任哓红, 王会肖, 刘昌明, 范玲. 基于AquaCrop模型的阿勒泰地区春小麦节水潜力分析[J]. 中国生态农业学报 (中英文), 2022, 30(10): 1638−1648. DOI: 10.12357/cjea.20220031
引用本文: 任哓红, 王会肖, 刘昌明, 范玲. 基于AquaCrop模型的阿勒泰地区春小麦节水潜力分析[J]. 中国生态农业学报 (中英文), 2022, 30(10): 1638−1648. DOI: 10.12357/cjea.20220031
REN X H, WANG H X, LIU C M, FAN L. Water-saving potential analysis of spring wheat in Altay based on AquaCrop model[J]. Chinese Journal of Eco-Agriculture, 2022, 30(10): 1638−1648. DOI: 10.12357/cjea.20220031
Citation: REN X H, WANG H X, LIU C M, FAN L. Water-saving potential analysis of spring wheat in Altay based on AquaCrop model[J]. Chinese Journal of Eco-Agriculture, 2022, 30(10): 1638−1648. DOI: 10.12357/cjea.20220031

基于AquaCrop模型的阿勒泰地区春小麦节水潜力分析

Water-saving potential analysis of spring wheat in Altay based on AquaCrop model

  • 摘要: 为进一步发展节水农业, 提高水分利用效率, 建立作物产量与耗水量的响应关系, 确定区域农业节水潜力, 为水资源合理配置提供依据。以AquaCrop为研究模型, 对模型产量模块参数标准化的水分生产效率(WP*)和参考收获指数(HI0)进行多年率定与验证, 以2017年作为现状水平年, 通过设置4月10日和4月20日两个春小麦(‘新春6号’)播种日期, 每个播种日期下设置400 mm、350 mm、300 mm、250 mm、200 mm的灌溉定额和7 d和10 d的灌溉周期, 共20个情景, 对新疆阿勒泰地区春小麦产量进行模拟, 对比不同情景下的春小麦产量和灌溉水利用效率受灌溉定额和灌水次数的影响, 以产量和水分利用效率均较高为目标, 择优选择最佳灌溉策略。利用现状年春小麦种植面积作为参考值, 对比现状水平年和未来水平年在不同情景下的小麦产量及总节水量差异, 分析小麦的节水潜力。结果表明: 1)推荐WP*=18 g∙m−2和HI0=48%作为阿勒泰地区2005—2014多年率定及2015—2017年验证后的产量模块参数, 率定产量误差范围为−3.44%~5.67%, 适用性评价指标均方根误差(RMSE)、相对均方根误差(RRMSE)、残差系数(CRM)、Willmott一致度(d)和纳什效率系数(ENS)值分别为0.110、0.023、0.002、0.956、0.935, 适用性好; 2015—2017年验证产量误差分别为−0.41%、−3.02%、3.34%, 模拟误差较小。2)不同情景下的模拟结果显示, 情景S15 (4月20日播种、灌溉定额为300 mm、灌溉周期为7 d)可以作为推荐灌溉策略, 该模拟情景下, 作物产量和灌溉水分利用效率分别为5.610 t∙hm−2和1.870 kg∙m−3。3)对于阿勒泰地区的春小麦种植, 情景S15作为推荐的灌溉策略, 现状水平年可以实现节水2.335亿m3, 节水潜力是可观的, 未来水平年可以分别实现节水2.407亿m3、2.431亿m3、2.476亿m3

     

    Abstract: In 2017, the comprehensive irrigation quota of various crops in Altay was 955 mm, and the actual irrigation quota of spring wheat, the main food crop, was approximately 780 mm, which is far higher than the actual water demand of spring wheat. To improve water-use efficiency, the best irrigation quota for spring wheat was determined, and the relationship between crop yield and water consumption was established. The AquaCrop model was used as the research model, and the main parameters of the AquaCrop model were localized in northern Xinjiang. The annual rate and applicability of the AquaCrop model from 2005 to 2014 were evaluated based on standardized water production efficiency (WP*) and reference harvest index (HI0). After determining the appropriate parameters, the meteorological data from 2015 to 2017 were used for verification. Two sowing dates of spring wheat variety ‘Xinchun 6’ on April 10 and April 20 were set in this study, and irrigation quotas of 400 mm, 350 mm, 300 mm, 250 mm, and 200 mm and irrigation cycles of 7 d and 10 d were set under each sowing date for a total of 20 scenarios. The spring wheat yield in the Altay region of Xinjiang was simulated, and the spring wheat yield and irrigation water use efficiency under different scenarios and influence of the irrigation quota and irrigation times were compared. The optimal irrigation strategy was selected, with high yield and water-use efficiency as the goal. Using the wheat planting area and irrigation quota in 2017 as reference values, the differences in wheat yield and total water-saving amount under different scenarios in 2017 and 2020 were compared to analyze the water-saving potential of wheat. The results are as follow: 1) WP*=18 g∙m−2 and HI0=48% were recommended as the yield module parameters in the Altay area. The parameters of the AquaCrop model are divided into two modules: crop growth and yield. The parameters of the crop growth module required field experiments; therefore, the parameters of the yield module were adjusted. WP*=18 g∙m−2, HI0=48% and WP*=19 g∙m−2, HI0=45% were selected as the yield module parameters, and the yield error ranges were −3.44% to 5.67% and −4.92% to 4.56%, respectively. WP*=18 g∙m−2 and HI0=48% had better applicability, and the evaluation indexes: root mean square error (RMSE), relative RMSE (RRMSE), residual coefficient (CRM), Willmott cossitancy (d), and Nash efficincy coefficient (ENS) were 0.110, 0.023, 0.002, 0.956, and 0.935, respectively. Finally, meteorological data from 2015 to 2017 were used for validation, and the validated yield error results were −0.41%, −3.02%, and 3.34%, respectively, with small simulation errors. 2) Scenario S15 (sowing on April 20, irrigation quota of 300 mm, irrigation cycle of seven days) can be used as the recommended irrigation strategy. Through the simulation of spring wheat yield and calculation of irrigation water-use efficiency under different scenarios, it was found that the postponement of sowing date was beneficial to the accumulation of crop yield because the crop was less exposed to low-temperature stress at that time. The effect of irrigation cycle on spring wheat yield was the opposite under different planting dates and irrigation quotas. The spring wheat yield of S15 was 5.610 t∙hm−2, and the irrigation water use efficiency was 1.870 kg∙m−3. 3) In 2017, scenario S15 saved 2.335×108 m3 of water. Under irrigation quotas (400 mm, 350 mm, 300 mm, 250 mm and 200 mm), 1.849×108 m3, 2.092×108 m3, 2.335×108 m3, 2.579×108 m3, and 2.822×108 m3 were saved in 2017. Under the recommended irrigation strategy, water savings of 2.407×108 m3, 2.431×108 m3, and 2.476×108 m3 can be achieved in the future when the utilization coefficient of irrigation water is 0.570, 0.580 and 0.600, respectively; indicating a huge water-saving potential.

     

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