 9.37 0.27 25.57 19.97 1.96 6.04 19.44

1.29 1.37 11.31 2.39 8.26 5.28 5.53 13.88 3.14 24.56 11.06 10.37 7.24 10.06 6.52 9.74 8.67 16.85 29.27 19.85 1.74 2.88 7.89 5.12 8.65 0.85 2.90 6.00 0.94 5.29 11.40

p

December

W

Up2

Wp

Up2

2.64 3.40 52.74 20.31 20.11 21.23 39.56 50.31 1.45 27.61 30.69 29.62 11.01 31.25 12.73 34.90 23.32 37.22 24.86 24.94 3.95 9.84 43.30 25.68 25.34 0.19 16.72 6.81 0.47 4.07 5.57

0.75 0.85 9.00 3.70 5.36 1.66 1.77 7.50 1.05 14.15 10.14 4.21 0.65 0.66 2.17 10.72 4.49 1.85 7.57 5.42 1.85 2.40 6.57 1.39 5.70 0.51 5.83 7.34 0.68 6.95 5.51

0.09 0.12 2.90 0.90 0.06 0.01 0.01 0.04 0.08 4.35 4.85 5.11 0.10 0.09 0.30 8.43 1.73 0.21 0.02 0.03 0.00 1.16 5.25 1.30 4.21 0.05 1.77 0.72 0.02 0.12 0.67

0.00 0.04 1.14 0.22 3.20 1.76 2.84 10.54 7.30 35.30 6.55 11.40 7.23 10.50 11.04 5.96 7.04 15.93 12.00 10.22 1.30 1.55 0.00 3.10 2.63 0.00 0.61 1.28 0.00 1.11 26.72

southeast China, and the snow melt water in west, north and northeast China. These differences explain the higher SDCWR in west, north and south China in March and in the North China Plain, the Changjiang (Yangtze River) Basin, and south China in Jun. Data in Table 2 cannot be directly used to compare regional water demand and supply because the area of each province is different. 3.2. The SDCWR of six main food crops in China The WSDs of major food crops are calculated by Eq. (15) using the statistics of water supply, water use, and planting area of crops in every province in China (Fig. 4), and The SDCWR spatial–temporal change in China can be stated as follows. Overall, the SDCWR of food production in China is not satisfactory because of lower SDCWR in the main bases of gain production and the main growing seasons of food crops. One of typical cases is in September (Fig. 4) when the SDCWR of most regions is lower than 0.5. Another case is in March when the SDCWR in most of the main bases of food production is also lower 0.5, except in South China. There are acute water shortages in crop growing periods in China. The SDCWR in the south is relatively higher than in the north in crop growing seasons such as in June (Fig. 4) because of the monsoon rainy season. In March, the areas of low SDCWR (0.0 to 0.2) are widely distributed in Northeast, North and Southwest China, accounting for about 33% of the area of China. The growth of food crops is threatened with drought in these regions. A “water shortage zone” is stretched from Jilin and Liaoning Provinces to Shaanxi, Sichuan and then to Yunnan province. Most of the water shortage is in North China. The area for which agricultural water can meet the water requirement of crops is about half of China, distributed mainly in South China, because these regions have much more precipitation and small percentage and area of wheat planting. The higher SDCWR is in the Xinjiang region because the planting area of food crops is small and it has the supplement of melting snow, which can alleviate the pressures of water shortage in the agriculture oasis. In June the major food crops that are growing are spring wheat, corn, early rice, and middle rice. The area of medium SDCWR dominates nearly half of China. The cultivated areas of crops play a crucial role in regional water demand because of the few differences of water demand in this month. The SDCWR in Shaanxi province with 0.11 is the lowest value in China because the cultivated area of wheat, corn and middleseason rice is larger, and water supply is not enough, only about 2.9 × 108 m3. The high SDCWR regions are scattered mainly in Tibet, Yunnan, and Guangdong and Guangxi provinces. The area of Tibet is large. There is a small planting proportion of crops, and water supply can meet the demands of agricultural water use. The abundant rainfall results in the higher SDCWR in Guangdong and Guangxi provinces. September is dominated by three SDCWR grades with even distribution, i.e. [0, 0.2), [0.2, 0.4) and [0.8, 1]. The minimum SDCWR is 0.02 in Jiangxi Province, where the growths of crops are faced with serious water shortages. In recent years, the average water supply of Jiangxi province is only about 0.6647 × 108 m3, which is lower than Shandong and Henan province in north China (about 1.0 × 108 m3), but the crop water demand in Jiangxi is larger than other provinces. The area of late rice is more than 1.34 × 106 hm3 in Jiangxi province, and in September late rice has the peak of water demand, accounting for about 25% of the whole growing phase. Therefore, main food crops in Jiangxi province suffer from the most serious water shortage in China. Compared with other months, the crop water supply is not enough in south China while the water shortage is mitigated somewhat in north China except in the northeast areas. The reason is that winter wheat is the main crop in north China, and it is just in the seeding time, so water demand is small. However, the water demand of rice is high in this month in south China. In December the SDCWR is generally higher on the country scale, but the differences are obvious. High SDCWR amounts on about 62% of total

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

221

Fig. 4. Grading results of water-satisfying degree in China.

land in China, but low SDCWR accounts for only about 16%. Winter wheat is only food crop in this month; its water demand is about 5% of the whole growing period in December in most areas, and in some regions it is just 2%. Therefore, the total water demand depends on the cultivated area of winter wheat. According to statistics, the Henan, Shandong, Shanxi, Hebei, Shaanxi and Sichuan provinces have cultivated winter wheat with area of more than 0.67 × 106 hm3 in the last 10 years. The SDCWR in Hebei, Sichuan and Shaanxi provinces are the lowest, because the large-area planting of winter wheat results in high demand of agricultural water use. The low SDCWR in Tianjin, Beijing, Qinghai and Tibet is related to winter water shortage. There are higher SDCWRs in Ningxia, Inner Mongolia, and Northeast China in December because of no food crop growing in these areas at the time. For water resources management, in high SDCWR regions, it could be considered to increase the water-intensity crops appropriately in these months, and should also prevent floods to avoid unnecessary loss. In low SDCWR regions, we can adjust the planting structures in five-year period land use planning and reduce the total monthly water demand to ensure food production.

Counting the raster data of Fig. 4, the relationships between the SDCWR of food crop and its area in China in four typical months are shown in Fig. 5. Fig. 5 shown that over 50% of China is with the SDCWR of higher 0.8 in December, and near 50% in March. It is a pity that these periods are not the growing season of main food crop. At this time, there are not planting seasons of food crop in the north and planting area of food crop is small in the south. On the contrary, the SDCWR with area of 50% is lower than 0.5 in every typical month. It reflects real experiences in water demand and supply of food production in China. It is most evident in June and September. These two months are the main growing periods of food crop, so these results reflect the shortage of agricultural water in China. 4. Discussions 4.1. Explanations of the model and results Different from the existing assessing method (Liu, 2009; Poussin et al., 2010; Sarker et al., 2012), our model emphasizes two mechanisms based on the theory of regional water balance: one is the analysis of the

Fig. 5. Area ratio in different grades of the SDCWR in China.

222

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

water requirement of crop combinations, and another is the analysis of the SDCWR in terms of crop water requirements in growing periods. The analysis of the water requirement of crop combinations is emphasized because agricultural water requirements are changed with different spatiotemporal combinations of crops. By the SDCWR model, different crop combinations can be selected to meet the special purposes of water resources with the SDCWR. In this study, our aim is to assess the SDCWR of the main food crops in terms of food security and water resource management in China. By the Chinese eating habits, six main food crops with their different spatiotemporal planting combinations are used in this study to investigate the SDCWR of food production in China. The results should be much closer to the reality. The planting area and spatial distribution of the crops are the average value of the last decade. However, it can be seen from Eq. (15) that changes of planting patterns (Si/Sn) would affect the water requirements of regional agriculture, and the agricultural water requirements would be much bigger if consideration is given to other crops such as cotton, sugar beet, oil crops, and so on (Huang et al., 2012; Shen et al., 2013). In the future other crop combinations can be selected with the SDCWR model to meet different aims of the SDCWR assessment such as agricultural water management, agricultural risk assessment, irrigation planning, and so on. The allocation of crop water requirements in different growing periods is emphasized because it can express accurately that water requirements of main crop combinations change with time in food production. In many previous research reports (Singh et al., 2006; Liu et al., 2007b; Yan and Wu, 2014), the crop water requirements are assumed as a constant, and only the equation of regional water balance is use to calculate the SDCWR of crop growth. The spatial and temporal variation of the simulated seasonal water requirement, water consumption and water deficit of wheat and sugar beets are investigated in some studies (Supit et al., 2010). The results show that the regional trend patterns in actual crop water consumption and water deficit are less distinct than found for the crop water requirements. In fact, the water requirements change with crop growing periods. The water requirement in the same region and period is the function of water requirements of different crops and their growing periods, which expresses the seasonal changes of the water requirements of crops in regional food production. In spite of abundant rainfall in the region, it also cannot meet the water demands of crop growth if the rainfall does not match the growing period, which has been demonstrated by this study. In our model the crop water requirement is allocated to different growing periods, and the regional water requirements of crop combinations are calculated according to the combinations of crop growing periods in the same region and period. In this way analysis of regional water-satisfied degree of crop growth is more objective and exact than that of simply calculating the macroscopic water balance. In this study the results show that the space-time distribution of water resources severely restricts the production of the main food crops in China. The SDCWR spatial–temporal features are as follows. (1) There are serious water deficits in the seeding time, the tillering stage, and the filling stage of the main food crops, which impacts growth and yield. The Ai distributions (Fig. 3) show that the water resources cannot meet the crops' water demands in all major food-producing bases of China in March and September. (2) In terms of the production of food crops, the water crisis also exists in south China with plentiful rainfall, especially in March and September, but alleviates in June because of the rainy season. The water resources can sufficiently meet water requirements in December due to lack of large planting area of the main food crops during this time. (3) The agricultural available water cannot meet the crops water requirements widely in major food-producing bases in China. The same is true as in India, “the real food security and water management challenge lies in the mismatch between water availability and agricultural water demand: high demands occur in water scarcity but agriculturally prosperous regions and low demands in naturally water-abundant but agriculturally backward

regions” (Kumar et al., 2012). This situation will be difficult to change in the near future. Agricultural water demand in south China will decrease generally, and the cropland soil-moisture deficit would decrease due to climate change. However, in north China, agricultural water demand will increase, and the soil-moisture deficit will increase generally (Tao et al., 2003). Results of this study also mean that Chinese food production will face a series of potential crises in terms of water resources. Therefore, the problems of water management in major food-producing areas should be given much more attention in China. The results also reveal a higher SDCWR in Xinjiang Uygur Autonomous Region and Ningxia Hui Autonomous Region (Fig. 3). Northwest China belongs in the arid area, so the higher SDCWR is seemingly unconvincing. Yet, it is true and can be explained. In spite of the widespread area of arid climate in northwest China, plenty of water resources exit in some local areas which have become important agricultural regions, such as in Hexi Corridor and many oases. In terms of Xinjiang, three factors influence the SDCWR. First, it is a sparsely populated area, the amount of industrial and domestic water is small, and the amount of available water for agriculture is larger. Second, animal husbandry accounts for more than 30% of agricultural production, and even reaches to 50% in some regions. Therefore, the percentage of farm irrigation (90%) is overestimated in calculating the water consumption of the main food crops. Third, the planting area and types of crops are relatively less, and the total amount of water requirements of food crops is also small. Ningxia belongs to sub-humid, semi-arid and arid climates in the temperate zone from south to north, with rainfall about 1.495 × 1011 m3 or 289 mm. Additionally, every year about 4 × 1010 m3 of foreign water of the Huanghe (Yellow River) is allocated to Ningxia, which provides an advantageous condition for agricultural production. In fact, the Huanghe (Yellow River) irrigation area, the Chengdu Plain, the Guanzhong Plain, the Hexi Corridor and the Ili River Valley are known as “the five big granaries of West China” and the important commodity grain bases in Northwest China. This area is also known as “the irrigated agricultural region with development potential” and a “rare region for agriculture” by economists (Wu and Zhang, 2003). 4.2. Water-satisfied degree, agricultural water management and food security The SDCWR model mainly is used for evaluating the inhibiting degree of water resources on the crop growth in a region. Apparently, a low SDCWR would influence the yield of crops, and even results in death of crops in some serious cases. In this way the WSD can be used for assessing potentiality of crop production. The SDCWR model, developed in this study, aims to manage agricultural water. Shortage of agricultural water resources is a common phenomenon around the world (Milano et al., 2013). The adjustment of irrigation practice is one of the important measures to cope with this shortage. Irrigated cropland plays a crucial role for agricultural production but also has been identified as a major user of global freshwater resources (Schaldach et al., 2012). To increase the yield of crops, irrigation plays an important role in food production in China (Liu et al., 2007a). Agricultural irrigation should be arranged reasonably in terms of the SDCWR spatial–temporal features with different crop combinations in China. Application of deficit irrigation can help in increasing water productivity in water limited regions (Karrou and Oweis, 2012) for various crops without causing severe yield reductions (Geerts and Raes, 2009). The strategy of deficit irrigation is propitious to fully utilize limited water resources (Consoli et al., 2014). The SDCWR model can test the WSD with different crop combinations and is a feasible tool to supply–demand analysis of agricultural water. However, the prerequisite of farm irrigation is a source of irrigation water. Under current conditions conversion from rainfed to irrigated cropland is most likely in eastern China, northern Africa, and parts of the Mediterranean region (Neumann et al., 2011). In the 2030s, drought

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

stress would overstep the irrigation capacity of current supplies in northern and western China, while drought will remain at baseline climate levels in the central, eastern and southern regions (Zhang et al., 2013). To solve the water issues in northern China, one of the magnificent countermeasures is the South–North Water Diversion Project in China. In theory, with more precipitation water resources are abundant in south China. So, it seems we can take the superfluous water to offset the water deficiency in north China on a macro scale. Yet, we find in this study that growth of the main food crops all face the risk of water shortage in the growing periods, even in south China with abundant rainfall. In terms of water resource management, the results of this study mean that maybe the South–North Water Diversion will influence the food crop growth in the South. Therefore the effects of the South–North Water Diversion Project on future production of food crops in China are still to be determined. How much water can be transported from south to north China? Maybe the SDCWR model with different crop combinations can answer this question in terms of crop farming and food security. Global grain production has increased dramatically during the past fifty years (Neumann et al., 2010). In the view of food security, food trade may be another way to solve the regional water shortage. One interesting opinion is that the water footprint of a crop mainly depends on agricultural management rather than the regional climate and its variation (Sun et al., 2013). Never the less, calculation of the crop water footprint is related to food trade. China is a country with a large population, and it cannot overly depend on food trade to deal with food security. Consequently, it is right to solve the food security problem by adopting suitable measures of water management based on the results of the SDCWR model in China. 4.3. Analysis on uncertainty Bias is inevitable in scientific research and mainly comes from the knowledge structure and research perspective of researchers (Yu et al., 2010). The theme of this study is related to a complicated human-earth system (Yu et al., 2013). The intricate driving factors and evolution processes in this system increases the difficulties of model analysis (Yu et al., 2014a, 2014b) which also brings some uncertainties of research results. This study has four uncertain factors. (1) Scenario of the crop combination in the case study. For the SDCWR model, crop combination {i (i = 1, 2, 3, …, n)} is optional, but as a case study, we select n = 6. Different crop combinations will obtain different results by the SDCWR model. In this study six main food crops were selected in terms of Chinese eating habits, but other crops not included in this scenario. In theory these six crops could represent the main food crops in China, accounting for about 85% of all food crops (Zhang et al., 2012), but in fact there also are some “coarse cereals” with small planting areas and yield. The economic value of water at the farm level and regionally aggregated levels are similar, but the variability and distributional effects of each scenario are affected by aggregation (Medellín-Azuara et al., 2010). The amount of water required and risk of water shortage in agricultural production would be much larger if the “coarse cereals” are counted. (2) The hydro-meteorological data and crop planting areas in this paper are the average values in the last decade, and climate change is not considered. According to China Water Resources Bulletin (MWR, 2013), since 2003 the amount of water use has tended to increase slightly in China on the whole. Industrial and domestic water increases regularly, while agricultural water fluctuates due to the influence of climate and actual irrigation area (Aurbacher et al., 2013; Mushtaq et al., 2013). The proportion of industrial and domestic water in the total water used has increased gradually, and the amount of agricultural water has decreased. In addition, the planting area of the main food

223

crops in some regions declined continuously in the last decade (NBS, 2013b). The results express the SDCWR of the main food crops in a large time scale and do not simulate the SDCWR in future. The uncertainty of these results also comes from the potential impacts of agricultural expansion and climate changes on irrigation water requirements, however these influences also are with uncertainty (Maeda et al., 2011). (3) The influences of water management measures are not analyzed in the case study. The inter-basin water diversion significantly influences regional water balance and evolution of the humanearth system in China (Jiang et al., 2012). Their effects on agricultural production are still to be studied further. In terms of the model, the complicated supply–demand relationship in regional water balance and the long-term water supply capacity should be emphasized in future studies. (4) Uncertainties also exist in the analysis of water resources and demand variability. Water resources will change with climate change. How much and in what ways does climate change? How might climate change influence regional water resources? How will climate change affect crop water needs? These problems still need to be investigated further. In this study the SDCWR model does not refer to climate change, averages of annual and monthly rainfalls are used to calculate the seasonal variations of water resources. This calculation is not reported by references, but it is logically agreed upon with the truth of seasonal variations of water resources in China. From Fig. 2 and Eq. (8), you can see that we give priority to domestic, industrial and ecological water users in the water demand calculation, this is stated in the Methods (the fourth step). We believe that the seasonal variation of water demands is small to these prior users, and adopt the monthly average in the SDCWR model and case study.

5. Conclusions Based on theory of regional water balance and allocation, and water demand in crop growing periods, we developed a new SDCWR evaluation model for water-satisfied degree of the main food crops. Combined with the relevant data in the last decade, the SDCWR in production of the main food crops in China is calculated. The results show: (1) There is a serious risk in water demand of the main food crops in China and this risk is universal, even in south China with abundant rainfall. (2) The spatial–temporal variation in water demand of the main food crops is large in China. For spatial distribution, the risks of the main food-producing bases are great because of the farming systems and crop combinations, but the northwest region is an interesting special case. For seasonal variation, water shortage is serous in March and September. This is a serious restriction for China's grain production. (3) Results of this study also show that Chinese food production will face a series of potential crises. The strategic measures of water resource and food safety management should be chosen cautiously and appropriately in China.

Acknowledgments This work is supported by the Natural Scientific Foundation of China (No. 40771088). The authors thank Prof. Dr. Norman Moline and Dr. Randy Ferrin for editorial assistance and express our thanks to the anonymous reviewers and the Editor for useful suggestions for improvement. Any errors and views expressed in this paper are the sole responsibility of the authors.

224

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225

Map. KMZ file containing the Google map of the most important areas described article.

Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2015.12.105. These data include the basic data cited in Section 2.4, an interactive plot, and the Google map of the most important areas described in this article. References Aggarwal, P.K., Penning de Vries, F.W.T., 1989. Potential and water-limited wheat yields in rice based cropping systems in Southeast Asia. Agric. Syst. 30 (1), 49–69. Ahmad, M., Masih, I., Giordano, M., 2014. Constraints and opportunities for water savings and increasing productivity through resource conservation technologies in Pakistan. Agric. Ecosyst. Environ. 187, 106–115. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. Rome, FAO. Araújo, R.S., Alves, M.G., C Melo, M.T., MP, C.Z., Mendes, M.P., Júnior G.C.S., 2015. Water resource management: a comparative evaluation of Brazil, Rio de Janeiro, the European Union, and Portugal. Sci. Total Environ. 511, 815–828. Aurbacher, J., Parker, P.S., Sanchez, G.A.C., Steinbach, J., Reinmuth, E., Ingwersen, J., Dabbert, S., 2013. Influence of climate change on short-term management of field crops—a modelling approach. Agric. Syst. 119, 44–57. Berkoff, J., 2003. China: the South–North Water Transfer Project—is it justified? Water Policy 5, 1–28. Boling, A.A., Tuong, T.P., van Keulen, H., Bouman, B.A.M., Sugand, H., Spiertz, J.H.J., 2010. Yield gap of rainfed rice in farmers' fields in Central Java, Indonesia. Agric. Syst. 103, 307–315. Cao, X., Wang, Y., Wu, P., Zhao, X., Wang, J., 2015. An evaluation of the water utilization and grain production of irrigated and rain-fed croplands in China. Sci. Total Environ. 529, 10–20. Carrillo-Guerrero, Y., Glenn, E.P., Hinojosa-Huerta, O., 2013. Water budget for agricultural and aquatic ecosystems in the delta of the Colorado River, Mexico: implications for obtaining water for the environment. Ecol. Eng. 59, 41–51. Cheng, Y., Guo, G., 1993. Study on Chinese Main Crop Water Requirement Graph. China Agricultural Science and Technology Press, Beijing, pp. 46–50. Collet, L., Ruelland, D., Borrell-Estupina, V., Dezetter, A., Servat, E., 2013. Integrated modelling to assess long-term water supply capacity of a meso-scale Mediterranean catchment. Sci. Total Environ. 461–462, 528–540. Collet, L., Ruelland, D., Borrell-Estupina, V., Dezetter, A., Servat, E., 2015. Water supply sustainability and adaptation strategies under anthropogenic and climatic changes of a meso-scale Mediterranean catchment. Sci. Total Environ. 536, 589–602. Conrad, C., Rahmann, M., Machwitz, M., Stulina, G., Paeth, H., Dech, S., 2013. Satellite based calculation of spatially distributed crop water requirements for cotton and wheat cultivation in Fergana Valley, Uzbekistan. Glob. Planet. Chang. 110, 88–98. Consoli, S., Stagno, F., Roccuzzo, G., Cirelli, G.L., Intrigliolo, F., 2014. Sustainable management of limited water resources in a young orange orchard. Agric. Water Manag. 132, 60–68. Cui, D., Liu, H., Min, J., 1984. Climate Resources Atlas of Main Crops in China. Meteorology Press, Beijing. Curmi, E., Richards, K., Fenner, R., Allwood, J., Kopec, G.M., Bajzelj, B., 2013. An integrated representation of the services provided by global water resources. J. Environ. Manag. 129, 456–462. Dile, Y.T., Karlberg, L., Temesgen, M., Rockström, J., 2013. The role of water harvesting to achieve sustainable agricultural intensification and resilience against water related shocks in sub-Saharan Africa. Agric. Ecosyst. Environ. 181, 69–79. Doorenbos, J., Pruitt, W.O., 1976. Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper 24, second ed. Food and Agriculture Organization of the United Nation (Rome 156 pp.). Doorenbos, J., Pruitt, W.O., 1977. FAO Irrigation and Drainage Paper 24. Food and Agriculture Organization of the United Nation ((Rev.) Rome, 156 pp.). FAO, 2013. FAO Statistical Yearbook 2013—World Food and Agriculture. Food and Agriculture Organization of the United Nation, Rome. Fracasso, A., et al., 2015. Determinants of virtual water flows in the Mediterranean. Sci. Total Environ. http://dx.doi.org/10.1016/j.scitotenv.2015.02.059 Geerts, S., Raes, D., 2009. Deficit irrigation as an on-farm strategy to maximize crop water productivity. Agric. Water Manag. 96, 1275–1284. Graaf, I.E.M.d., van Beek, L.P.H., Wada, Y., Bierkens, M.F.P., 2014. Dynamic attribution of global water demand to surface water and groundwater resources: effects of abstractions and return flows on river discharges. Adv. Water Resour. 64, 21–33. Heinemann, A.B., Hoogenboom, G., de Faria, R.T., 2002. Determination of spatial water requirements at county and regional levels using crop models and GIS: an example for the State of Parana, Brazil. Agric. Water Manag. 52, 177–196. Huang, J., Ridoutt, B.G., Xu, C., Zhang, H., Chen, F., 2012. Cropping pattern modifications change water resource demands in the Beijing Metropolitan Area. J. Integr. Agric. 11 (11), 1914–1923.

Jalilov, S.-M., Amer, S.A., Ward, F.A., 2013. Water, food, and energy security: an elusive search for balance in central Asia. Water Resour. Manag. 27, 3959–3979. Jiang, Y., LuoY, P.S., Wang, W., Jiao, X., 2012. Agricultural water transfers in China: current issues and perspectives. Procedia Eng. 28, 263–267. Kang, Y., Khan, S., Ma, X., 2009. Climate change impacts on crop yield, crop water productivity and food security — a review. Prog. Nat. Sci. 19, 1665–1674. Karrou, M., Oweis, T., 2012. Water and land productivities of wheat and food legumes with deficit supplemental irrigation in a Mediterranean environment. Agric. Water Manag. 107, 94–103. Kawy, W.A.A., El-Magd, I.H.A., 2013. Assessing crop water requirements on the bases of land suitability of the soils South El Farafra Oasis, Western Desert, Egypt. Arab. J. Geosci. 6, 2313–2328. Khan, S., Hanjra, M.A., Mu, J., 2009. Water management and crop production for food security in China: a review. Agric. Water Manag. 96, 349–360. Kudo, Y., Noborio, K., Shimoozono, N., Kurihara, R., 2014. The effective water management practice for mitigating greenhouse gas emissions and maintaining rice yield in central Japan. Agric. Ecosyst. Environ. 186, 77–85. Kumar, M.D., Sivamohan, M.V.K., Narayanamoorthy, A., 2012. The food security challenge of the food–land–water nexus in India. Food Secur. 4, 539–556. Kumar, V., et al., 2015. Adaptation strategies for water supply management in a drought prone Mediterranean river basin: application of outranking method. Sci. Total Environ. http://dx.doi.org/10.1016/j.scitotenv.2015.06.062. Lal, R., 2013. Food security in a changing climate. Ecohydrol. Hydrobiol. 13, 8–21. Li, X., Waddington, S.R., Dixon, J., Joshi, A.K., de Vicente, M.C., 2011. The relative importance of drought and other water-related constraints for major food crops in South Asian farming systems. Food Secur. 3, 19–33. Liang, W., Lu, H., Wang, G., Connor, D.J., Rimmington, G.M., 2007. A simulation model-assisted study on water and nitrogen dynamics and their effects on crop performance in the wheat–maize system I: the model. Front. Agric. China 1 (2), 155–165. Liu, J., 2009. A GIS-based tool for modelling large-scale crop–water relations. Environ. Model. Softw. 24, 411–422. Liu, J., Wiberg, D., Zehnder, A.J.B., Yang, H., 2007a. Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China. Irrig. Sci. 26, 21–33. Liu, J., Williams, J.R., Zehnder, A.J.B., Yang, H., 2007b. GEPIC — modelling wheat yield and crop water productivity with high resolution on a global scale. Agric. Syst. 94, 478–493. Liu, Z., Yao, Z., Yu, C., Zhong, Z., 2013. Assessing crop water demand and deficit for the growth of spring highland barley in Tibet, China. J. Integr. Agric. 12 (3), 541–551. Liyantono, K.T., Kuroda, H., Yoshida, K., 2013. GIS analysis of conjunctive water resource use in Nganjuk district, east Java, Indonesia. Paddy Water Environ. 11, 193–205. Loeve, R., Dong, B., Hong, L., Chen, C., Zhang, S., Barker, R., 2007. Transferring water from irrigation to higher valued uses: a case study of the Zhanghe irrigation system in China. Paddy Water Environ. 5 (4), 263–269. Luo, X., Xia, J., Yang, H., 2015. Modeling water requirements of major crops and their responses to climate change in the North China Plain. Environ. Earth Sci. 74, 3531–3541. MA (Ministry of Agriculture, the People's Republic of China), 2009a. Agricultural Statistics Of Recent 60 Years in China. China Agriculture Press, Beijing. Maeda, E.E., Pellikka, P.K.E., Clark, B.J.F., Siljander, M., 2011. Prospective changes in irrigation water requirements caused by agricultural expansion and climate changes in the eastern arc mountains of Kenya. J. Environ. Manag. 92, 982–993. Magombeyi, M.S., Taigbenu, A.E., 2008. Crop yield risk analysis and mitigation of smallholder farmers at quaternary catchment level: case study of B72A in Olifants river basin, South Africa. Phys. Chem. Earth 33, 744–756. Medellín-Azuara, J., Harou, J.J., Howitt, R.E., 2010. Estimating economic value of agricultural water under changing conditions and the effects of spatial aggregation. Sci. Total Environ. 408, 5639–5648. Milano, M., Ruelland, D., Dezetter, A., Fabre, J., Ardoin-Bardin, S., Servat, E., 2013. Modeling the current and future capacity of water resources to meet water demands in the Ebro basin. J. Hydrol. 500, 114–126. Mushtaq, S., Maraseni, T.N., Reardon-Smith, K., 2013. Climate change and water security: estimating the greenhouse gas costs of achieving water security through investments in modern irrigation technology. Agric. Syst. 117, 78–89. MWR (Ministry of Water Resources, People's Republic of China), 2013n. China Water Resources Bulletin (2003–2012). China WaterPower Press, Beijing. Nana, E., et al., 2014. A model for crop yield and water footprint assessment: study of maize in the Po valley. Agric. Syst. 127, 139–149. NBS (National Bureau of Statistics, People's Republic of China), 2013h. China Statistical Yearbook 2001. China Statistics Press, Beijing, p. 2001. NBS (NationalBureau Statistics, People's Republic of China), 2013). China Rural Statistical Yearbook (1987–2002). China Statistics Press, Beijing. Neumann, K., Verburg, P.H., Stehfest, E., Müller, C., 2010. The yield gap of global grain production: a spatial analysis. Agric. Syst. 103, 316–326. Neumann, K., Stehfest, E., Verburg, P.H., Siebert, S., Müller, C., Veldkamp, T., 2011. Exploring global irrigation patterns: a multilevel modelling approach. Agric. Syst. 104, 703–713. Niles, M.T., Lubell, M., Brown, M., 2015. How limiting factors drive agricultural adaptation to climate change. Agric. Ecosyst. Environ. 200, 178–185. Pei, Y., Zhao, Y., Zhang, J., 2007. Study on rational deployment of generalized water resources: I. Theory. J. Hydraul. Eng. 38 (1), 1–7. Penning de Vries, F.W.T., 2007. Learning alliances for the broad implementation of an integrated approach to multiple sources, multiple uses and multiple users of water. Water Resour. Manag. 21, 79–95. Pfister, S., Bayer, P., 2014. Monthly water stress: spatially and temporally explicit consumptive water footprint of global crop production. J. Clean. Prod. 73, 52–62.

G. Yu et al. / Science of the Total Environment 547 (2016) 215–225 Poussin, J.C., Pouget, J.C., D'hont, R.L., 2010. ZonAgri: a modeling environment to explore agricultural activities and water demands on a regional scale. Land Use Policy 27, 600–611. Riediger, J., Breckling, B., Svoboda, N., Schröder, W., 2016. Modelling regional variability of irrigation requirements due to climate change in northern Germany. Sci. Total Environ. 541, 329–340. Salman, A.Z., Al-Kaarablieh, E.K., Fisher, F.M., 2001. An inter-seasonal agricultural water allocation system (SAWAS). Agric. Syst. 68, 233–252. Sari, D.K., Ismullah, I.H., Sulasdi, W.N., Harto, A.B., 2013. Estimation of water consumption of lowland rice in tropical area based on heterogeneous cropping calendar using remote sensing technology. Procedia Environ. Sci. 17, 298–307. Sarker, A.R., Alam, K., Gow, J., 2012. Exploring the relationship between climate change and rice yield in Bangladesh: an analysis of time series data. Agric. Syst. 112, 11–16. Schaldach, R., Koch, J., der Beek, T.A., Kynast, E., Flörke, M., 2012. Current and future irrigation water requirements in pan-Europe: an integrated analysis of socio-economic and climate scenarios. Glob. Planet. Chang. 94-95, 33–45. Shen, Y., Li, S., Chen, Y., Qi, Y., Zhang, S., 2013. Estimation of regional irrigation water requirement and water supply risk in the arid region of northwestern China 1989–2010. Agric. Water Manag. 128, 55–64. Singh, R., van Dam, J.C., Feddes, R.A., 2006. Water productivity analysis of irrigated crops in Sirsa district, India. Agric. Water Manage. 82, 253–278. Stancalie, G., Marica, A., Toulios, L., 2010. Using earth observation data and CROPWAT model to estimate the actual crop evapotranspiration. Phys. Chem. Earth 35, 25–30. Sun, S., Wu, P., Wang, Y., Zhao, X., Liu, J., Zhang, X., 2013. The impacts of inter-annual climate variability and agricultural inputs on water footprint of crop production in an irrigation district of China. Sci. Total Environ. 444, 498–507. Supit, I., van Diepen, C.A., Boogaard, H.L., Ludwig, F., Baruth, B., 2010. Trend analysis of the water requirements, consumption and deficit of field crops in Europe. Agric. For. Meteorol. 150, 77–88. Surendran, U., Sushanth, C.M., Mammen, G., Joseph, E.J., 2015. Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: a case study in Palakkad district of humid tropical Kerala, India. Aquat. Procedia 4, 1211–1219.

225

Tao, F., Yokozawa, M., Hayashi, Y., Lin, E., 2003. Future climate change, the agricultural water cycle, and agricultural production in China. Agric. Ecosyst. Environ. 95, 203–215. Wu, W., Zhang, W., 2003. Present land use and cover patterns and their development potential in North Ningxia. J. Geogr. Sci. 13 (1), 54–62. Yan, N., Wu, B., 2014. Integrated spatial–temporal analysis of crop water productivity of winter wheat in Hai Basin. Agric. Water Manag. 133, 24–33. Yu, G., Feng, J., Che, Y., Lin, X., Hu, L., Yang, S., 2010. The identification and assessment of ecological risks for land consolidation based on the anticipation of ecosystem stabilization: a case study in Hubei Province, China. Land Use Policy 27 (2), 293–303. Yu, G., Yu, Q., Hu, L., Zhang, S., Fu, T., Zhou, X., He, X., Liu, Y., Wang, S., Jia, H., 2013. Ecosystem health assessment based on analysis of a land use database. Appl. Geogr. 44, 154–164. Yu, G., Wang, S., Yu, Q., Wu, L., Fan, Y., He, X., Zhou, X., Jia, H., Zhang, S., Tian, X., 2014a. The regional limit of flood-bearing capability: a theoretical model and approaches. Water Resour. Manag. 28, 1921–1936. Yu, G., Zhang, S., Yu, Q., Fan, Y., Zeng, Q., Wu, L., Zhou, R., Nan, N., Zhao, P., 2014b. Assessing ecological security at the watershed scale based on RS/GIS: a case study from the Hanjiang River Basin. Stochastic Environ. Res. Risk Assess. 28 (2), 307–318. Zhang, Y., Li, Z., Li, Z., Xu, X., 2012. China grains demand forecast analysis based on China economy-wide multi-market model. Food Nutr. China 18 (2), 40–45. Zhang, T., Simelton, E., Huang, Y., Shi, Y., 2013. A Bayesian assessment of the current irrigation water supplies capacity under projected droughts for the 2030s in China. Agric. For. Meteorol. 178-179, 56–65. Zheng, Y., 1994. Water conference in Stockholm oriented to the 21st century. Resour. Sci. 16 (5), 78–79. Zinyengere, N., Crespo, O., Hachigonta, S., Tadross, M., 2014. Local impacts of climate change and agronomic practices on dry land crops in Southern Africa. Agric. Ecosyst. Environ. 197, 1–10.