基于多GCMs模式的气候变化对河北棉花生产与耗水影响评估

Evaluation of the effect of future climatic change on Hebei cotton production and water consumption using multiple GCMs

  • 摘要: 气候模式是气候变化影响评估中不确定性的主要来源, 前人的研究多采用单个或较少的气候模式进行评估, 采用多种气候模式进行驱动可以降低由于气候模式的选择带来的误差。本研究在两年大田试验的基础上对作物模型APSIM-COTTON进行了精细的校验, 并选择22个GCMs (Global Climate Models)模式驱动作物模型评估了气候变化对河北棉花生产和耗水的影响。结果显示, 在所有气候情景下, 未来所有时间段, 播期提前, 各个发育时期(出苗、现蕾、吐絮、成熟)都较基准期缩短, 例如收获期在2090s 年代的SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5情景下分别提前15.3 d、21.0 d、30.3 d和35.2 d。年内总蒸散量在多数情景下总体呈增加趋势, 在SSP5-8.5情景下2030s、2050s、2070s和2090s分别增加6.5 mm、7.8 mm、14.3 mm和32.7 mm , 而灌水量减少25.7 mm、23.8 mm、30.5 mm和29.0 mm。棉花产量在未来则表现出在低辐射下不同年代差异不大, 而在高辐射强迫下随着年代增加而降低的趋势。在SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5情景下2090s皮棉产量相比基准期分别减少61.5 kg∙hm−2、46.6 kg∙hm−2、407.1 kg∙hm−2和432.5 kg∙hm−2。棉花生产和耗水对未来气候变化的响应是气候要素CO2浓度、太阳辐射强度、温度、降雨等综合作用的结果, 本研究模拟结果为未来农业措施的响应提供理论支撑。

     

    Abstract: Climatic models are the primary source of uncertainty in climate change impact assessments. Uncertainty can be significantly decreased by using multiple climate models during an assessment. In this study, the crop model APSIM-COTTON was carefully calibrated based on two years of field experiments, and 22 GCM (Global Climate Models) models (AR6) were used to drive crop models to evaluate the effects of climate change on cotton production and water consumption in Hebei Province. The leaf area index, plant height, squares number, bolls number, and dry matter weight of each plant were used to correct various APSIM-COTTON parameters. The coefficient of determination was greater than 0.8, indicating that the simulated and observed values fit well. The trend of climate change at this site was that the solar radiation intensity under SSP1-2.6, SSP2-4.5, and SSP5-8.5 was higher than the baseline (from 1980 to 2010) and increased with time, but it was lower than the baseline under SSP3-7.0. Temperature tended to increase in all scenarios, and the amplitude increased with the increase in radiative forcing and time. In most scenarios, the minimum temperature increased more than the maximum temperature, and annual rainfall increased over time. The responses of cotton production and water consumption to future climate change are the comprehensive effects of CO2 concentration, solar radiation, temperature, rainfall, and other climatic factors. The crop model simulation results showed that the sowing date was advanced under all climate scenarios and future time periods, and all development stages (emergence, squaring, flowering, and harvesting) were shorter than those of the baseline period. In the 2090s, under scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, the boll opening stage advanced 9.3, 12.0, 14.7, and 16.0 days, respectively, whereas the harvest stage advanced 15.3, 21.0, 30.3, and 35.2 days, respectively. The annual evapotranspiration (ET) under all scenarios, except SSP3-7.0, showed an increasing trend, whereas the irrigation amount decreased. Under the SSP5-8.5 scenario, the annual ET in the 2030s, 2050s, 2070s, and 2090s increased by 6.5, 7.8, 14.3, and 32.7 mm compared with the baseline, whereas the irrigation amount decreased by 25.7, 23.8, 30.5, and 29.0 mm, respectively. In the future, changes in cotton yield will not be large in scenarios of lower radiation focusing (SSP1-2.6), and there will be a decreasing trend with age under high radiation forcing (SSP5-8.5 and SSP3-7.0). Under SSP1-2.6 and SSP2-4.5 scenarios, lint yield decreased by approximately 61.5 and 46.6 kg∙hm2, respectively, in the 2090s. However, under SSP3-7.0 and SSP5-8.5 scenarios, the reduction by 2090s reached 407.1 and 432.5 kg∙hm2, respectively. In this study, 22 GCM models were used to simulate the response of cotton growth and water consumption to climate change over 100 years in the 21st century, and the changing trends in different scenarios and time periods were compared to provide technical support for developing adaptation strategies to climate change. However, the uncertainty of evaluating the climatic effect on cotton production still exists in this study. More site data should be considered in the calibration process, and more crop simulation models with different mechanisms should be compared in future research.

     

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