Evaluation of the effect of future climatic change on Hebei cotton production and water consumption using multiple GCMs
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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∙hm−2, 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∙hm−2, 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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Key words:
- Cotton /
- Climatic change /
- GCM /
- APSIM-COTTON /
- Yield /
- Water consumption /
- Hebei cotton production region
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图 1 ‘中植棉2号’棉花干物质量模拟结果的验证
Figure 1. Verification of simulated dry matter of cotton cultivar ‘Zhongzhimian 2’ with results of the field experiment
图 2 ‘中植棉2号’棉花叶面积指数(LAI)、株高、蕾数和吐絮数的验证结果
Figure 2. Verification results of leaf area index (LAI), plant height, squares number and open bolls number of cotton cultivar ‘Zhongzhimian 2’ with results of the field experiment
图 3 SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5气候情景下研究区各气候指标变化趋势
Figure 3. Annual change trend of climate indexes at the study area under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 climate scenarios
图 4 SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5气候情景下22种GCMs模拟的棉花物候期变化预测
在箱型图里, 箱体为25%~75%的范围, 箱体内横线为中位线, 方形表示平均值, 上下两须线是1.5倍IQR (四分位间距)内的范围, 图中为左箱体、右数据的分布类型。Y轴题中, DOY为日序, DAS为播种后天数, DAE为出苗后天数。X轴上Baseline为1981—2010年。In the box plots, the box is 25%–75% of the range, the horizontal line is the median line, the square represents the average value, the upper and lower two muster lines are within the range of 1.5 times IQR (Interquartile Quartile Range), and the point beyond the muster line is the abnormal value. The figure shows the distribution type of the left box body and the right data. In the Y-axis title, DOY is the days of year, DAS is the days after seeding day, and DAE is the days after emergence day. In the X-axis, the Baseline is from 1981 to 2010.
Figure 4. Prediction of cotton phenology changes simulated by 22 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 climate scenarios
图 5 SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5气候情景下22种GCMs模拟的棉花耗水指标的变化预测
在箱型图里, 箱体为25%~75%的范围, 箱体内横线为中位线, 方形表示平均值, 上下两须线是1.5倍IQR (四分位间距)内的范围, 图中为左箱体、右数据的分布类型。在Y轴题目中, ET是蒸散发, ES是土壤蒸发量。X轴上的Baseline为1981—2010年。In the box plots, the box is 25%–75% of the range, the horizontal line is the median line, the square represents the average value, the upper and lower two muster lines are within the range of 1.5 times IQR (Interquartile Quartile Range), and the point beyond the muster line is the abnormal value. The figure shows the distribution type of the left box body and the right data. In the Y-axis title, ET is the evapotranspiration and ES is the soil evaporation. In the X-axis, the Baseline is from 1981 to 2010.
Figure 5. Prediction of cotton water consumption indexes changes simulated by 22 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 climate scenarios
图 6 SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5气候情景下22种GCMs模拟的棉花产量、地上干物质重量和叶面积指数的变化预测
在箱型图里, 箱体为25%~75%的范围, 箱体内横线为中位线, 方形表示平均值, 上下两须线是1.5倍IQR (四分位间距)内的范围, 图中为左箱体、右数据的分布类型。X轴上的Baseline为1981—2010年。In the box plots, the box is 25%–75% of the range, the horizontal line is the median line, the square represents the average value, the upper and lower two muster lines are within the range of 1.5 times IQR (Interquartile Quartile Range), and the point beyond the muster line is the abnormal value. The figure shows the distribution type of the left box body and the right data. In the X-axis, the Baseline is from 1981 to 2010.
Figure 6. Prediction of cotton yield, dry matter aboveground and leaf area index (LAI) changes simulated by 22 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 climate scenarios
表 1 本研究所涉及的22个全球气候模式(GCMs)信息
Table 1. Information of the 22 Global Climate Models (GCMs) applied in the study
编号 Number 代码 GCM code GCM名称 GCM name 机构 Institution ID 国家 Country 1 ACC1 ACCESS-CM2 BoM 澳大利亚 Australia 2 ACC2 ACCESS-ESM1-5 BoM 澳大利亚 Australia 3 BCC BCC-CSM2-MR BCC 中国 China 4 Can1 CanESM5 CCCMA 加拿大 Canada 5 Can2 CanESM5-CanOE CCCMA 加拿大 Canada 6 CNR1 CNRM-ESM2-1 CNRM 法国 France 7 CNR2 CNRM-CM6-1 CNRM 法国 France 8 CNR3 CNRM-CM6-1-HR CNRM 法国 France 9 ECE1 EC-Earth3-Veg EC-EARTH 欧盟 European Union 10 ECE2 EC-Earth3 EC-EARTH 欧盟 European Union 11 FGOA FGOALS-g3 FGOALS 中国 China 12 GFD GFDL-ESM4 NOAA GFDL 美国 United States 13 GISS GISS-E2-1-G NASA GISS 美国 United States 14 INM1 INM-CM4-8 INM 俄罗斯 Russia 15 INM2 INM-CM5-0 INM 俄罗斯 Russia 16 LPSL IPSL-CM6A-LR LPSL 法国 France 17 MIR1 MIROC6 MIROC 日本 Japan 18 MIR2 MIROC-ES2L MIROC 日本 Japan 19 MPI1 MPI-ESM1-2-HR MPI-M 德国 Germany 20 MPI2 MPI-ESM1-2-LR MPI-M 德国 Germany 21 MTIE MRI-ESM2-0 MIR 日本 Japan 22 UKES UKESM1-0-LL MOHC 英国 Britain 
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表 2 不同深度的土壤参数
Table 2. Soil parameters in different depths
深度
Soil depth
(cm)容重
Bulk density
(g∙cm−3)风干含水量
Air-dried moisture
(mm∙mm−1)凋萎系数
Wilting coefficient
(mm∙mm−1)田间最大持水量
Field capacity
(mm∙mm−1)饱和含水量
Saturated moisture
(mm∙mm−1)0~15 1.470 0.060 0.119 0.274 0.425 15~30 1.460 0.059 0.119 0.273 0.448 30~60 1.390 0.050 0.109 0.264 0.444 60~90 1.510 0.060 0.109 0.274 0.430 90~120 1.510 0.058 0.097 0.272 0.430 120~150 1.553 0.055 0.097 0.269 0.414 150~180 1.510 0.065 0.097 0.313 0.430
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表 3 APSIM-COTTON中棉花‘中植棉2号’的作物品种遗传参数
Table 3. Genetic parameters of cotton cultivar ‘Zhongzhimian 2’ used in APSIM-COTTON model
参数
Parameter单位
Unit描述
Description赋值
ValuePercent_l % 衣分 Lint percentage 43 Scboll g∙boll−1 单铃籽棉重 Seed cotton weight per boll 3.8 Respcon 呼吸常量 Respiration constant 0.015 93 Sqcon 蕾发生速率系数 Squaring constant 0.0181 Fcutout 与从营养生长停止到达到最大载铃量时间相关的常数
Constant relating timing of cutout to boll load0.5411 Flai 叶面积生长速率修正值 Adjustment constant for leaf areas growth rate 0.52 DDISQ ℃·d 从播种到现蕾的积温 Degree-day from sowing to first square 402 TIPOUT d 控制主茎生长点的时间 Tipping out time 52 FRUDD(1~8) ℃·d 不同级别蕾铃发育所需积温
Thermal development requirements for each cotton fruiting stage50, 169, 329, 356, 499, 642, 857, 1099 BLTME(1~8) 不同级别蕾铃在一天中完成的发育比例
Fraction of boll development completed in one day0.00, 0.00, 0.00, 0.07, 0.21, 0.33, 0.55, 1.00 Dlds_max mm2·site−1 每节位叶面积指数增加的最大速率
Maximum leaf area index growth rate per site0.12 Rate_emergence mm·(℃·d)−1 出苗速率 Rate of emergence 1 Popcon 密度系数 Population constant 0.036 33 Fburr 单铃重与籽棉重量比值
Ratio of weight of boll to seed cotton per boll1.23 ACOTYL mm2 子叶叶面积 Area of cotyledons 525 RLAI 现蕾之前叶面积指数相对生长率
Leaf area index increase rate pre squaring0.01
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表 4 APSIM-COTTON模型模拟结果的评价指标
Table 4. Evaluation indexes of APSIM-COTTON model simulation results
总干物重
Dry matter-total
weight叶干物重
Dry matter-leaf
weight茎干物重
Dry matter-stem
weight铃干物重
Dry matter-boll
weight叶面积指数
Leaf area index株高
Plant height绿铃数
Bolls number吐絮数
Open bolls
numberR2 0.9937 0.8833 0.8635 0.9538 0.7234 0.8103 0.7059 0.9849 RMSE 193.1381 219.4410 502.2122 688.1290 0.6812 269.9656 23.8967 4.5918 NRMSE 2.2359 0.2186 0.3227 0.3799 0.4484 0.4349 0.5002 0.8266 R2 为决定系数; RMSE 为均方根误差; NRMSE 为相对均方根误差。R2 is the determination coefficient; RMSE is the root mean square error; NRMSE is the relative root mean square error.
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