Responses of crop growth and water productivity to climate change and agricultural water-saving in arid region

Journal Pre-proofs Responses of crop growth and water productivity to climate change and agricultural water-saving in arid region Minghuan Liu, Xu Xu, Yao Jiang, Quanzhong Huang, Zailin Huo, Liu Liu, Guanhua Huang PII: DOI: Reference:

S0048-9697(19)34612-1 https://doi.org/10.1016/j.scitotenv.2019.134621 STOTEN 134621

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Science of the Total Environment

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23 July 2019 16 September 2019 21 September 2019

Please cite this article as: M. Liu, X. Xu, Y. Jiang, Q. Huang, Z. Huo, L. Liu, G. Huang, Responses of crop growth and water productivity to climate change and agricultural water-saving in arid region, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134621

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Responses of crop growth and water productivity to climate change and agricultural water-saving in arid region Minghuan Liu1, 2, Xu Xu1, 2, Yao Jiang1, 2, Quanzhong Huang1, 2, Zailin Huo2, Liu Liu2，Guanhua Huang1, 2* Manuscript submitted to Science of the Total Environment

1

Chinese-Israeli International Center for Research and Training in Agriculture, China

Agricultural University, Beijing 100083, P. R. China 2

Center for Agricultural Water Research, China Agricultural University, Beijing 100083, P. R.

China [email protected] (M. Liu), [email protected] (X. Xu), [email protected] (J. Yao), [email protected] (Q. Huang), [email protected] (Z. Huo), [email protected](L. Liu), [email protected] (G. Huang)

*

Corresponding author: Phone: +86-10-62737144, Fax: +86-10-62737979, Email:

[email protected]

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Abstract Climate change and associated elevated atmospheric CO2 concentration and rising temperature have become a great challenge to agricultural production especially in arid and semiarid regions, and a great concern to scientists worldwide. Thus, it is very important to assess the response of crop growth and water productivity to climate change projections, which in turn can help devise adaptive strategies to mitigate their impact. An agro-hydrological model with well consideration of CO2 effects on both the stomatal conductance and leaf area was established. The model was well calibrated and validated using the data collected from the middle oasis of Heihe River basin, northwest China, which was selected as a typical arid region. Simulations of soil water contents and crop growth matched well with observations. Then various scenarios were designed with considering three climate change alternatives (RCP 2.6, RCP 4.5 and RCP 8.5) and three agricultural water-saving alternatives in the context of irrigation water availability being constant. Responses of crop growth and water productivity were predicted for thirty years from 2018 to 2047. As compared to current situation, there would be a reduction of 3.4%-8.6% in crop yield during the period of 2018-2027 and an increase of 1.5%-18.7% in crop yield during the period of 2028-2047 for seed corn, and an increase of 7.4%- 26.7% in crop yield during the period of 2018-2047 for spring wheat, respectively. Moreover, results showed an increase in water productivity ranged from 14.3% to 44.5% for seed corn and from 34.7% to 52.0% for spring wheat, respectively. Furthermore, adaptive strategies to climate change were recommended for the seed corn and spring wheat, respectively. Our results are expected to provide implications for devising adaptive strategies to changing environments in other arid and irrigation-fed areas. 2

Keywords: elevated CO2 concentration; rising temperature; agricultural water-saving; arid region; water productivity 1. Introduction Climate change is a public concern all over the world (Howden et al., 2007, Piao et al., 2010). It was reported that the Earth’s climate has warmed with an increase in temperature of approximate 0.6 ℃ over the past 100 years (Walther et al., 2002). CO2 concentration is expected to increase from the concentration of 390 ml m-3 (ppm) in 2012 to around 935 ppm till the end of the 21st century (IPCC, 2014). Agricultural production is greatly affected by the changes of climatic factors, such as greenhouse gas concentrations, radiation, precipitation and temperature (Dubey and Sharma, 2018). Warmer climate can extend the length of crop potential growing season with earlier planting time and later harvesting time (Piao et al., 2006; Walther et al., 2002). It can also shorten crop growth period from emergence to maturity, resulting in a decrease in crop yield (Asseng et al., 2018; Asseng et al., 2019; Kheir et al., 2019). Other researchers argued that warming climate is beneficial to irrigated agriculture as they can use water to offset the heat with enough water supplies (Wang et al., 2009). Rain-fed agriculture with cooler climate can also be benefited from the warmer climate. On the other hand, effects of elevated atmospheric CO2 concentration on crop growth seem to be very comprehensive. Experimental evidences showed that the stomatal conductance of some crops (including C4 crop and C3 crop) declines as leaves open less stomata to absorb the necessary amount of CO2 for crop photosynthesis with increasing the atmospheric CO2 concentration, leading to a reduction in transpiration (Gedney et al., 2006; Morison and Gifford, 1983, 1984; Wand et al., 1999). Larger leaf area and thus higher transpiration were also observed corresponding to the increase in atmospheric CO2 concentration (Dubey et al., 2015; Dermody et al., 2006; Wand et al., 1999; Zhu et al., 2017), potentially offsetting the effects of 3

reduction in stomatal conductance. Besides, CO2 itself is also a fertilizer which can promote crop growth. The implementation of agricultural water-saving practices has been widely considered as an effective measure to improve water use efficiency, to mitigate water shortage, and to reduce environmental problems, e.g. over pumping groundwater for irrigation in arid regions and/or those regions with severe water shortage (Cheng et al., 2014; Deng et al., 2006; Hu et al., 2010; Kang et al., 2008). However, precise quantifying the effects of climate change and agriculturalwater saving strategies on crop growth and water productivity still remained a great challenge for both decision makers and researchers. Simulation models, such as DSSAT (Ngwira et al., 2014), EPIC (Bhattarai et al., 2017), RZWQM (Ma et al., 2017), AquaCrop (Farahani et al., 2009), CERES-Maize (Johnston et al., 2015), CERES-Wheat (Luo et al., 2005), SWAP (Kroes and van Dam, 2003), SWAP-EPIC (Xu et al., 2013), etc. mainly including agro-hydrological models and crop models were increasingly used as an effective and low-costing way to evaluate the responses of crop growth and water use efficiency to agricultural water-saving strategies and more complex changes of climatic factors. However, the effects of elevated atmospheric CO2 concentration on stomatal conductance and leaf area were less considered in the simulations due to either the limitation of models’ function and/or experiment data (Eckhardt and Ulbrich, 2003), which may bring in great uncertainties when assessing the impacts of climate change with atmospheric CO2 concentration being one of the main factors. Besides, great uncertainties also lie in the modeling results, which are closely related to climate projections of different General Circulation Models (GCMs) or regional climate models (RCMs) (Kang et al., 2009) and the latitude of the area, irrigation application, agrotechnology or other human activities.

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Therefore, exploring and precisely quantifying the effects of both climate change and agricultural water-saving on crop growth and water productivity are both crucial and challenging. The objectives of the paper are: (1) to establish an agro-hydrological model that could well simulate crop growth with well consideration of the CO2 effects on both stomatal conductance and leaf area; (2) to precisely quantify the responses of crop growth and water productivity to climate change and agricultural water-saving with more reliable climate change projections; (3) to devise appropriate adaptive strategies to the climate change and agricultural water-saving. Two main crops, i.e. seed corn representing C4 crop and spring wheat representing C3 crop, respectively were chosen for the evaluation study. 2. Materials and methods 2.1. Agro-hydrological model 2.1.1 Model description and modification The Agro-Hydrological & chemical and Crop Systems simulator, i.e. the AHC model was developed by Xu et al., (2018) and used to simulate the soil water flow in vadose zone and crop growth at field scale and daily time-step. The 1-D Richards’ equation was adopted to describe vertical soil water flow and solved by the finite-difference method. The upper boundary could be defined by the actual evaporation and transpiration rates, the irrigation and precipitation fluxes. The bottom boundary was defined by the first, second and third boundary conditions, according to the practical need. The EPIC crop growth model was used to simulate crop growth and yield based on the accumulated temperature (Williams et al., 1989). It could consider leaf area development, light interception, and the conversion of intercepted light into biomass and yield together with effects of temperature, water and salt stress. The AHC model has been well tested in the upper reaches of the Yellow river basin and the North China Plain. Modeling results showed that the AHC model could well simulate the soil water contents and crop growth (i.e. leaf 5

area index, dry aboveground biomass, evapotranspiration and yield) for both corn and wheat (Xu et al., 2018). To investigate the impact of climate change, we further extended the function of the AHC model to simulate the responses of crop growth to elevated atmospheric CO2 concentration. The effects of CO2 on the stomatal conductance and leaf area index can be expressed as (Wu et al., 2012): gl ,CO2  gl *[(1  p)  p *

CO 2 ] 330 ,

LAI max,CO2  LAI max *[(1  q )  q *

(1) CO 2 ] 330 ,

(2)

where g l ,CO 2 is the modified stomatal conductance to reflect the effects of CO2 (mmol m-2 s-1); gl is the stomatal conductance with the atmospheric CO2 concentration being 330 ppm (mmol m-2 s1

); p is the percentage decrease in leaf stomatal conductance (-), here p=0.4 for wheat and p=0.26

for corn (Li et al., 2018); CO2 is the atmospheric CO2 concentration (ppm); LAI max,CO2 is the modified maximum leaf area index to reflect CO2 effects (m2 m-2); LAI is the maximum leaf max

area with the atmospheric CO2 concentration being 330 ppm (m2 m-2); q is the percentage increase in leaf area index (%), and q=0.4 (Wu et al., 2012). 2.2 Water saving and climate change scenarios 2.2.1 Scenarios design In general, the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) scenarios, RCP 2.6, RCP 4.5, and RCP 8.5, are chosen as the future climate change alternatives. The RCP scenarios were named according to the radiative forcing target level for 2100. RCP2.6, RCP4.5 and RCP8.5 represent the mitigation scenario, medium stabilization scenario and high baseline emission scenario, respectively, based on the forcing of greenhouse gases and other forcing agents (Van Vuuren et al., 2011). Water saving

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strategies are usually developed through reducing the irrigation amount without inducing significant negative effects on crop growth. 2.2.2 Scenario forcing data Crop cultivation data, irrigation scheduling data and meteorological data are necessary to drive the model. Crop cultivation data and irrigation scheduling data can be collected from previous surveys and set to be constant during the simulation. Meteorological data used for evaluation mainly included precipitation, air temperature, solar radiation, relative humidity and wind speed. Projected weather data could be either obtained from the statistical downscaling model (SDSM) and/or dynamical downscaling model (DDSM) for the future years, which could be defined by the researchers based on their personal need. However, the obtained projected data could not be directly used as the future forcing data for the agro-hydrological model. Thus, the projected weather data are further modified based on the collected observed historical data at meteorological stations with the modification process being described as: X  X obs  (

X rcp ,i  X base X base

) X obs ,

(3)

where X is the modified meteorological data, representing minimum temperature, maximum temperature, wind speed and precipitation; Xobs is the projected future weather data produced by SDSM; Xbase is the observed historical weather data, i represents 2.6, 4.5 or 8.5, respectively; Xrcp,i is the projected weather data corresponding to the observed historical period produced by SDSM or DDSM or for i RCP scenario. Besides, the modified precipitation could not be directly used to drive the agro-hydrological model. Precipitation produced by SDSM or DDSM is the expectation for each day based on the probability density function. Precipitation in each day (mostly <1 mm) is far below the historical precipitation. Thus, the precipitation data were further modified based on the assumptions that the

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regression relationships developed between the historical observed data and GCM factors in the SDSM model is still useful in a changed climate (Karl et al., 1990; Wilby et al., 2002). Thus, yearly precipitation can be allocated to each day assuming that precipitation in the climate change scenarios is the same as the historical precipitation observed at local metrological stations as the yearly projected precipitation is equal to or approached the observed one. Historical precipitation should be collected covering the extremely wet years, wet years, normal years, dry years and extremely dry years. Solar radiation is calculated using the Hargreaves’ radiation formula suggested by Allen et al. (1998). Humidity is calculated as follows (Allen et al., 1998): ea  eo (Tdew )  0.611exp[

17.27Tdew ] Tdew  237.3

,

(4)

where ea is the actual vapour pressure (kPa); eo(Tdew) is the vapour pressure for temperature Tdew (kPa); Tdew is the temperature at dewpoint (oC); Tdew is estimated based on the empirical linear relationship obtained from the historical dewpoint temperature and minimum temperature. Atmospheric CO2 concentration for three representative scenarios (RCP2.6, RCP4.5 and RCP8.5) are obtained from the projections provided in the IPCC fifth assessment report (IPCC, 2014) as shown in Fig. 1. 2.3 Case study 2.3.1 Location, water use, climate and cropping pattern The Heihe middle oasis (38.6°-39.8° N, 99.2°-100.8° E), located in the middle reach of Heihe River basin (HRB) (see Fig. 2) was chosen as the case study area. It is one of the most important cereal and seed production bases in northwest China. It has the most intensive agriculture in the basin. Simultaneously, it has a typical continental climate with the annual precipitation being about 130 mm and pan evaporation (20 cm) being about 2050 mm,

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respectively. Thus, agriculture in this basin greatly depends on the irrigation, which has consumed nearly 90% of the available water. The Ecological Water Diversion Project (EWDP) has been implemented by the local authority since 2000 aiming at decreasing the surface water allocation to middle HRB and restoring the ecosystems in downstream. However, excess irrigation was still very common in this area with the over-exploitation of groundwater for irrigation, which has caused the continuous decline of groundwater level in the main groundwater exploitation areas (Mi et al., 2015; Zhou et al., 2011). This would further affect the natural ecosystems in this area. Thus, implementing water-saving practices is urgent and essential to the sustainable development of agriculture and ecosystems. There were four main crops in this area (Fig. 2), i.e. corn (including seed corn and field corn), spring wheat and cash crop (mainly including cotton and vegetable), accounting for 73.75%, 7.41% and 18.84% of the total area of farmlands, respectively. Soil in the seed corn areas was usually covered with white plastic film (about 50%) to maintain the soil moisture and heat. Corn was usually irrigated four times while spring wheat was irrigated three times during the growing season, which corresponded to April to September for corn and April to July for wheat, respectively. Detailed irrigation events and quantities can be seen in Table 1. Surface (basin) irrigation was the common method. Irrigation amount ranged from 400-700 mm with relative large variations in the middle oasis. Relative high irrigation amounts were distributed either along the main stream of the Heihe River or at the head of the canal systems with convenient water diversion conditions while lower irrigation amounts were distributed far away from the Heihe River or at the tail of the canal systems. Farmlands were also irrigated in winter (usually November) or spring (usually March) before sowing so that soil water contents could be maintained at an appropriate value for the coming seeding season. There were two main soil types in cultivated farmlands in the middle oasis, i.e. silt loam and loam (Xu et al., 2019). 9

Coarse-textured soils were also typical in northeast area of downstream basin due to the Badain Jaran Desert. Climate in the middle oasis of Heihe River basin has become warmer over the past 52 years (year 1960-2011), especially after 1995 (Fig. 3). Yearly minimum temperature has risen from 0.005 ℃ in the year 1960-1985 to 1.133 ℃ in the year 1986-2011 while yearly maximum temperature has risen from 15.489℃ in the year 1960-1985 to 16.379 ℃ in the year 1986-2011. A 1.024 ℃ increase was also observed in the yearly average temperature compared to that in the year 1960-1985. Yearly wind speed has declined from nearly 2.5 m s-1 in the 1960s and 1970s to less than 2m s-1 in the next 30 years. There was also a slight increase in the amount of precipitation. By the end of the 2012, annual precipitation was still less than 150 mm, indicating a typical dry climate. Besides, groundwater depth in the farmland areas ranged from less than 2 m to more than 30 m. Farmland areas with shallow groundwater depth were mainly distributed along the mainstream of the Heihe River. Groundwater depth in most farmland areas was larger than 3 m (Liu et al., 2018). 2.3.2 Model calibration and validation The extended AHC model was applied at two specific sites with one representing the field of seed corn (Zea mays L, Jinkai No. 3), and another representing the field of spring wheat (Triticum aestivum L, Longchun No. 30) (see Fig. 2). 0-300 cm was adopted as the the simulation depth. The simulation depth was further discretized into 104 layers, including 40 layers with 1 cm depth (i.e. 0-40 cm), 20 layers with 2 cm depth (i.e. 40-80 cm) and 44 layers with 5 cm depth (i.e. 80300 cm). Soil layers of depth ranging 0-140 cm shared the same soil properties while soil layers of depth ranging 140-300 cm shared another set of soil properties based on our previous field experiments (Jiang et al., 2015). Soil water contents, LAI, aboveground biomass and crop heights

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for seed corn collected from our previous field experiments during the growing season in 2013 and 2012 (Jiang et al., 2015) were used to calibrate and validate the parameters related to soil hydraulic properties and seed corn growth, respectively. LAI, aboveground biomass and crop heights for spring wheat in 2013 (Jiang et al., 2015) at two sites were used to calibrate and validate the parameters related to spring wheat growth. Additionally, experimental data in 2009 collected from previous literature (Li et al., 2012) were also used to validate the parameters related to seed corn growth. And experimental data in 2014 and 2015 in the nearby Shiyang River basin collected from previous literature (Yang et al., 2018) were also used to validate the parameters related to spring wheat growth. Soil hydraulic parameters are first obtained using the pedotransfer functions (ROSETTA, Schaap et al., 2001) combining with soil physical and othe relevant properties (e.g. soil texture compisition, organic content, bulk density, etc.), which are measured directly with soil samples and/or obtained from previous literature. Parameters referring to crop growth are obtained from the corresponding default values of the EPIC model (Williams et al., 1989). All these parameters are then adjusted through the processes of model calibration and validation. The root mean squared error (RMSE), mean relative error (MRE) and coefficient of determination (R2) are used as indicators of goodness of fit (Legates and McCabe, 1999; Moriasi et al., 2007). Their definitions can be referred to Xu et al. (2013). 2.3.3 Simulations under changing environment Agricultural water-saving alternatives were developed based on the improved irrigation schedule in our previous research (Xu et al., 2019). Massive simulations were firstly conducted for various irrigation amounts using the SWAP-EPIC model (Xu et al., 2013). Crop yields, actual evapotranspiration and deep percolation was calculated and used as indicators to select optimized irrigation schedule. For the alternative with improved irrigation schedule (1.0IR), crops yields 11

could be maintained equal to that in year 2012 with appropriate ratio of actual evapotranspiration to potential evapotranspiration (>0.9) and appropriate ratio of deep percolation to the sum of irrigation and effective precipitation. Two more agricultural alternatives were thus developed as references, i.e., a decrease of 20% (0.8IR) and an increase of 20% (1.2IR) in irrigation amount, respectively. Alternative 0.8IR was set up to further explore the agricultural water-saving potential, while alternative 1.2IR was set up as an reference in which the crop could be fully irrigated to meet its crop water requirement without water stress. RCP2.6, RCP4.5 and RCP8.5 were adopted as the climate change scenarios. Thus nine scenarios were developed by grouping the climate change alternatives and agricultural water-saving alternatives. Detailed information is shown in Table 2. The statistical downscaling model (SDSM) was used to produce the projected weather data (including minimum temperature, maximum temperature, wind speed and precipitation) for historical period from 1986 to 2015, and the future years from 2018 to 2047, and the optimal modeling results were selected from twenty-five general circulation model (GCM) outputs based on the score-based method to avoid the uncertainties induced by different GCM projections in our previous research (Wang et al., 2019). Crop cultivation data and irrigation scheduling data were collected from our previous surveys (Jiang et al., 2015) and set to be constant during the simulation. Additionally, irrigation water availability from both surface water and groundwater remained the same during the simulation. 3. Results and discussion 3.1. Agro- hydrological model calibration and validation The simulated and observed soil water contents (SWCs) for six layers, biomass, crop heights and leaf area index for seed corn and spring wheat are shown in Figs. 4-6, respectively. Whereas, the indicators of goodness of fit (MRE, RMSE, and R2) for both calibration and validation are 12

summarized and shown in Table 3. It could be found that fluctuations of SWCs were well captured by the agro-hydrological model with MRE ranging from -7.2% to 1.8%, RMSE ranging from 0.020 to 0.035 cm3 cm-3, and R2 ranging from 0.672 to 0.826, respectively during the calibration period (Fig. 4a). Additionally, the simulated SWCs for the validation were also in good agreement with the observed ones with MRE less than 1.6%, RMSE less than 0.051 cm3 cm-3 and R2 larger than 0.549. As shown in Figs. 5-6 and Table 3, the simulated crop growth index (i.e. crop height, leaf area index and aboveground biomass) matched well with the observed ones. The MRE values were less than 30% except that for seed corn height (49.6%) in calibration period and spring wheat biomass (78.0%) in 2015. R2 was larger than 0.92 except that for seed corn LAI (0.750) in validation period and spring wheat LAI (0.433) in 2012 during the validation period. Besides, RMSE was within acceptable ranges. Moreover, the simulated seed corn yields also matched well with the observed ones with

RMSE less than 1800 kg ha-1. This implies the calibrated and

validated agro-hydrological model should be reasonable and convincing to simulate soil water flow, seed corn and spring wheat growth, and hence could be used as a promising tool to assess the response of crop growth and water productivity to climate change and agricultural watersaving. 3.2. Scenario analysis on projected climate changes and agricultural water-savings For the case of comparison, in the following analyses, the projected period of 2018-2047 was divided into three sub-periods with a length of 10 years, corresponding to the years of 20182027, 2028-2037, and 2038-2047, respectively. Each projected results i.e. crop yield, evapotranspiration and water productivity, were averaged over the sub-period. Whereas, the years of 2003-2012 were selected as the base period, and the correspondingly simulated results with the measured data were used as the references for comparison. 13

3.2.1 Crop yield and actual evapotranspiration The simulated evapotranspiration (ETa) and crop yields for seed corn and spring wheat for all scenarios are presented in Figs. 7 and 8, respectively. As compared with the references, ETa was reduced by 7.4%-20.2% for spring wheat (see Fig.7b) and 15.0%- 21.8% for seed corn (see Fig.7a) during the period of 2018-2047. The reduction in ETa is mainly attributed to the elevated atmospheric CO2 concentration, resulting in a reduction in stomatal conductance, similar result was also obtained by climatic phytotron experiments (Li et al., 2018; Li et al., 2019; Morison and Gifford, 1983, 1984). Additionally, severe reduction in ETa for spring wheat might be due to that the stomatal conductance of spring wheat is more sensitive to the atmospheric CO2 concentration. Increase in ETa was also found for the period of 2028-2047 as compared with that in 2018-2027 due to the rising temperature. Under the improved irrigation schedule (1.0IR), the simulated yields of two crops during the period 2018-2047 were higher than their references (see Fig. 8), with exception of seed corn yield in 2018-2027 being lower than its reference. This might be attributed to that the negative effects of reduction in stomatal conductance and rising temperature were greatly offset by the positive effects of elevated atmospheric CO2 concentration on Radiation-use efficiency (RUE) and crop leaf area. Besides, the largest yield was found for scenario RCP8.5-1.0IR during the period of 2038-2047, which was about 18.7% (for seed corn) and 26.7% (for spring wheat) higher than the references, respectively. The different yield change pattern of C3 and C4 crops might be attributable to their difference in photosynthetic pathway (Farquhar et al., 1989; Furbank and Hatch, 1987; Jenkins et al., 1989; Reich et al., 2018; Yamori et al., 2014). Increasing atmospheric CO2 concentration can increase the intercellular CO2 concentration for C3 crop (e.g. spring wheat), which results in a higher increasing rate of photosynthesis for C3 crop than that for C4 crop (e.g. seed corn). 14

Crop yield and ETa are different for different RCP scenarios, the highest yield and lowest ETa for both crops were found with the scenario of RCP8.5, whereas the scenario of RCP2.6 had the lowest yield and highest ETa. This might be attributed to that among the three scenarios, RCP8.5 has the highest temperature with its minimum air temperature 10.4 oC and RCP2.6 has the lowest temperature with its minimum air temperature of 9.7 oC during seed corn growing season for the period of 2028-2037. Whereas, RCP8.5 has the highest atmospheric CO2 concentration with the average value of 459.2 ppm, while RCP2.6 has the lowest atmospheric CO2 concentration with the average value of 432.9 ppm for the period of 2028-2037. Warmer climate was likely to decrease the crop yield by 6%-20% per oC in China (Tao et al., 2008). In contrast, the radiation of scenario RCP8.5-1.0IR was lower than those for scenarios RCP2.6-1.0IR and RCP4.5-1.0IR, respectively. Decreasing radiation was believed to decrease both the crop yields and ETa. However, those negative effects of rising temperature and decreasing radiation on crop yields were all offset by the positive effects of elevated atmospheric CO2 concentration on radiation-use efficiency. Whereas, the positive effects of rising temperature were offset by the reduction in stomatal conductance and radiation. As a result, the differences for both crop yields and ETa among the three RCP scenarios both reached their maximum value during the period of 20382047. Besides, increments in seed corn yields from RCP2.6-1.0IR to RCP8.5-1.0IR were larger than those of spring wheat yields due to the aforementioned difference in photosynthetic pathway. Agricultural water-saving strategy (a reduction of 20% in irrigation amount, 0.8 IR) had a slight impact on yield of both crops. The largest difference for seed corn yield was found between scenario RCP8.5-1.0IR and scenario RCP8.5-0.8IR with a value of 200.0 kg ha-1, revealing a reduction of 1.5% for water-saving strategy. The reduction of spring wheat yield for RCP-0.8IR scenarios was also less than 1% (Fig. 8b). Besides, a slight difference was also found for ETa among scenarios RCP-1.0IR and RCP-0.8IR for both crops. This might be attributed to that crop 15

water requirements were greatly reduced by the reduction in stomatal conductance induced by elevated atmospheric CO2 concentration. Thus less irrigation water was required to meet crop water use, causing the negative effects of the reduction in irrigation amount on yield and ETa can be neglected. No obvious improvement in yield for both crops could be found for the 1.2IR alternative (i.e. an increase of 20% in irrigation amount). This might be due to that the improved irrigation schedule (1.0IR) could already satisfy crop water requirements in current situation. Additionally, extra irrigation might cause negative effects on the crop yield, e.g. spring wheat yield for RCP8.5-1.2IR during the period of 2018-2027 was reduced by 0.8% as compared with that for RCP8.5-1.0IR. 3.2.2 Water productivity Water productivity (WP) was defined as the ratio of crop yield to ETa (Jiang et al., 2015). Simulated WPs for seed corn and spring wheat of all scenarios are shown in Figs. 9a and 9b, respectively. Modeling results showed that the WPs for both crops were significantly increased for the RCP scenarios during the period of 2018-2047. As compared with the references, WPs have increased by 14.3%-44.5% for seed corn (Fig. 9a) and 34.7%-52.0% for spring wheat (Fig.9b), respectively. This might be mainly attributed to the significant increase in crop yield. Whereas, there was also slight increase in ETa. The increments of ETa were less than 3%, which was much lower than that of crop yield. WPs increased significantly from RCP2.6 scenarios to RCP8.5 scenarios. The largest WPs were found 2.53 kg m-3 (for seed corn) and 2.63 kg m-3 (for spring wheat) in RCP8.5-1.0IR scenario during the period of 2038-2047. It could be attributed to that the significant increase in crop yield and slight decrease in ETa. Besides, agricultural water-saving strategy had a slight

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impact on WPs. It could be attributed to the insufficient response of crop yield and ETa to the reduced irrigation amount (i.e. 0.8IR alternative). 3.3 Insights on the adapations to climate change and agricultural water-saving The impact of elevated atmospheric CO2 concentration on crop production is complex, increasing or decreasing crop yield are strongly related to the level of atmospheric CO2 concentration and crop photosynthetic pathway of C3 (spring wheat) and C4 crop (seed corn). Meanwhile, with long term exposure to elevated atmospheric CO2 concentration, responses of crop production are less pronounced as crop acclimates. Thus, the actual increase in crop production during the projection period might be lower than the projected ones. Rising temperature might result in a decrease in crop production. Besides, climate-sensitive pests and diseases were reported to move northward in north China (Piao et al., 2010), which might cause a loss in crop production. Thus, developing new varieties that are more adaptive to the elevated atmospheric CO2 concentration and more tolerant to rising temperature is very essential to satisfy the increasing seed demand and thus to ensure crop production. Resistance to the pests and diseases should also be considered in the future crop breeding. Moreover, implementing the agricultural water-saving practices is also very important to ensure the irrigation water availability in the future. Policies and laws should also be promoted to slow down the increasing rate of both atmospheric CO2 concentration and temperature. 4. Conclusion The AHC model (Agro-Hydrological & chemical and Crop Systems simulator) with well consideration of CO2 effects on both the stomatal conductance and leaf area development was successfully established for both the C3 crop (spring wheat) and C4 crop (seed corn) in and arid region, i.e. the middle oasis of Heihe River basin, northwest China. The model was well calibrated and validated using the collected field experiment data of soil water and crop growth. 17

Three climate change scenarios (i.e. RCP2.6, RCP4.5 and RCP8.5) combined with three agricultural water-saving alternatives (i.e. 0.8IR, 1.0IR and 1.2IR) were developed and applied for evaluating the response of crop growth and water productivity to climate change and agricultural water-saving for the future thirty years (2018-2047) using the AHC model. Elevated atmospheric CO2 concentration and rising temperature have strong impact on yield, evapotranspiration (ETa) and water productivity (WP) of both crops. For seed corn, a reduction of 3.4%-8.6% in crop yield was found during the period of 2018-2027, and an increase of 1.5%-18.7% in crop yield was found during the period of 2028-2047. Whereas, spring wheat had a significant increase of 7.4%-26.7% in crop production during the period of 2018-2047. ETa was reduced about 7.4%-20.2% for spring wheat and 15.0%- 21.8% for seed corn during the period of 20182047. Additionally, WP for both crops was significantly increased by the elevated atmospheric CO2 concentration and rising temperature. Agricultural water-saving strategy (0.8 IR) had a slight impact on yield, ETa and WP for both crops. A reduction of 20% in irrigation amount was recommended for spring wheat. Developing new varieties was quite needed for both crops to adapt to the climate change and agricultural water-saving. In summary, this paper quantitatively evaluated the response of crop growth and water productivity to climate change and agricultural water-saving and provided possible strategies to adapt to the changing environments. It should also be useful to other arid regions facing the similar situations. Besides, the effects of climate-sensitive pests and diseases should be further investigated in the follow-up researches. Acknowledgements This research was jointly supported by the National Natural Science Foundation of China (grant numbers: 51639009 and 51679235). We especially acknowledge Cold and Arid Region Science

18

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Fig. 5 Comparison between the simulated (lines) and observed (points) leaf area index, crop height and biomass for seed corn during calibration period (a) and validation period (b). Fig. 6 Comparison between the simulated (lines) and observed (points) leaf area index, crop height and biomass for spring wheat during calibration period (a) and validation period (b). Fig. 7 Simulated actual evapotranspiration (ETa) for seed corn (a) and spring wheat (b) for all scenarios, red solid points were the average ETa in the years of 2003-2012. Fig. 8 Simulated crop yields for seed corn (a) and spring wheat (b) for all scenarios, red solid points were the average yields in the years of 2003-2012. Fig. 9 Water productivity (WP) for seed corn (a) and spring wheat (b) for all scenarios, red solid points were average WP in the years of 2003-2012. Table 1 Irrigation events and quantities for seed corn and spring wheat for year 2013 Crop

Seed corn

Spring wheat

Date 06/06 06/29 07/30 08/24 05/02 06/01 06/30

Irrigation amount (mm) 150 147 163 171 100 140 160

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Table 2 Scenarios of climate change and agricultural water-saving Climate change alternatives

Agricultural water-saving alternatives

RCP 2.6

IR: Improved irrigation schedule

RCP 4.5

×

0.8IR: Improved irrigation schedule decreases 20%

RCP 8.5 1.2IR: Improved irrigation schedule increases 20% Note: RCP is the Representative Concentration Pathway; basin irrigation was also adopted in the simulations.

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Table 3 Indicators of goodness of fit for soil water content (SWC), seed corn and spring wheat growth during the calibration period and validation period. Calibration period Seed corn Indicators of goodness of fit MRE RMSE

SWC (cm3 cm-3) -7.2% - 1.8% 0.020 - 0.035

LAI 2.10% 0.577

Height (cm) 49.6% 35.7

R2

0.672 - 0.826

0.954

0.978

Spring wheat Biomass (kg ha-1) 2.6% 2567 0.942 Validation period

LAI 29.4% 0.278 0.966

Seed corn

SWC(cm3 cm-3)

Height (cm) -6.10% 5.5 0.92

Biomass (kg ha-1) 18.10% 1591.1 0.945

Spring wheat

Height (cm) 22.2%

Biomass (kg ha-1) -3.6%-2.1%

LAI Height (cm) 0.1%-7.2% 0.80%

Biomass (kg ha-1) 14.60%-78.0%

MRE

-4.2% - 1.6%

LAI -25.0%-1.0%

RMSE

0.023 - 0.041

0.575-0.965

24.4

1803-2366

0.25-0.76

5.2

1900.7-2322.7

R2

0.549 - 0.720

0.750-0.939

0.951

0.983-0.989

0.433-0.975

0.953

0.932-0.993

Note: SWC is the soil water content; LAI is the leaf area index

30

615

Fig. 1

Atmospheric CO2 concentration (ppm)

540

520

500

480

460

440

420

400 2015

616

RCP2.6 RCP4.5 RCP8.5

2020

2025

2030

2035

Time (year)

32

2040

2045

2050

617

Fig. 2

618

33

619

Fig. 3

4

10

2 1 0 -1

1970

1980

1990

2000

6

2010

1960

Time (Year)

18

1970

1980

1990

2000

2010

Time (Year)

250

(d)

(c)

T_max

17

200 Precipitation (mm)

Temperature (oC)

7

4 1960

16 15 14

150 100 50

13 12

0 1960

1970

1980

1990

2000

2010

1960

Time (Year) (e) 3.5 -1

3.0 2.5 2.0 1.5 1.0 1960

1970

1980

1990

1970

1980

1990

Time (Year)

4.0

Wind speed (m s )

8

5

-2

620

(b)

T_ave

9 Temperature (oC)

3 Temperature (oC)

(a)

T_min

2000

2010

Time (Year)

34

2000

2010

622

623 Time (date)

(a)

35 Time (date)

13

-1

-9 -1

-8 -1

-1 0

13

13

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-7 -1

-6 -1

13

1

1

1

1

1

-1

-9 -

-8 -

-7 -

-6 -

-1 0

13

13

13

13

-5 -

1

Time (date)

13

13

60-80 cm

13

-4 -

20-40 cm

-5 -1

-4 -1

-1

-9 -1

-8 -1

-7 -1

-6 -1

-5 -1

-4 -1

-1 0

13

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13

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13

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13

13

13 -

-9

-8

-7

-6

-5

10

13

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13

-4

1

1

1

1

1

1

-1

-1

-1

-1

-1

-1

-1

-1

-9 -

-8 -

-7 -

-6 -

-5 -

-4 -

-1 0 13

13

13

13

13

13

13

13 0-10 cm

13

-1

-1

-1

-1

01

-9

-8

-7

-6

-1

13

13

13

13

13

13

.4

-1

-1

.4

-5

-4 .4

13

13

-3 Soil water content (cm3cm )

621 Fig. 4 .5

10-20 cm

.3

.2

0.0 .1

.5 Time (date)

40-60 cm

.3

.2

.1

0.0

.5 Time (date)

80-110 cm

.3

.2

0.0 .1

Observed soil water content (cm3cm-3)

.5

.4

.3

.2

.1

0.0 0.0

624 625

.1

.2

.3

.4

Simulated soil water content (cm3cm-3) (b)

36

.5

Fig. 5 200

4 3 2 1

150 100 50

20000 15000 10000 5000

0 13-4-1 13-5-1 13-6-1 13-7-1 13-8-1 13-9-113-10-1

0 13-4-1 13-5-1 13-6-1 13-7-1 13-8-1 13-9-113-10-1

0 13-4-1 13-5-1 13-6-1 13-7-1 13-8-1 13-9-113-10-1

Time (date)

Time (date)

Time (date)

627

(a) 200

4 3 2 1

-1

5

25000

Biomass (kg ha )

6

Crop height (cm)

Leaf area index(cm3cm-3)

628

629

-1

5

25000

Biomass (kg ha )

6

Crop height (cm)

Leaf area index(cm3cm-3)

626

150 100 50

20000 15000 10000 5000

0 12-4-1 12-5-1 12-6-1 12-7-1 12-8-1 12-9-112-10-1

0 12-4-1 12-5-1 12-6-1 12-7-1 12-8-1 12-9-112-10-1

0 12-4-1 12-5-1 12-6-1 12-7-1 12-8-1 12-9-112-10-1

Time (date)

Time (date)

Time (date)

37

631

-1

4 3 2 1

-1

Observed seed corn yield (kg ha )

5

Biomass (kg ha )

Leaf area index(cm3cm-3)

630

25000

6

20000 15000 10000 5000

0 09-4-1 09-5-1 09-6-1 09-7-1 09-8-1 09-9-109-10-1

0 09-4-1 09-5-1 09-6-1 09-7-1 09-8-1 09-9-109-10-1

Time (date)

Time (date)

(b)

38

16000 14000 12000 10000 8000 6000 4000 4000

6000

8000

10000 12000 14000 16000 -1

Simulated seed corn yield (kg ha )

Fig. 6

5

80

4 3 2 1 0 13-3-1

13-4-1 13-5-1

13-6-1 13-7-1

60 40 20 0 13-3-1

13-8-1

Time (date)

633

13-4-1 13-5-1

13-6-1 13-7-1

20000 15000 10000 5000 0 13-3-1

13-8-1

13-4-1 13-5-1

13-6-1 13-7-1

13-8-1

Time (date)

Time (date)

(a) 100

4 3 2 1 0 13-3-1

13-4-1 13-5-1

13-6-1 13-7-1

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13-8-1

Biomass (kg ha )

5

25000

80

-1

6

Crop height (cm)

2

-2

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634

635

25000 -1

100

Biomass (kg ha )

6

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632

60 40 20 0 13-3-1

13-4-1 13-5-1

13-6-1 13-7-1

Time (date)

39

13-8-1

20000 15000 10000 5000 0 13-3-1

13-4-1 13-5-1

13-6-1 13-7-1

Time (date)

13-8-1

-1

Biomass (kg ha )

Leaf area index(cm3cm-3)

25000

8 6 4 2 0 14-4-1

14-5-1

10000 5000 0 14-3-1

14-8-1

8

14-6-1 14-7-1

14-8-1

25000 -1

6 4 2 0 15-4-1

14-4-1 14-5-1

Time (date)

Biomass (kg ha )

Leaf area index(cm3cm-3)

638

14-7-1

15000

Time (date)

636

637

14-6-1

20000

15-5-1

15-6-1

15-7-1

20000 15000 10000 5000 0 15-3-1

15-8-1

Time (date)

15-4-1 15-5-1

15-6-1 15-7-1

Time (date)

(b)

40

15-8-1

640

641 R C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4.5- IR C P 1 .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8.5- IR C P 8 1 .0 .5 IR -1 .2 IR R C P R 2 .6 C -0 P .8 R 2 .6 IR C -1 P .0 R 2.6- IR C P 1 .2 R 4 .5 IR C -0 P .8 R 4 .5 IR C -1 P .0 R 4.5- IR C P 1 .2 R 8 .5 IR C -0 P .8 R 8 . 5 IR C -1 P8 .0 .5 IR -1 .2 IR R C P R 2 .6 C -0 P .8 R 2 .6 IR C -1 P .0 R 2 . 6 - IR C P 1.2 R 4 .5 IR C -0 P .8 R 4 .5 I R C -1 P .0 R 4 .5 - IR C P 1.2 R 8 .5 IR C -0 P .8 R 8 .5 IR C -1 P8 .0 .5 IR -1 .2 IR

ETa (cm)

639 Fig. 7 80

Year 2018-2027 Year 2028-2037

(a)

41

Year 2038-2047

60

40

20

0

642

643 R C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6- IR C P 1 .2 R 4.5 IR C -0 P .8 R 4.5- IR C P 1 .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8.5- IR C P 8 1 .0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6- IR C P 1 .2 R 4.5 IR C -0 P .8 R 4.5 IR C -1 P .0 R 4.5- IR C P 1 .2 R 8.5 IR C -0 P .8 R 8.5 IR C -1 P8 .0 . 5 IR -1 .2 IR R C P R 2 .6 C -0 P .8 R 2 .6 IR C -1 P .0 R 2.6- IR C P 1.2 R 4 .5 IR C -0 P .8 R 4 .5 IR C -1 P .0 R 4.5- IR C P 1.2 R 8 .5 IR C -0 P .8 R 8 .5 IR C -1 P8 .0 .5 IR -1 .2 IR

ETa (cm) 50

Year 2018-2027 Year 2028-2037

(b)

42

Year 2038-2047

40

30

20

10

0

C P R 2.6 C -0 P .8 R 2.6- IR C P 1. 0 R 2.6 IR C -1 P .2 RC 4.5- IR P 0. 8 R 4.5 IR C -1 P .0 R 4.5 IR C -1 P .2 R 8.5- IR C P 0. 8 R 8.5 IR C -1 P8 .0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 R 2.6- IR CP 1 .0 R 2.6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4.5- IR C P 1. 0 R 4.5 IR C -1 P .2 R 8.5- IR C 0 P .8 R 8.5 IR C -1 P8 .0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 RC 2.6- IR P 1. 0 R 2.6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4.5- IR C P 1. 0 R 4.5 IR C -1 P .2 R 8.5- IR CP 0 .8 R 8.5 IR C -1 P8 .0 .5 IR -1 .2 IR

R

-1

Yield (kg ha )

644

646 Fig. 8 16000

14000

Year 2018-2027 Year 2028-2037

645

(a)

43

Year 2038-2047

12000

10000

8000

6000

4000

2000

0

647

648 C P R 2.6 C -0 P .8 R 2.6- IR C P 1. 0 R 2.6 IR C -1 P .2 R 4.5- IR C P 0. 8 R 4.5 IR C -1 P .0 R 4.5- IR C 1 P .2 R 8.5 IR C -0 P .8 R 8.5- IR C P8 1 . 0 .5 IR -1 .2 IR R C P R 2.6C P 0.8 R 2.6 IR C -1 P .0 R 2.6- IR C P 1.2 R 4.5 IR C -0 P .8 R 4.5- IR C 1 P .0 R 4.5 IR C -1 P .2 R 8.5- IR C P 0.8 R 8.5 IR C -1 P8 . 0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6 IR C -1 P .2 R 4.5- IR C P 0.8 R 4.5 IR C -1 P .0 R 4.5- IR C 1 P .2 R 8.5 IR C -0 P .8 R 8.5 IR CP -1 8. .0IR 51. 2I R

R

-1

Yield (kg ha ) 12000

10000

Year 2018-2027 Year 2028-2037

(b)

44

Year 2038-2047

8000

6000

4000

2000

0

C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2 .6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4.5 IR C -1 P .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8.5 IR C -1 P 8 .0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6 IR C -1 P .2 R 4 .5 I R C -0 P .8 R 4.5 IR C -1 P .0 R 4.5 IR C -1 P .2 R 8 .5 I R C -0 P .8 R 8.5 IR C -1 P8 .0 .5 I R -1 .2 IR R C P R 2 .6 C -0 P .8 R 2 .6 IR C -1 P .0 R 2.6 IR C -1 P .2 R 4 .5 IR C -0 P .8 R 4.5 IR C -1 P .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8.5 IR C -1 P8 .0 .5 IR -1 .2 IR

R

-3

WP (kg m )

649

651 Fig. 9 4

Year 2018-2027 Year 2028-2037

650

(a)

45

Year 2038-2047

3

2

1

0

C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4.5 IR C -1 P .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8.5 IR C -1 P8 .0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 R 2. 6 I R C -1 P .0 R 2.6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4.5 IR C -1 P .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8. 5 I R C -1 P8 .0 .5 IR -1 .2 IR R C P R 2.6 C -0 P .8 R 2.6 IR C -1 P .0 R 2.6 IR C -1 P .2 R 4.5 IR C -0 P .8 R 4. 5 I R C -1 P .0 R 4.5 IR C -1 P .2 R 8.5 IR C -0 P .8 R 8.5 IR C -1 P8 .0 .5 IR -1 .2 IR

R

-3

WP (kg m ) 4

653

Year 2018-2027 Year 2028-2037

652

(b)

654

46

Year 2038-2047

3

2

1

0

Graphical abstract

31

Responses of crop growth and water productivity to climate change and agricultural watersaving in arid region Highlights 

An agro-hydrological model was established in the middle oasis of Heihe River basin



The effects of CO2 on stomatal conductance and leaf area were considered in the model



The impacts of climate change and water-saving on crop growth and WP were evaluated



Adaptive strategies to climate change were proposed

32