Cheng Lin, Fang Wensong. Estimation of climate change effects on water use efficiency of rain-fed winter wheat. J Appl Meteor Sci, 2015, 26(3): 300-310. DOI:  10.11898/1001-7313.20150305.
Citation: Cheng Lin, Fang Wensong. Estimation of climate change effects on water use efficiency of rain-fed winter wheat. J Appl Meteor Sci, 2015, 26(3): 300-310. DOI:  10.11898/1001-7313.20150305.

Estimation of Climate Change Effects on Water Use Efficiency of Rain-fed Winter Wheat

DOI: 10.11898/1001-7313.20150305
  • Received Date: 2014-09-03
  • Rev Recd Date: 2015-02-11
  • Publish Date: 2015-05-31
  • Investigating the influencing rule of climate change on water use efficiency (WUE) of rain-fed winter wheat can offer scientific reference for agriculture adapting to climate change. Based on yield information and observed soil water data at representative stations, the historical trend of WUE is analyzed. Simulation models for meteorological yield and soil water variation quantity are established, and four different kinds of climate change scenarios, which are outputs by regional climate models of PRECIS and REGCM4.0 are combined to estimate the probable variation trend of WUE in the future years of 2021-2050 for rain-fed wheat. It is validated that in the basic scenario years, simulated yields by the combination of two regional climate models with meteorological yield simulation model are close to actual values, so methods for estimating future yield of wheat is proved feasible. Results by data analyzing shows that the average yield for representative stations varies as a cubic curve during the last 30 years of 1981-2010, and grows faster before the year of 2000. Water consumption of wheat also increases with fluctuating. The average WUE value of rain-fed wheat for representative stations in Gansu, Shanxi and Henan are 13.19 kg·mm-1·hm-2, 12.86 kg·mm-1·hm-2 and 11.28 kg·mm-1·hm-2, respectively. The varying trend of WUE is similar to a quadratic curve, and the maximum value appears in the year of 2003. Estimation results under four different climate change scenarios shows that in 2021-2050, water consumption of winter wheat would increase dramatically, and the increasing amount could reach to 6.2% for all the representative stations and all scenarios averagely. Yields in the future would decrease and some increase, and the variation rate would be 1.4% on average. The value of WUE would decrease 3.8% on average, meanwhile, the variability rate would also decrease. The increase of water consumption would be the main cause for WUE decreasing in the future. From the inter-annual variation during 2021-2050, WUE would show a non-significant trend of increasing under the simulation of PRECIS model, and comparing to the average value of 1981-2010, the decreasing rate of WUE would be more significant under A2 scenario than B2. However, there would be a significant decline trend for WUE simulated by REGCM4.0 model, and under the scenario of RCP8.5, the reduced value of WUE would be higher than that of RCP4.5. Generally speaking, the climate scenario of RCP has even more negative effects on WUE of rain-fed wheat.
  • Fig. 1  Annual variation of average value of yield (a), water consumption (b) and water use efficiency (c) of rain-fed winter wheat for Mianchi, Yiyang and Linfen stations

    Fig. 2  Annual variation of average value of yield (a), water consumption (b) and water use efficiency (c) of rain-fed winter wheat for representative stations

    Fig. 3  Estimated value for water consumption (a), yield (b) and water use efficiency (c) of winter wheat under different conditions at representative stations

    Fig. 4  Annual variation of water use efficiency by different climate models in the future (straight lines denote trends)

    Table  1  Parameters of linear regression model for meteorological yield of winter wheat at representative stations

    站点 时段 正相关因子 回归系数 负相关因子 回归系数 常数项
    渑池 1961—2010年 P(1)* 0.802 Tave(1)* -47.09 -1144.5
    P(2)** 20.664 Tmin(10)* -41.798
    P** 2.127 Tmin(1)* -32.458
    宜阳 1961—2010年 Tmax 9.202 Tmax(11)** -59.177 599.8
    P(1) 4.906
    P(2)** 10.1
    P* 1.546
    西峰 1981—2010年 Tave(5) 1018.696 Tmin(5)* -807.948 1843.3
    P(10)** 9.274 Tmax(5) -507.127
    P(2) 73.29
    P(5)** 1.084
    天水 1981—2010年 Tmin(9) 21.319 -4085.9
    Tmin(1) 176.754
    Tmax(6) 125.673
    P(1) 78.789
    P(2) 55.977
    P(4) 18.353
    临汾 1961—2010年 P(4)* 2.805 Tave(5)* -79.409 3917.810
    Tmax(10)* -94.758
    Tmax(4)* -23.005
    Tmax(all)* -32.565
      注:*表示达到0.05显著性水平,**表示达到0.01显著性水平。
    DownLoad: Download CSV

    Table  2  Correlation coefficient for yield simulation

    气候模式 站点 变量数 气象产量 单产
    PRECIS 渑池 29 0.4217* 0.8587**
    宜阳 29 0.3551* 0.9040**
    西峰 10 0.4869 0.7565**
    天水 10 0.2223 0.3334
    临汾 29 0.3169 0.8730**
    REGCM4.0 渑池 44 0.4058** 0.8493**
    宜阳 44 0.3611* 0.8506**
    西峰 25 0.3840* 0.6181**
    天水 25 0.2934 0.7892**
    临汾 44 0.3049* 0.9014**
      注:*表示达到0.05显著性水平,**表示达到0.01显著性水平。
    DownLoad: Download CSV

    Table  3  Parameters of linear regression model for soil water variation of the whole growing season of winter wheat at representative stations

    站点 时段 正相关因子 回归系数 负相关因子 回归系数 常数项
    渑池 1961—2010年 Tave(5)* 13.076 P(3) -0.453 -187.329
    Tmin(11) 0.390 P(4)** -0.778
    P(5) 0.322 P(all)* -0.181
    宜阳 1961—2010年 Tave(4) 3.822 P(4)** -0.175 -149.946
    Tave(5)** 6.312 P(5) -0.076
    T(all) 2.895
    Tmin(12)** 10.321
    西峰 1981—2010年 Tave(10) 20.835 Tmax(10)* -7.138 21.011
    P(10)** 0.867 Tmax(5) -4.543
    P(2)* 1.244 P(5)* -0.634
    天水 1981—2010年 Tave(6)** 0.627 Tmin(4) -9.135 -155.832
    P(1)* 3.242 P(9) -0.058
    Tave(9) 12.474
    临汾 1961—2010年 Tave(6)* 109.948 Tmin(11)* -9.052 -268.412
    Tmax(5) 5.587 Tmax(6)* -81.384
    P(11)* 2.581 P(6) -0.464
      注:*表示达到0.05显著性水平,**表示达到0.01显著性水平。
    DownLoad: Download CSV

    Table  4  Variable coefficient of water use efficiency for rain-fed winter wheat (unit:%)

    情景 渑池站 宜阳站 西峰站 天水站 临汾站
    A2 27.12 23.49 23.12 31.18 21.86
    B2 30.39 17.49 23.90 34.55 11.63
    RCP4.5 20.86 21.43 28.55 26.35 18.13
    RCP8.5 14.93 23.33 23.91 23.51 24.23
    DownLoad: Download CSV
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    • Received : 2014-09-03
    • Accepted : 2015-02-11
    • Published : 2015-05-31

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