Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China

Agricultural Water Management 193 (2017) 89–101

Contents lists available at ScienceDirect

Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

Research Paper

Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China Yimin Ding a,b , Weiguang Wang a,b,∗ , Ruiming Song a , Quanxi Shao c , Xiyun Jiao b , Wanqiu Xing b,d a

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China College of Water Resources and Hydrology, Hohai University, Nanjing 210098, China c CSIRO Data 61, Leeuwin Centre, 65 Brockway Road, Floreat Park, WA 6014, Australia d School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China b

a r t i c l e

i n f o

Article history: Received 22 July 2016 Received in revised form 14 July 2017 Accepted 4 August 2017 Keywords: Rice Climate change Effective temperature Irrigation scheduling Water balance Irrigation water requirement

a b s t r a c t Accounting for over 70% of global water withdrawals, irrigation plays a crucial role in the development of agriculture. Irrigation water requirement (WIRR) will be influenced by climate change due to the alteration in soil water balances, evapotranspiration, physiology and phenology under global warming. This is particularly true for rice, a high water-consuming crop. Therefore, exploring the impact of climate change on rice WIRR is of great significance for the sustainable utilization of water resources and food security. This paper aims to investigate spatially and temporally the responses of rice WIRR to climate change in the middle and lower reaches of the Yangtze River (MLRYR), which is one of the most important rice farming districts in China. With the help of the specially developed rice growing period calculation method and water balance model coupled with rice irrigation scheduling, the impacts of climate change on WIRR for early rice, late rice and single cropping rice during the historical (1961–2012) and future (2011–2100) periods were evaluated. Meanwhile, to consider the uncertainty from climate models in future projection, four GCMs under RCP2.6, RCP4.5 and RCP8.5 emission scenarios from the 5th Coupled Model Intercomparison Project (CMIP5) were employed as the input of the water balance model. The results indicate the following: (1) The days of growing period (DGP) for all three types of rice display shortened trends in historical and most future periods. However, in the middle region of the MLRYR, the DGP for early rice and late rice would increase by up to10 days in 2080s under RCP8.5 scenario. (2) Over the past 50 years, the WIRR significantly decreased by 1.58 and 2.10 mm yr−1 for late rice and single cropping rice, respectively. While for early rice, the WIRR only slightly decreased by 0.13 mm yr−1 . (3) Projected future WIRR would increase for all three types of rice in the whole region under RCP4.5 and RCP8.5 scenarios (up to 100 mm), but decrease for single cropping and late rice in the southeast region (up to 40 mm). The results can provide beneficial reference and comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization and agricultural management in the middle and lower reaches of the Yangtze River. © 2017 Published by Elsevier B.V.

1. Introduction

∗ Corresponding author at: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China. E-mail address: [email protected] (W. Wang). http://dx.doi.org/10.1016/j.agwat.2017.08.008 0378-3774/© 2017 Published by Elsevier B.V.

Continued emissions of greenhouse gases will further unequivocally cause warming and affect all components of the climate system (IPCC, 2013). Within this context, the climate variables such as temperature and precipitation will be undergoing significant changes (Terink et al., 2013; Wang et al., 2013a; Amorim Borges et al., 2014; Wang and Chen, 2014; Okkan, 2015). Under

90

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

most climate change scenarios, global surface temperature change is expected to exceed 1.5 ◦ C at the end of the 21st century (IPCC, 2013). While the precipitation in response to the warming over the 21st century will not be uniform in different regions (IPCC, 2014). Climate change and variability have already affected both natural and social systems (Parmesan and Yohe, 2003; Yu et al., 2006; Huntington, 2006; Wang et al., 2012, 2013b). Agricultural production, one of the most sensitive sectors to climate change, is experiencing significant changes (Piao et al., 2010; Tao and Zhang, 2013; Yang et al., 2013). Irrigation, accounting for 70% of global water withdrawals and even 90% in some countries such as India, Pakistan and Mexico (Fischer et al., 2007; Döll, 2009), has enabled farmers to improve crop output by approximately 40% (Leng and Tang, 2014). To feed a growing global population under a changing climate, irrigation water requirements (WIRR) will continue to increase in the next several decades to meet the rising food demand (Belder et al., 2004; Fischer et al., 2007). Hence, the water scarcity situation is likely to be exacerbated continuously, indicating that agricultural production and food security will face serious challenges (Tao et al., 2008). This situation makes it crucial to simulate the historical response of WIRR to climate change and project future WIRR, and further provide water resource management schemes. Therefore, the evaluation of potential consequences of climate change on WIRR has received considerable attention over the past decades (e.g., Yano et al., 2007; Fischer et al., 2007; Fleischer et al., 2008; Thomas, 2008; Tanasijevic et al., 2014; Ouda et al., 2015; Luo et al., 2015; Ye et al., 2015). As a key element of soil water balance, crop evapotranspiration (ETc ) plays an important role in estimation and projection of WIRR. Therefore, the response of ETc to climate change has also received a great deal of attention. For example, Cong et al. (2011) reported that ETc will increase separately by 3.21% and 2.89% in east and central south China during 2046–2065 compared with that during baseline period (1961–1990). A study carried out by Tanasijevic et al. (2014) showed that in the Mediterranean countries, ETc of olive was expected to increase by up to 8% in the middle of this century. Generally, although the specific change magnitude of ETc in previous studies is highly dependent on local climate conditions, researchers had confirmed the significant increase of ETc in the next several decades due to global warming. Along with the enhanced ETc and the highly varied precipitation pattern under future climate change scenarios, remarkable increase of WIRR was found in a number of studies at the global sacle (Döll, 2002; Fischer et al., 2007) and the regional scale such as Bangladesh (Shahid, 2011; Mainuddin et al., 2015), Sri Lanka (De Silva et al., 2007), America (Elgaali et al., 2007) and China (Thomas, 2008; Zhu et al., 2015; Leng and Tang, 2014). For instance, Fischer et al. (2007) showed that WIRR would increase by about 50% and 16% in developing and developed countries, respectively. With the help of A1B emission scenario and daily soil water balance model, Mainuddin et al. (2015) found that WIRR of Boro rice would increase by up to 3% in the middle of this century. China, the largest rice producer around the world, accounts for 18.5% of the world’s rice-harvested area and contributes nearly 29.1% of rice production in the world (Faostat, 2011; Yu et al., 2012). As the most important rice planting zone in China, the middle and lower reaches of the Yangtze River (MLRYR) accounts for 49% of rice cropping area of this country (National Bureau of Statistics of China, 2015). In the MLRYR, there are three types of rice (early, late and single cropping rice) planting under two different ricecropping systems (double cropping system and single cropping system). Traditionally, apart from the northern part of Jiangsu and Anhui province, most part of the study region belongs to double cropping system (Shen et al., 2011). Recently, given the inevitable influence of climate change on rice production, previous studies such as Shen et al. (2011) and Yang et al. (2015) have already explored the effects of climate change on rice yield in the MLRYR.

However, the study on assessment the influence of climate change on rice WIRR is scarce in spite of the great significance of WIRR to the sustainable utilization of water resources and rice production security. Thus, it is urgent to quantify how climate change will affect the WIRR in the MLRYR, especially the investigation of the spatial distribution pattern of WIRR due to the complex spatial heterogeneity in climate conditions and rice varieties. Moreover, because of some social and economic reasons, such as shortage of workforce, the double cropping rice in the MLRYR has significantly decreased in the past decades, while the planting of single cropping rice increased obviously (Shen et al., 2011). Besides, along with the warming environment, the suitable double cropping region has expanded to northern part of the MLRYR (Ye et al., 2015). Therefore, it is necessary to investigate the influence of climate change on the WIRR for each type of rice planting in the whole region separately to provide deep insights into the sustainable utilization of water resources and food security. Overall, the approaches used in previous studies to assess the impacts of climate change on WIRR can generally be grouped into two categories. The first one is driving crop models with GCMs data to simulate the responses of WIRR to climate change (e.g., Yano et al., 2007; De Silva et al., 2007; Wang et al., 2014). Crop models can characterize the dynamics of plant development process. However, crop models are always limited by the complexity in model structure and parameter estimation (Boote et al., 1996). Particularly, due to the spatial complexity of rice varieties in large study region, it is difficult to evaluate the spatial distribution of WIRR by crop model with limited field experimental data. The second one is driving the soil water balance model with future climatic input, which has been widely used in the evaluation of WIRR at large scale (e.g., Rodríguez Díaz et al., 2007; Zhu et al., 2015; Shahid, 2011). In this approach, effective precipitation is usually used to indicate the part of rainfall that can be used to meet the evapotranspiration of upland crops, such as wheat and maize. However, rice in the MLRYR is planted in submerged paddy field, in which a certain depth of water should always be maintained. Thus, the effective precipitation could not effectively reflect the utilization of precipitation in paddy field. Besides, the variability of crop phenology due to rising temperature has always not been taken into account in previous studies. Therefore, in this study, considering the influence of climate warming on rice growing cycle, a specially developed rice growing length calculation method was used to simulate the days of growing period (DGP) of rice. Subsequently, daily water balance model and rice irrigation scheduling were jointly employed to detect the spatial patterns of the long-term trends of ETc and WIRR for early rice, late rice and single cropping rice in the MLRYR under the happened climate change. Besides, the future change patterns of DGP, ETc and WIRR were projected based on an ensemble of four GCMs under three representative concentration pathways (RCP) from the 5th Coupled Model Intercomparison Project (CMIP5). The results are expected to contribute to an in-depth understanding of how the irrigation water requirements response to climate change, especially in regional scales, which will serve as a reference for policymakers and stakeholders to put forward regional strategies on local water resource against the potential menaces of global change.

2. Materials and methods 2.1. Study area, soil, phenology and climate data The middle and lower Reaches of the Yangtze River (MLRYR) is located between 24 and 35◦ N and 109–122◦ E, including six provinces (Jiangsu, Zhejiang, Anhui, Hubei, Hunan and Jiangxi) and Shanghai municipality (Fig. 1). Under the control of subtropical

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

monsoon climate, the MLRYR is characterized by hot and rainy summers, cold and dry winters. The annual mean temperature ranges from 14 to 20 ◦ C. The average annual precipitation is approximately 1000–1400 mm, and 40–60% of the annual precipitation is concentrated in the period from June to August. The warm and humid climate, adequate lighting and rainy season coincided with high temperature make the MLRYR become one of the most important rice production regions in the world. Historical observed meteorological data including relative humidity, wind speed, sunshine duration, precipitation, maximum temperature and minimum temperature at 94 National Meteorological Observatory stations from the period of 1961–2012 were obtained from the Meteorological data center of China Meteorological Administration. Based on sunshine hour, solar radiation was calculated by the method provided by Allen et al. (1998). The locations and distribution of the meteorological stations over the MLRYR are shown in Fig. 1. The detail information of the stations including name, latitude, longitude and altitude can be seen in Table S1. Using a range of GCMs is broadly considered as a useful way to address the uncertainties derived future climate projections (Yang et al., 2011; Xing et al., 2014; Rurinda et al., 2015). Therefore, three RCPs – RCP2.6, RCP4.5 and RCP8.5–were the inputs for the simulation of future irrigation water requirement with an ensemble of four GCMs – BCC-CSM1.1(m), MIROC-ESM-CHEM, GFDL-ESM2M and HadGEM2-ES from the CMIP5 experiment (Taylor et al., 2012). These four GCMs were employed because they can fully meet the data requirement to drive the water balance model and were widely used in climate change evaluations with favorable feasibility in China (Guo et al., 2015; Yin et al., 2015; Wang et al., 2017). Among the three RCPs, RCP8.5 exhibits the highest level of radiative forcing (8.5 W/m2 by 2100), RCP4.5 assumes a medium stabilization scenario (4.5 W/m2 by 2100), while RCP2.6 represents a low radiative forcing emission (2.6 W/m2 by 2100). From the four GCMs, climatic

91

variables, i.e., relative humidity, precipitation, solar radiation, wind speed, minimum temperature and maximum temperature were directly obtained. Soil physical and chemical properties were derived from the Harmonized World Soil Database (HWSD, http://www.fao.org/ soils-portal), with more than 16,000 different soil types around the world (Shen et al., 2011). HWSD soil database provides the basic soil information such as soil type, water storage capacity, soil organic carbon for each grid. However, the parameters, soil saturated hydraulic conductivity and saturated water content, for calculating soil water balance are not included. Therefore, SPAW model was used to calculate these parameters (https://www.ars. usda.gov). Rice phenological data for key growing stages were also extracted from the Meteorological Data Center of China Meteorological Administration. The data were collected in a total of 29, 36 and 36 agrometeorological experimental stations for single cropping rice, early rice and late rice, respectively, from 1992 to 2012. The locations and detail information of these stations are shown in Fig. 1 and Table S2, respectively. 2.2. Days of growing period Rice growth rate is sensitive to the growing temperature (Myneni et al., 1997). However, rice development rate does not always increase with the rise of temperature when the temperature is higher than an optimal threshold. Therefore, considering the physiological characteristics of rice, a special effective temperature calculation method (hereafter as RETM) provided by Kropff et al. (1994), was used to estimate rice DGP. In RETM, three critical temperature points, i.e., base temperature, optimal temperature and maximum temperature are taken into consideration. If temperature is higher than the maximum temperature or lower than the base temperature, rice development will stop. Besides, when

Fig. 1. Maps of the MLRYR and locations of the meteorological and agrometeorological experimental stations.

92

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

temperature is higher than the optimal temperature, the growing rate will slow down with temperature rising. The hourly effective temperature (HUH) can be described as:

HUH =

⎧0 ⎪ ⎪ ⎨ (Td − Tbase )

Td ≤ Tbase , Td ≥ Thigh

24

⎪ ⎪ ⎩ Topt − (Td − Topt )(Topt − Tbase )/(Thigh − Topt ) 24

Tbase < Td ≤ Topt

(1)

Topt < Td < Thigh

where Td is hourly temperature and calculated by a sine function based on daily maximum temperature and minimum temperature: Td =

(Tmax + Tmin ) (Tmax − Tmin ) cos(0.2618(h − 14)) + 2 2

(2)

where h is the number of hours in a day. Then, the daily effective temperature (DTU) can be written as: DTU =

24 

where dW is the change of field water stored; I is irrigation supply; R, E, T, P and D represent rainfall, evaporation, transpiration, percolation and surface drainage, respectively.

(HUH)

(3)

2.3.1. Crop evapotranspiration Crop evapotranspiration (ETc ) means the total amount of evaporation and transpiration during rice growing period. The single crop coefficient method was applied to compute the daily water demand of rice (Allen et al., 1998): ETc = kc × ETo

where kc the is the crop coefficient (dimensionless); ETo is the reference crop evapotranspiration (mm d−1 ). The FAO Penman-Monteith (P-M) method is used to calculate ETo (Allen et al., 1998): 900 0.408(Rn − G) +  (T +273) U2 (es − ea )

ETo =

2.3. Paddy water balance In order to calculate the spatial distribution and temporal trends of rice WIRR, paddy water balance and conventional irrigation scheduling (Chen et al., 1995) were used. Under conventional irrigation scheduling, paddy water depth is maintained between 0 and 50 mm during most phenological periods (Table 1). Besides, in a few days before transplanting, to provide a suitable environment for rice transplanting, paddy field should be maintained a certain depth of water to soften paddy soil. Here, based on the conventional planting experience, we set that the depth of plough layer is 40 cm and the initial soil water content is 60% of saturated water content. Subsequently, starting from 5 days before transplanting, paddy water depth is kept in around 30 mm. The paddy water balance formula can be briefly described as: dW = I + R − E − T − P − D

(4)

(6)

 + (1 + 0.34U2 )

h=1

DTU is calculated from rice sowing date. When accumulated DTU reached a fix temperature threshold (TT), rice growth process is completed. In this study, considering the differences of traditional farming habits and weather conditions in different regions, different sowing date and required TT were given to each province rather than a uniform parameter set for the whole study area. In addition, the lengths of different growing stages under different climate conditions were modified based on the changes of the whole growth duration by using fixed proportions derived from observed rice phenology. The proportions for the three types of rice in each province are shown in Table S3. Observed rice phenological data from 1992 to 2002 were used to calibrate the required TT for all three types of rice in all provinces, while the phenological data from 2003 to 2012 were used for validation. The validation results were evaluated by root mean square error (RMSE).

(5)

where Rn is the net radiation at the crop surface (MJ m−2 d−1 ); G is the soil heat flux density (MJ m−2 d−1 ); T is the mean daily air temperature at 2 m height (◦ C); U2 is the wind speed at 2 m height (m s−1 ); es is the saturated vapor pressure (kPa); ea is the actual vapor pressure (kPa); es − ea is vapor pressure deficit (kPa);  is the slope of vapor pressure curve (kPa ◦ C−1 ); and  is the psychrometric constant (kPa ◦ C−1 ). The crop coefficients of rice vary over the growing period. The critical values of kc for the early, middle and late stages of rice growing cycle are 1.05, 1.20 and 1.00, respectively (Allen et al., 1998; Li et al., 2011; Ye et al., 2015). 2.3.2. Paddy percolation Among all the components of paddy water balance formula, deep percolation is one of the most important parts of paddy water loss. Besides, it is also the most difficult element for estimating an accurate value because underground water level, soil texture, depth of paddy field and field drain spacing all play important roles in deep percolation (Chen et al., 1995). However, paddy percolation is insensitive to climate change under sufficient irrigation scheduling, making it possible to employ a simple method to estimate the value of percolation in paddy field. We thus used the estimation method provided by Li and Luo (2003) to reflect the influence of soil spatial heterogeneity characteristics on paddy percolation. For the unsaturated condition, daily percolation can be described as: Si =

1000K0

(7)

t

1 + K0 ˛ Hi

where Si is the percolation amount of i day (mm); K0 is the saturated hydraulic conductivity (m/d); ␣ is the empirical constant, generally between 50 and 250, and it increase with the increase of soil stickiness; ti is the number of days from the last soil saturated condition (d); H is the depth of rice root (m). Here, based on the investigation of paddy percolation in south China (Chen et al., 1995) and the def-

Table 1 Detail information about the irrigation scheduling used in this study. Growth stage

Green up

Upper limit (mm) Lower limit (mm) Drainage limit (mm) Root depth (cm)

50 30 60 20

a

Soil saturated water content.

Tillering

Booting

Early

Middle

Late

Early

Late

50 30 80 0–20

30 15 80

0 70%a 80

50 30 100 0–30

50 30 100

Heading

Milk Maturity

Yellow Ripe

50 30 100 0–40

30 0 60

– – 0 –

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

inition of ␣, we simplified the relationship between ␣ and soil clay fraction (CLAY, %) with a linear relation: ˛ = 50 + 2.0(CLAY )

(8)

Based on the investigation of field percolation in south China (Chen et al., 1995), the percolation under saturated condition was set as 1.2 times of the percolation of first unsaturated day to guarantee the calculation results located in the reasonable range.

2.4. Bias correction of future climate data On account of the coarse spatial resolution and the systematic bias of GCMs output, the scenarios delivered by GCMs output cannot be directly used for climate change studies (Rocheta et al., 2014). Therefore, it is necessary to use downscaling methods to acquire relative realistic GCMs output from coarse grid to point scale (Gu et al., 2015). In this study, a downscaling method named quantile mapping (QM), continuously developed by Li et al. (2010) and Wang and Chen (2013) was employed to adjust the GCM output based on a transfer function between observed data and model outputs. QM was selected due to its excellent performance in removing biases of precipitation relative to other methods (Gudmundsson et al., 2012; Zollo et al., 2014). To evaluate the performance of QM method, the 40-year observed climate data (1961–2000) were divided into two groups. The data of 1961–1990 were used for calibration, while the data of 1991–2000 were used for validation. Correlation coefficient (R2 ), coefficient of efficiency (Ens) and model relative biases (Bias) were calculated to quantitatively evaluate the performance of this method.

93

2.5. Trend analysis methods As the most frequently employed method for detecting longterm trends of time series, the simple linear regression method was applied to assess the long-term trend magnitudes of DGP, ETc and WIRR. Besides, the nonparametric Mann-Kendall test method (Mann, 1945; Kendall, 1975) was used for testing weather the time series are significant at the 5% significance level or not (Gocic and Trajkovic, 2013; Zhang et al., 2011). Furthermore, before applying the Mann-Kendall test, the trend free pre-whitening procedure (TFPW) should be carried out to eliminate the influence of the serial correlation or autocorrelation in the time series (Yue et al., 2002; Yue and Wang, 2002). 3. Results 3.1. The performance of RETM and the phenological parameters With the help of the observed rice phenology, the performance of RETM was evaluated (Table 2). RETM performed well in predicting the growing length for all types of rice in all provinces with the RMSE varying from 2.58 to 6.92 days. Mean sowing date and mean required TT for all three types of rice in all provinces are also shown in Table 2. It can be clearly found the mean sowing dates of single cropping rice varied greatly in different provinces. Taking Hunan and Zhejiang provinces as an example, the difference of sowing date between the two provinces reached 43 days. While the largest gap of mean sowing dates for late rice between different provinces was only 9 days. Unlike the large variation of sowing date for single cropping rice, the mean required TT for the same type of rice in different provinces differ slightly.

Table 2 Mean observed sowing date (SD), mean temperature threshold (TT), mean days of growing period (DGP) and the validation results of DGP for three rice types in different provinces. Mean SD (day of year)

Mean TT (◦ Cd)

Mean DGP (days)

Std (days)

R2

RMSE (days)

Jiangsu

a

Early Rice Late Ricea Single Cropping Rice

96 172 133

1661 1937 2388

115 123 151

6.63

0.89

5.51

Anhui

Early Rice Late Rice Single Cropping Rice

96 172 120

1661 1937 2398

115 123 147

3.41 5.31 7.45

0.75 0.82 0.76

3.36 4.47 5.32

Hubei

Early Rice Late Rice Single Cropping Rice

91 171 109

1617 1907 2356

110 123 148

4.31 7.48 4.56

0.75 0.67 0.86

4.09 6.92 3.38

Hunan

Early Rice Late Rice Single Cropping Rice

88 172 106

1654 1976 2356

108 118 142

2.79 6.36 6.09

0.88 0.79 0.75

2.58 5.83 6.02

Jiangxi

Early Rice Late Rice Single Cropping Rice

87 175 141

1642 1982 2364

109 117 136

5.31 7.45 6.47

0.82 0.76 0.42

4.47 5.32 5.22

Zhejiang

Early Rice Late Rice Single Cropping Rice

90 173 149

1589 2094 2396

112 126 144

4.68 4.03 4.29

0.85 0.79 0.82

4.67 3.89 4.26

Province

a

Variety

Parameters for Early Rice and Late Rice in Jiangsu province was set the same as that in Anhui province due to the missing of observed phenology.

Table 3 Performance assessment for minimum temperature (Tmin ), maximum temperature (Tmax ), precipitation (P) and solar radiation (Rn ) in validation based on the four different climate models. Climatic Variables

BCC 2

Tmin Tmax P Rn

GFDL 2

HadGEM2 2

MICRO

R

Ens

Bias

R

Ens

Bias

R

Ens

Bias

R2

Ens

Bias

0.981 0.973 0.694 0.950

0.962 0.961 0.483 0.944

0.015 0.011 0.035 −0.006

0.980 0.974 0.621 0.944

0.974 0.957 0.424 0.940

0.022 0.004 0.038 −0.001

0.989 0.971 0.504 0.942

0.971 0.943 0.407 0.940

0.008 −0.002 0.020 0.002

0.989 0.973 0.687 0.946

0.971 0.962 0.472 0.941

0.024 0.010 −0.001 0.012

94

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

3.2. Evaluation on the performance of QM method The performances of QM method were evaluated for the major climate variables (i.e., minimum temperature, maximum temperature and solar radiation and precipitation) under all four GCMs. The comparisons of three different measures (i.e., R2 , Ens and Bias) between observed and bias corrected results during validation period are shown in Table 3. The minimum temperature, maximum temperature and solar radiation were generally in accordance with observations. Averaged R2 and Ens for these three variables between observed and simulated ones were higher than 0.94, and averaged Bias were close to zero for all four GCMs. Meanwhile, the change characteristics of rainfall can be overall captured by QM method, although the R2 and Ens of precipitation were lower than other variables. Generally, although the capability of correcting precipitation was limited, QM method can effectively eliminate the bias of most variables effectively. 3.3. Spatial-temporal distribution of DGP, ETc and WIRR in the past decades The lengths of DGP for single cropping rice, early rice and late rice in the past 52 years were simulated by RETM with the provincial phenological parameters. The spatial distribution patterns of DGP were similar for three different types of rice, shorter in the

middle and southern regions and longer in the northern and northwest regions (Fig. 2). For single cropping rice, the DGP was less than 145 days in the southern region, but in the northeast and northwest regions, the DGP reached more than 165 days. For early and late rice, smaller variations of DGP were found across different regions compared with that for single cropping rice. However, it should be noted that the DGP for late rice was 10 days longer in the southeast region than in the other part of the study area. This should be attributed to the special late rice variety planted in this region that required higher TT (Table 2). In general, the DGP for single cropping rice (147 days on regional average) was longer than that for early rice and late rice (126 days and 118 days, respectively). Besides, the regional mean DGP for the three types of rice all showed significantly decreasing trends (P < 0.05) in the past five decades, the decreasing trends for single cropping rice, early rice and late rice were −0.12, −0.12 and −0.15 day a−1 , respectively (Fig. 3). The spatial distribution patterns of ETc are also shown in Fig. 2. It can be seen that the spatial structures of ETc were inconsistent between the three types of rice. For single cropping rice, the ETc was lower in the eastern and western regions (varied from 570 to 650 mm), but higher in the middle region (varied from 670 to 730 mm). For early rice, from low to high value, a clear southernnorthern gradient of ETc can be found in the study region, while a complete reverse spatial distribution pattern was detected in the ETc for late rice. The regional average ETc for late rice was close

Fig. 2. Spatial variability of days of growing period (DGP), crop water requirement (ETc ) and irrigation water requirement (WIRR) for single cropping rice, early rice and late rice during the past decades (1961–2012) in the MLRYR.

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

95

to early rice, but obviously smaller than that for single cropping rice (Fig. 3). Overall, the regional average ETc showed remarkably decreasing trends (P < 0.05) with the slope values of −1.5, −1.0 and −1.2 mm a−1 for single cropping rice, early rice and late rice, respectively. Despite that the spatial distribution patterns of WIRR were generally consistent with that of ETc , a larger variation magnitude in WIRR among different regions were projected (Fig. 2). For early rice, the highest values of WIRR (up to 500 mm) mainly occurred in the northern part, but the values were usually less than 100 mm in the southern region. For late rice, no coherent spatial pattern was found from north to south, the lowest WIRR values (ranged from 200 to 300 mm) were mainly observed in the western and eastern regions, while the highest values (attained as much as 550 mm) were concentrated in the middle region. Similar to the spatial distribution pattern of late rice, the highest values of WIRR for single cropping rice (varied from 570 to 690 mm) were also found in the middle region. The regional average variation trends of WIRR are shown in Fig. 3, from which significantly decreasing trends for single cropping rice and late rice were found with the slope values of −2.1 and 1.6 mm a−1 , respectively, while the deceasing trend of early rice was only 0.13 mm a−1 . 3.4. Spatial-temporal changes of DGP, ETc and WIRR under future scenarios By using the period of 1961–1990 as a reference period, the changes in rice DGP, ETc and WIRR across the MLRYR in three future periods, i.e., 2020s (2011–2040), 2050s (2041–2070) and 2080s (2071–2100), under RCP2.6, RCP4.5 and RCP8.5 scenarios were projected. The spatial patterns of the changes in DGP were similar across different scenarios, but with different magnitudes (Figs. 4 , S1 and S2). Under RCP8.5 scenario, the average DGP would be shortened for all three types of rice in future three periods in the whole study region, except that for single cropping rice and late rice in 2080s in the middle region (Fig. 4). For single cropping rice in 2020s, the DGP was shortened by 0–4 days in the middle region (e.g., southern Hubei and Jiangxi provinces), and 6–12 days in the eastern region (e.g., northern Jiangsu and eastern Zhejiang provinces). While in 2050s, although the DGP for single cropping rice in the eastern and southwest regions would continue to be shortened, the reduced days in this period was smaller than in 2020s, especially in the middle region, where the DGP was even prolonged by 0–4 days. As in 2080s, with the continually increasing temperature, the DGP for single cropping was lengthened (up to 10 days) in most part of the study area. For late rice, the changing patterns of DGP in three future periods were similar to that for single cropping rice, but the prolonged days for late rice in 2080s (up to 8 days) was less than that for single cropping rice. Unlike the changing trends for single cropping and late rice, the DGP of early rice showed continually shortening trends from 2020s to 2080s under all three RCPs in the whole study region. The spatial patterns of changes in ETc for three types of rice were also similar across the different RCPs, but the magnitudes differ considerably (Figs. 6, S3 and S4). Under RCP8.5 scenario, contrary to the complex change trends of DGP, ETc for the three types of rice almost presented consistently increasing trends in different future stages in the whole study region, except that for early rice in the southwest region. For single copping rice, the largest increase in future three periods all occurred in the northeast and middle regions (up to 60, 120 and 200 mm in 2020s, 2050s and 2080s, respectively). The increase for other regions ranged from 0 to 20 mm in 2020s, 20 to 60 mm in 2050s and 60 to 100 mm in 2080s, respectively. For early rice, although the spatial distributions of ETc in future three stages were similar to that for single cropping rice, smaller increasing magnitude was found. Besides,

Fig. 3. Temporal variability of days of growing period (DGP), crop water requirement (ETc ) and irrigation water requirement (WIRR) for single cropping rice, early rice and late rice during the past decades (1961–2012) in the MLRYR. Z < −1.96 indicate significantly decreasing trend at the 95% confidence level.

it should be noted that ETc for early rice in 2050s even decreased about 5 mm in the southwest region relative to that in 2020s. However, this decreasing trend would be reversed in 2080s due to the significantly increased temperature. For late rice, the large increments were also distributed in the northeast region with the values varied from 20 to 40 mm in 2020s, 60 to 80 mm in 2050s and mm in 2080s. Overall, apart from 2080s under RCP4.5 scenario, ETc for the three rice types would increase dramatically from 2020s to 2080s. Among the three types of rice, single cropping rice had the largest increment (Fig. 5). The changes of WIRR in future three periods showed more obviously spatial variability relative to that of ETc , especially for single cropping rice and late rice with increase of WIRR mainly concentrating in the northern region, but for early rice, an inverse spatial pattern was found (Figs. 7 , S5 and S6). For single cropping rice,

96

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

Fig. 4. Changes of days of growing period (DGP) for single cropping rice, early rice and late rice under RCP8.5 scenario during future three periods (2020s, 2050s and 2080s) relative to the baseline period. The boxes indicate the 25th–75th percentiles and the whiskers show the 5th–95th percentiles of DGP for the whole study area.

there was a small decrease of WIRR in the southern region with the value less than 20 mm in 2020s. In 2080s, although the increase of WIRR for single cropping rice in the southern region was still smaller than that in the baseline period, sharply increased WIRR (up to 200 mm) was found in the northern region. For late rice, similar to the spatial-temporal patterns of single cropping rice, the decreasing WIRR also mainly occurred in the southern region, but the magnitude was relatively small. For early rice, the increase magnitude of WIRR was similar to late rice, but inverse spatial distribution pattern was found. From the mean value of the whole region (Fig. 5), WIRR for the three types of rice would increase under most future conditions with the largest increase of WIRR (up to 100 mm) occurring in 2080s under RCP8.5 scenario. 4. Discussion Climate change with the increasing temperature and the highly varied precipitation will inevitably affect future crop irrigation, which is particularly the case in China (Piao et al., 2010). Rice irrigation, the largest water-consuming sector in the MLRYR, plays the most important role in the sustainable utilization of water resources and food security under future climate change. Based on water balance model combining with single GCM, effective precipitation and fixed rice growing duration, extensive literatures have investigated the potential consequences of climate change on rice irrigation water requirement (e.g., Cong et al., 2011; Leng and Tang,

2014; Ye et al., 2015; Zhu et al., 2015). However, the inevitable uncertainties from GCMs make it difficult to output accurate ET0 and WIRR by single climate model. Currently, some studies have already employed multiple climate models in assessing the impacts of climate change on crop yield to obtain more robust results (e.g., Tao et al., 2008; Trnka et al., 2014; Rurinda et al., 2015). Nevertheless, using multiple models to evaluate the response of large-scale WIRR to climate change is rarely reported. Moreover, due to the special planting habit, using effective precipitation in water balance model to calculate rice WIRR will also generate large uncertainties. Furthermore, fixed growing duration will bring imprecise results as well because rice-growing season will change more than 10 days under some future extreme situations (Wang et al., 2017). Therefore, considering the aforementioned uncertainties, an ensemble of four GCMs, daily water balance model, a specially developed rice DGP calculation method and a conventional irrigation scheduling were employed jointly to assess the impacts of climate change on irrigation water requirement for early rice, late rice and single cropping rice in the MLRYR. Under most schemes (combination of different periods and RCPs), DGP for all three types of rice in the whole study region would be shortened by the remarkably increased temperature (Figs. 4, S7 and S8), but much more for early rice than the other two types of rice (Figs. 3 and 5). However, in 2080s under RCP8.5 scenario, the DGP for single cropping rice and late rice in the middle study region would be lengthened by up to 10 days. This result was not in accor-

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

-1 -2 -3 -4 -5 -6 -7 -8 -9

Single cropping rice

Early Rice

Late rice

2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s RCP2.6

RCP4.5

RCP8.5

140 120

Single cropping rice

Early Rice

Late rice

100 80 60 40 20 0 -20 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s RCP2.6

RCP4.5

RCP8.5

100 Single cropping rice

Early Rice

Late rice

80 60 40 20 0 -20 -40 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s RCP2.6

RCP4.5

RCP8.5

Fig. 5. Simulated impacts of climate change on the days of growing period (DGP), crop evapotranspiration (ETc ) and irrigation water requirement (WIRR) for three rice types under three scenarios (RCP2.6, RCP4.5 and RCP8.5) in three future periods (2020s, 2050s and 2080s).

dance with most previous studies in the MLRYR (e.g., Tao et al., 2008; Shen et al., 2011; Wang et al., 2014). In fact, rice-developing rate will slow down when the temperature during rice growing season is higher than the optimal temperature threshold. Taking the changes of DGP for single cropping rice under RCP8.5 scenario as an example (Fig. 8), an inflection point of mean maximum temperature during growing period was shown between 33 ◦ C and 34 ◦ C. When the mean maximum temperature was higher than the inflection point, the DGP would increase obviously with temperature rising. In line with our results, some literatures have also pointed out that DGP of rice would not be shortened continuously by the increasing temperature, which is especially true for rice in subtropical regions of China (e.g., Zhao et al., 2007; Liu et al., 2008). Despite that increasing temperature would have positive effects on evapotranspiration, ETc showed continuously decreasing trends

97

for all three types of rice in the whole study region without exception during the past decades. This could be partly explained by the sharply decreased net radiation together with the reduced wind speed (Fig. S7). Besides, the shortened DGP resulted in a shorter time for crop evapotranspiration. Thus, the combined negative effects of the aforementioned three factors on ETc offset the positive effects brought from the rising temperature (Fig. S7), which corresponds well with previous studies (e.g., Xu et al., 2006; Tao et al., 2008; Wang et al., 2012, 2014). Along with the increased precipitation, decreased ETc and the reduced deep percolation of paddy field due to shortened growing cycle, the WIRR for single cropping rice and late rice also showed significantly decreasing trends in the past 50 years (Fig. 3). However, it should be noticed that different with the significantly decreasing trend of WIRR for single cropping rice and late rice, the WIRR of early rice decreased slightly, which could be attributed to the decreasing precipitation during the growing period of early rice (Fig. S7). This result was further supported by Becker et al. (2006), who indicated that precipitation presented decreasing trends in April and May, the early rice-growing season. Due to the obviously increasing temperature in future periods (Fig. S8), the increasing ETc was found across different types of rice under most different schemes (Figs. 3 and 5). This has also been drawn by some previous studies on rice in China (e.g., Shen et al., 2011; Cong et al., 2011; Ye et al., 2015; Wang et al., 2014, 2017). However, under RCP8.5 scenario, ETc for early rice in the southern region decreased in 2050s relative to that in 2020s, which could be attributed to the sharply decreased DGP shortened the water consuming days (Fig. 5). Although WIRR for three types of rice in most schemes showed increasing pattern, the increasing precipitation (Fig. S9) combining with the shortened growing days for soil deep percolation resulted in the unremarkable increase of WIRR relative to ETc (Fig. 5). Besides, due to the greatly increased precipitation in the southern region, the WIRR for single cropping rice and late rice in this region even showed decreasing trends in all schemes (Fig. S9). With the help of the four GCMs, DGP calculation method, daily water balance model and irrigation scheduling, we evaluated the responses of rice DGP, ETc and WIRR to climate change. Although we have considered as much details as possible (e.g., giving different phenology parameters to each rice type in each province, using the specially developed rice growing period calculation method, biascorrection method and multiple climate models), several other sources of uncertainties and limitations should be point out here. Firstly, uncertainties come from GCMs. Taking single cropping rice as an example, Fig. 9 shows the inter-model spread of change in ETc and WIRR under three RCPs derived from the four GCMs. It can be found the spreads of ETc produced by GCMs were generally less than 15%. However, due to the large uncertainty of precipitation among GCMs, the spreads of WIRR showed higher than 25% under some conditions. In line with our study, Smith et al. (2014) found similar spreads of maize irrigation water demand. Moreover, although wind speed and humidity are directly provided by the newly released GCMs, the qualities of these two variables are less satisfactory (low correlation with measured data) than temperature and precipitation (Shiehbegi et al., 2014; Wang et al., 2014). As sensitive input variables of P-M method in some regions (Xu et al., 2006; Liu et al., 2017), the poor quality of these two variables will increase the uncertainty of ETc and WIRR. Therefore, the issue of how to effectively address the uncertainties from climate models is still facing huge challenges (Yao et al., 2011; Wang et al., 2014). Secondly, the influence of CO2 on crop evapotranspiration was not taking into consideration. Although P-M method combining with the specially developed DGP calculation method is an effective way to calculate crop water requirement, with the increasing concentration of CO2 , crop evapotranspiration will be consequently decreased due to the reason that higher

98

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

Fig. 6. Changes of crop evapotranspiration (ETc ) for three different rice types under RCP8.5 scenario during future three periods (2020s, 2050s and 2080s) relative to the baseline period. The boxes indicate the 25th–75th percentiles and the whiskers show the 5th–95th percentiles of ETc for the whole study area.

CO2 concentration will lead to crop stomatal closure (Tao et al., 2008; Zhao et al., 2015). Finally, irrigation efficiency was not taking into consideration. Irrigation efficiency is an essential measure to evaluate the performance of irrigation in term of the irrigation water requirement in field, irrigation district or watershed (Howell, 2003). However, highly effected by engineering and management measures, the issue of how to evaluate the response of irrigation efficiency to future change remains a challenge. Therefore, further exploration should be conducted to improve the results of irrigation water requirement with consideration of these limitations and uncertainties. In addition, to mitigate the adverse impacts of climate change on irrigation water requirement, adaptation strategies should be carried out. Generally, altering rice planting date and using deficit irrigation scheduling are recognized as useful adaptation strategies. However, to what extent these adaptation strategies will reduce the negative impacts of climate change require further investigation.

5. Conclusion The responses of crop evapotranspiration, irrigation water requirement to climate change in the middle and lower reaches of the Yangtze River (MLRYR) were simulated by driving rice daily water balance model with the bias-corrected four GCMs outputs.

Besides, during the simulation process, rice-growing length was determined by the specially developed rice effective temperature calculation method. Simulated and observed rice growing period for single cropping rice, early rice and late rice in each province matched well indicating that this method is applicable to predict rice-growing length. Bias correction method performed well in eliminating the biases of major climatic variables, making it suitable to correct the biases of GCM outputs. Under most schemes, the averaged rice DGP for the whole study region would be shortened by the remarkably increasing temperature. However, the DGP for single cropping rice and late rice in the middle region would be lengthened by up to 10 days in 2080s under RCP8.5 scenario. Apart from the WIRR of early rice, remarkably decreasing trends of ETc and WIRR were found in the past decades for all three types of rice. The high values of WIRR for late rice and single cropping rice were mainly observed in the northern region of the MLRYR. Generally, the regional average WIRR was significantly decreased by 1.58 and 2.10 mm yr−1 for late rice and single cropping rice, respectively, in the past decades. While for early rice, the regional average WIRR only slightly decreased by 0.13 mm yr−1 . Projected WIRR for the three types of rice increased in the whole region (less than 100 mm), but decreased for single and late rice in the southeast region (up to 40 mm).

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

99

ETc (%)

Fig. 7. Changes of irrigation water requirement (WIRR) for three different rice types under RCP8.5 scenario during future three periods (2020s, 2050s and 2080s) relative to the baseline period. The boxes indicate the 25th–75th percentiles and the whiskers show the 5th–95th percentiles of WIRR for the whole study area.

37 36

25 20

34

15 Spread of

35

33 32 31

RCP2.6 RCP4.5 RCP8.5

30

10 5 0 2020s

30

2050s

2080s

2020s

2050s

2080s

ETc

29 (days) Fig. 8. Relationship between maximum temperature and days of growing period (DGP) for single cropping rice under RCP8.5 scenario. The black horizontal and vertical lines indicate the mean maximum temperature and mean DGP during historical period, respectively. The green (2020s), red (2050s) and blue (2080s) circles show the mean value across years of each simulation station in the study area. The colorful lines indicate the linear fit of each period. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 9. Inter-model spreads of WIRR and ETc during three future periods (2020s, 2050s and 2080s) under three scenarios (RCP2.6, RCP4.5 and RCP8.5) relative to the mean value of the baseline period.

Acknowledgments This work was financially supported by the National Science Foundation of China (51379057), the Fundamental Research Funds for the Central Universities (2015B14114), the National “Ten Thousand Program” Youth Talent, the Open Foundation of State Key

100

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101

Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2015490211), and the Project of Jiangsu Water Conservancy Science and Technology (2016061). Thanks to the National Meteorological Information Center, China Meteorological Administration (http://data.cma.gov.cn/) for offering the meteorological data and also thanks to the Working Group of the World Climate Research Program on Coupled Modeling, which is responsible for CMIP5. Finally, cordial thanks should be extended to the Editor, Dr. Enrique Fernández, Associate Editor, Dr Peter S. Searles, and the two anonymous referees for their valuable comments which greatly improved the quality of this paper. 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.agwat.2017.08. 008. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements-Irrigation and Drainage Paper 56. Food and Agriculture Organization of the United Station, Rome. Amorim Borges, P.D., Barfus, K., Weiss, H., Bernhofer, C., 2014. Trend analysis and uncertainties of mean surface air temperature, precipitation and extreme indices in CMIP3 GCMs in Distrito Federal, Brazil. Environ. Earth Sci. 72 (12), 4817–4833, http://dx.doi.org/10.1007/s12665-014-3301-y. Becker, S., Gemmer, M., Jiang, T., 2006. Spatiotemporal analysis of precipitation trends in the Yangtze River catchment. Stoch. Environ. Res. Risk Assess. 20 (6), 435–444, http://dx.doi.org/10.1007/s00477-006-0036-7. Belder, P., Bouman, B.A.M., Cabangon, R., Guoan, L., Quilang, E.J.P., Yuanhua, L., Spiertz, J.H.J., Tuong, T.P., 2004. Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agric. Water Manage. 65 (3), 193–210, http://dx.doi.org/10.1016/j.agwat.2003.09.002. Boote, K.J., Jones, J.W., Pickering, N.B., 1996. Potential uses and limitations of crop models. Agron. J. 88 (5), 704–716. Chen, Y., Guo, G., Wang, G., Kang, S., Luo, H., Zhang, D., et al., 1995. Main Crop Water Requirement and Irrigation of China. Water conservancy and electric power press, Beijing (China), pp. 129. Cong, Z., Yao, B., Ni, G., 2011. Crop water demand in China under the SRA1 B emissions scenario. Adv. Water Sci. 22, 38–43 (in Chinese with English abstract). Döll, P., 2002. Impact of climate change and variability on irrigation requirement: a global perspective. Clim. Change 54, 269–293. Döll, P., 2009. Vulnerability to the impact of climate change on renewable groundwater resources: a global-scale assessment. Environ. Res. Lett. 4, 035006, http://dx.doi.org/10.1088/1748-9326/4/3/035006. De Silva, C.S., Weatherhead, E.K., Knox, J.W., Rodriguez-Diaz, J.A., 2007. Predicting the impacts of climate change—a case study of paddy irrigation water requirements in Sri Lanka. Agric. Water Manage. 93 (1–2), 19–29, http://dx.doi. org/10.1016/j.agwat.2007.06.003. Elgaali, E., Garcia, L.A., Ojima, D.S., 2007. High resolution modeling of the regional impacts of climate change on irrigation water demand. Clim. Change 84 (3–4), 441–461, http://dx.doi.org/10.1007/s10584-007-9278-8. Faostat, 2011. Statistics Database, Available at: http://faostat.fao.org. Fischer, G., Tubiello, F.N., van Velthuizen, H., Wiberg, D.A., 2007. Climate change impacts on irrigation water requirements: effects of mitigation, 1990–2080. Technol. Forecasting Social Change 74 (7), 1083–1107. Fleischer, A., Lichtman, I., Mendelsohn, R., 2008. Climate change, irrigation, and Israeli agriculture: will warming be harmful? Ecol. Econ. 65 (3), 508–515, http://dx.doi.org/10.1016/j.ecolecon.2007.07.014. Gocic, M., Trajkovic, S., 2013. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Global Planet. Change 100, 172–182, http://dx.doi.org/10.1016/j.gloplacha.2012.10. 014. Gu, H., Yu, Z., Wang, G., Wang, J., Ju, Q., Yang, C., et al., 2015. Impact of climate change on hydrological extremes in the Yangtze River basin, China. Stoch. Environ. Res. Risk Assess. 29 (3), 693–707. Gudmundsson, L., Bremnes, J.B., Haugen, J.E., Skaugen, T.E., 2012. Technical note: downscaling rcm precipitation to the station scale using quantile mapping−a comparison of methods. Hydrol. Earth Syst. Sci. Discuss. 9 (5), 6185–6201. Guo, S., Guo, J., Hou, Y., Xiong, L., Hong, X., 2015. Prediction of future runoff change based on Budyko hypothesis in Yangtze river basin. Shuikexue Jinzhan/Adv. Water Sci. 26 (2), 151–160. Howell, T.A., 2003. Irrigation efficiency. In: Dictionary Geotechnical Engineering/wörterbuch Geotechnik, 385–391, 398. Huntington, T.G., 2006. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319 (1–4), 83–95, http://dx.doi.org/10.1016/j. jhydrol.2005.07.003.

IPCC, 2013. Intergovernmental Panel on Climate Change, Summary for Policymakers: Climate Change 2013. The Physical Science Basis, Contribution of Working Group 1 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. IPCC, 2014. Intergovernmental Panel on Climate Change, 5th Assessment Report: Climate Change 2014. Synthesis Report. World Meteorological Organization. Kendall, M.G., 1975. Rank Correlation Methods. Griffin, London, UK. Kropff, M.J., Cassman, K.G., van Laar, H.H., 1994. Quantitative understanding of the irrigated rice ecosystem for increased yield potential. In: Virmani, S.S. (Ed.), Hybrid Rice Technology: New Developments and Future Prospects. International Rice Research Institute, Manila (Philippines), pp. 97–113. Leng, G., Tang, Q., 2014. Modeling the impacts of future climate change on irrigation over China: sensitivity to adjusted projections. J. Hydrometeorol. 15 (5), 2085–2103, http://dx.doi.org/10.1175/JHM-D-13-0182.1. Li, Y., Luo, J., 2003. Water Saving Irrigation Theory and Technique. Wuhan university press, Wuhan (China). Li, H., Sheffield, J., Wood, E.F., 2010. Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change ar4 models using equidistant quantile matching. J. Geophys. Res. Atmos. 115 (D10), 985–993. Li, Y., Yang, X., Ye, Q., Huang, X., 2011. Variation characteristics of rice water requirement in middle and lower reaches of Yangtze River during 1961–2007. Trans. Chin. Soc. Agric. Eng. 27 (9), 175–183 (in Chinese with English abstract). Liu, Q., Yan, C., Ju, H., Garré, S., 2017. Impact of climate change on potential evapotranspiration under a historical and future climate scenario in the huang-huai-hai plain, china. Theor. Appl. Climatol., 1–15. Luo, Y., Jiang, Y., Peng, S., Cui, Y., Khan, S., Li, Y., Wang, W., 2015. Hindcasting the effects of climate change on rice yields, irrigation requirements: and water productivity. Paddy Water Environ. 13, 81–89. Mainuddin, M., Kirby, M., Chowdhury, R.A.R., Shah-Newaz, S.M., 2015. Spatial and temporal variations of, and the impact of climate change on, the dry season crop irrigation requirements in Bangladesh. Irrig. Sci. 33 (2), 107–120, http:// dx.doi.org/10.1007/s00271-014-0451-3. Mann, H.B., 1945. Nonparametric tests against trend. Econometrica 13, 245–259. Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G., Nemani, R.R., 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386 (6626), 698–702. National Bureau of Statistics of China, 2015. China Statistical Yearbook. China Statistics Press, Beijing, China. Okkan, U., 2015. Assessing the effects of climate change on monthly precipitation: proposing of a downscaling strategy through a case study in Turkey. KSCE J. Civ. Eng. 19 (4), 1150–1156, http://dx.doi.org/10.1007/s12205-014-0052-y. Ouda, S., Noreldin, T., Abd El-Latif, K., 2015. Water requirements for wheat and maize under climate change in North Nile Delta. Span. J. Agric. Res. 13 (1), e301, http://dx.doi.org/10.5424/sjar/2015131-6412. Parmesan, C., Yohe, G., 2003. A globally coherent fingerprint of climate impacts across natural systems. Nature 421, 37–42. Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H., Ma, Y., Ding, Y., Friedlingstein, P., Liu, C., Tan, K., Yu, Y., Zhang, T., Fang, J., 2010. The impacts of climate change on water resources and agriculture in China. Nature 467 (7311), 43–51, http://dx.doi.org/10.1038/nature09364. Rocheta, E., Evans, J.P., Sharma, A., 2014. Assessing atmospheric bias correction for dynamical consistency using potential vorticity. Environ. Res. Lett. 9 (9), 124010–124016 (7). Rodríguez Díaz, J.A., Weatherhead, E.K., Knox, J.W., Camacho, E., 2007. Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Reg. Environ. Change 7 (3), 149–159, http://dx.doi.org/10.1007/ s10113-007-0035-3. Rurinda, J., van Wijk, M.T., Mapfumo, P., Descheemaeker, K., Supit, I., Giller, K.E., 2015. Climate change and maize yield in southern africa: what can farm management do? Global Change Biol. 21 (12), 4588–4601. Shahid, S., 2011. Impact of climate change on irrigation water demand of dry season Boro rice in northwest Bangladesh. Clim. Change 105 (3–4), 433–453, http://dx.doi.org/10.1007/s10584-010-9895-5. Shen, S., Yang, S., Zhao, Y., Xu, Y., Zhao, X., Wang, Z., Liu, J., Zhang, W., 2011. Simulating the rice yield change in the middle and lower reaches of the Yangtze River under SRES B2 scenario. Acta Ecol. Sin. 31 (1), 40–48, http://dx. doi.org/10.1016/j.chnaes.2010.11.007. Smith, P.C., Heinrich, G., Suklitsch, M., Gobiet, A., Stoffel, M., Fuhrer, J., 2014. Station-scale bias correction and uncertainty analysis for the estimation of irrigation water requirements in the swiss rhone catchment under climate change. Clim. Change 127 (3), 521–534. Tanasijevic, L., Todorovic, M., Pereira, L.S., Pizzigalli, C., Lionello, P., 2014. Impacts of climate change on olive crop evapotranspiration and irrigation requirements in the Mediterranean region. Agric. Water Manage. 144, 54–68, http://dx.doi.org/10.1016/j.agwat.2014.05.019. Tao, F., Zhang, Z., 2013. Climate change, wheat productivity and water use in the North China Plain: a new super-ensemble-based probabilistic projection. Agric. For. Meteorol. 170, 146–165, http://dx.doi.org/10.1016/j.agrformet. 2011.10.003. Tao, F., Hayashi, Y., Zhang, Z., Sakamoto, T., Yokozawa, M., 2008. Global warming, rice production, and water use in China: developing a probabilistic assessment. Agric. For. Meteorol. 148 (1), 94–110, http://dx.doi.org/10.1016/j.agrformet. 2007.09.012. Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93 (4), 485–498.

Y. Ding et al. / Agricultural Water Management 193 (2017) 89–101 Terink, W., Immerzeel, W.W., Droogers, P., 2013. Climate change projections of precipitation and reference evapotranspiration for the Middle East and Northern Africa until 2050. Int. J. Climatol. 33 (14), 3055–3072, http://dx.doi. org/10.1002/joc.3650. Thomas, A., 2008. Agricultural irrigation demand under present and future climate scenarios in China. Global Planet. Change 60 (3–4), 306–326, http://dx.doi.org/ 10.1016/j.gloplacha.2007.03.009. Trnka, M., Rötter, R.P., Ruiz-Ramos, M., Kersebaum, K.C., Olesen, J.E., Zˇ alud, Z., et al., 2014. Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change 4 (7), 637–643. Wang, L., Chen, W., 2013. Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation. Atmos. Sci. Let. 15 (1), 1–6. Wang, L., Chen, W., 2014. A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int. J. Climatol. 34 (6), 2059–2078, http://dx.doi.org/10.1002/joc.3822. Wang, W., Shao, Q., Peng, S., Xing, W., Yang, T., Luo, Y., Yong, B., Xu, J., 2012. Reference evapotranspiration change and the causes across the Yellow River Basin during 1957–2008 and their spatial and seasonal differences. Water Resour. Res. 48, W05530. Wang, W., Xing, W., Shao, Q., Yu, Z., Peng, S., Yang, T., Yong, B., Taylor, J., Singh, V.P., 2013a. Changes in reference evapotranspiration across the Tibetan plateau: observations and future projections based on statistical downscaling. J. Geophys. Res.: Atmos. 118 (10), 4049–4068, http://dx.doi.org/10.1002/jgrd. 50393. Wang, W., Xing, W., Yang, T., Shao, Q., Peng, S., Yu, Z., et al., 2013b. Characterizing the changing behaviours of precipitation concentration in the Yangtze river basin, China. Hydrol. Processes 27 (24), 3375–3393. Wang, W., Yu, Z., Zhang, W., Shao, Q., Zhang, Y., Luo, Y., Jiao, X., Xu, J., 2014. Responses of rice yield, irrigation water requirement and water use efficiency to climate change in China: historical simulation and future projections. Agric. Water Manage. 146, 249–261, http://dx.doi.org/10.1016/j.agwat.2014.08.019. Wang, W., Ding, Y., Shao, Q., Xu, J., Jiao, X., Luo, Y., et al., 2017. Bayesian multi-model projection of irrigation requirement and water use efficiency in three typical rice plantation region of china based on cmip5. Agric. For. Meteorol. 232, 89–105. Xing, W., Wang, W., Shao, Q., Peng, S., Yu, Z., Yong, B., Taylor, J., 2014. Changes of reference evapotranspiration in the Haihe River Basin: present observations and future projection from climatic variables through multi-model ensemble. Global Planet. Change 115, 1–15, http://dx.doi.org/10.1016/j.gloplacha.2014. 01.004. Xu, C., Gong, L., Jiang, T., Chen, D., 2006. Decreasing reference evapotranspiration to key climatic variables in a warming climate – a case of Changjiang (Yangtze River) catchment during 1970–2000. Adv. Atmos. Sci. 23, 513–520. Yang, T., Wang, X., Zhao, C., Chen, X., Yu, Z., Shao, Q., et al., 2011. Changes of climate extremes in a typical arid zone: observations and multimodel ensemble projections. J. Geophys. Res. Atmos. 116 (D19), 1441–1458.

101

Yang, X., Asseng, S., Wong, M.T.F., Yue, Q., Li, J., Liu, E., 2013. Quantifying the interactive impacts of global dimming and warming on wheat yield and water use in China. Agric. For. Meteorol. 182–183, 342–351. Yang, L., Qin, Z., Tu, L., 2015. Responses of rice yields in different rice-cropping systems to climate variables in the middle and lower reaches of the Yangtze River, China. Food Secur. 7 (5), 951–963, http://dx.doi.org/10.1007/s12571015-0497-y. Yano, T., Aydin, M., Haraguchi, T., 2007. Impact of climate change on irrigation demand and crop growth in a Mediterranean environment of Turkey. Sensors 7 (10), 2297–2315, http://dx.doi.org/10.3390/s7102297. Yao, F., Qin, P., Zhang, J., Lin, E., Boken, V., 2011. Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods. Sci. Bull. 56 (8), 729–737. Ye, Q., Yang, X., Dai, S., Chen, G., Li, Y., Zhang, C., 2015. Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China. Agric. Water Manage. 159, 35–44, http://dx.doi.org/10.1016/j. agwat.2015.05.022. Yin, Y., Tang, Q., Liu, X., 2015. A multi-model analysis of change in potential yield of major crops in china under climate change. Earth Syst. Dyn. 6 (1), 45–59. Yu, Z., Pollard, D., Cheng, L., 2006. On continental-scale hydrologic simulations with a coupled hydrologic model. J. Hydrol. 331 (1), 110–124. Yu, Y., Huang, Y., Zhang, W., 2012. Changes in rice yields in China since 1980 associated with cultivar improvement, climate and crop management. Field Crops Res. 136, 65–75, http://dx.doi.org/10.1016/j.fcr.2012.07.021. Yue, S., Wang, C., 2002. Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. Water Resour. Res. 38 (6), 1–4, http://dx.doi.org/10.1029/2001WR000861. Yue, S., Pilon, P., Phinney, B., Cavadias, G., 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Processes 16 (9), 1807–1829, http://dx.doi.org/10.1002/hyp.1095. Zhang, Q., Li, J., Chen, Y., Chen, X., 2011. Observed changes of temperature extremes during 1960–2005 in China: natural or human induced variations? Theor. Appl. Climatol. 106, 417–431. Zhao, H., Yao, F., Zhang, Y., Xu, B., Yuan, J., Hu, Y., Xu, Y., 2007. Correlation analysis of rice seed setting rate and weight of 1000-grain and agro-meteorology over the middle and lower reaches of the Yangtze River China? J. Integr. Agric. 6 (4), 430–436. Zhao, G., Webber, H., Hoffmann, H., Wolf, J., Siebert, S., Ewert, F., 2015. The implication of irrigation in climate change impact assessment: a european-wide study. Global Change Biol. 21 (11), 4031. Zhu, X., Zhao, A., Li, Y., Liu, X., 2015. Agricultural irrigation requirements under future climate scenarios in China. J. Arid Land 7 (2), 224–237, http://dx.doi.org/ 10.1007/s40333-014-0080-y. Zollo, A.L., Rianna, G., Mercogliano, P., Tommasi, P., Comegna, L., et al., 2014. Validation of a simulation chain to assess climate change impact on precipitation induced landslides. In: Sassa, K. (Ed.), Landslide Science for a Safer Geoenvironment, vol. 1. Springer International Publishing, Switzerland.