Characteristics of ecosystem energy closure and CO2 flux in a rice-wheat rotation area along the coast of East China
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摘要: 为科学评估华东沿海水稻-小麦轮作(简称“稻麦轮作”)农田生态系统能量通量变化特征和固碳能力, 基于2019—2020年涡度相关系统观测的稻麦轮作全生育期通量数据, 经质量控制, 研究分析了太阳净辐射(Rn)、潜热通量(LE)、显热通量(H)、土壤热通量(G)、CO2通量(FC)多时间尺度变化特征和稻麦轮作系统固碳量及其环境影响因子。结果表明: 有效能量和湍流通量能量平衡比率为0.80, 能量闭合度较高, 说明通量观测数据可靠。月均LE和Rn年内变化总体均呈“倒U型”, 两者变化基本同步, 峰值主要在5—8月, 谷值主要在1—2月、11—12月。H和G的波动幅度明显小于LE和Rn。日内逐小时FC呈“U型”单峰二次曲线, 总体为白天吸收CO2、夜间排放CO2, CO2日吸收峰值主要出现在10:00—12:30; 逐日FC和逐月FC在年内总体呈“W型”变化特征, 全年碳排放时段主要集中在1月、6月、11—12月, 其余均为碳吸收, 吸收峰值分别在冬小麦拔节孕穗期(3—4月)和水稻拔节孕穗期(8月)。2019年和2020年的2—5月冬小麦生长期的固碳量分别为387.4 g(C)∙m−2和382.2 g(C)∙m−2, 7—10月水稻生长期的固碳量分别为678.2 g(C)∙m−2和599.7 g(C)∙m−2; 白天, 若气温升高, 冬小麦和水稻的CO2吸收能力会随之增强, 但当饱和水汽压差高于1.7 kPa时, 会降低这种吸收趋势, 夜间主要是受气温影响。由此可见, 沿海稻麦轮作农田生态系统碳吸收能力有着明显的日变化和季节变化, 全年尺度上是碳汇, 且为强固碳区。Abstract: To scientifically evaluate the variation characteristics of energy flux and the carbon sequestration capacity of a winter wheat and one-season rice rotation farmland ecosystem on the coast of East China, we collected flux observations in winter wheat and one-season rice rotation farmland from a vorticity field observation experiment throughout the growth period from 2019 to 2020. We used the flux data processing software of American LI-COR Company to control the data quality and obtained a set of 30 min data sequences. After quality control, carbon sequestration of rice and wheat and its environmental impact factors and multi-time scale variation characteristics of solar net radiation (Rn), latent heat flux (LE), sensible heat flux (H), soil heat flux (G), and CO2 flux (FC) were studied and analyzed using energy balance and statistical methods. The results showed that the energy balance ratio of the effective energy and turbulent flux was 0.80, which indicated a high energy closure and reliable flux observations. The variations in monthly LE and Rn over the year showed an “inverted U” distribution, and these two variations were synchronous. The peak value mainly occurred from May to August, and the valley value mainly appeared from January to February and November to December. The fluctuation magnitudes of H and G were significantly lower than those of LE and Rn. Hourly FC during the day presented a “U-shaped” single peak quadratic curve, thereby indicating that CO2 was generally absorbed during the day and discharged at night. The daily absorption peak of CO2 mainly occurred during 10:00–12:30. Daily and monthly FC generally showed “W-type” variation characteristics throughout the year. The annual carbon emission periods were mainly concentrated in January, June, November, and December, whereas the rest of the year was classified as a carbon absorption period. The absorption peaks were at the jointing and booting stages of winter wheat (March to April) and rice (August). The carbon sequestration during the growth period of winter wheat from February to May was 387.4 g(C)∙m−2 and 382.2 g(C)∙m−2 in 2019 and 2020, respectively. Carbon sequestration during the growth period of rice from July to October was 678.2 g(C)∙m−2 and 599.7 g(C)∙m−2 in 2019 and 2020, respectively. During the day, when the air temperature increased, the CO2 absorption capacity of winter wheat and rice increased; however, this absorption trend decreased when the difference in saturated water vapor pressure was greater than 1.7 kPa. The absorption capacity of CO2 at night was mainly affected by temperature. The capacity of carbon absorption revealed clear diurnal and seasonal variations in coastal winter wheat and rice rotation farmland ecosystems. Considering the annual carbon balance, this ecosystem is a carbon sink. Moreover, it is a robust carbon sequestration area.
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图 3 2019年和2020年稻麦轮作农田生态系统潜热通量(LE)、显热通量(H)、土壤热通量(G)和太阳净辐射(Rn)各季节的日内变化
图a、b、c、d分别为2019年春季、夏季、秋季、冬季, 图e、f、g、h分别为2020年春季、夏季、秋季、冬季。Fig. a, b, c and d are spring, summer, autumn and winter in 2019; fig. e, f, g and h are spring, summer, autumn and winter in 2020, respectively.
Figure 3. Diurnal variations of latent heat flux (LE), sensible heat flux (H), soil heat flux (G) and solar net radiation (Rn) of rice-wheat rotation system in 2019 and 2020
图 4 2019年(a)和2020年(b)稻麦轮作农田生态系统能量平衡闭合情况
H: 显热通量; LE: 潜热通量; Rn: 太阳净辐射; G: 土壤热通量; Rn−G: 有效能量; LE+H: 湍流通量。H: sensible heat flux; LE: latent heat flux; Rn: solar net radiation; G: soil heat flux; Rn−G: effective energy; LE+H: turbulent flux.
Figure 4. Closure of energy balance of rice-wheat rotation system in 2019 (a) and 2020 (b)
图 6 2019—2020年稻麦轮作农田生态系统CO2通量(FC)各季节的日内变化
图a、b、c、d分别为2019年春季、夏季、秋季、冬季; 图e、f、g、h分别为2020年春季、夏季、秋季、冬季。Fig. a, b, c and d are spring, summer, autumn and winter of 2019; fig. e, f, g and h are spring, summer, autumn and winter in 2020, respectively.
Figure 6. Diurnal variations of CO2 flux (FC) of rice-wheat rotation system in each season of 2019 and 2020
图 7 稻麦轮作农田生态系统夜间CO2通量(FC)与气温(Ta, a)和饱和水汽压差(VPD, b)的关系
图上方公式中x为Ta, y为FC, R2为决定系数, N为样本天数, P为显著性水平检验值。In the formula above the figure, x is Ta; y is FC; R2 is the determination coefficient; N is the sample days; and P is the significance level test value.
Figure 7. Relationship between night CO2 flux (FC) and air temperature (Ta, a) and saturated water vapor pressure difference (VPD, b) of rice-wheat rotation system
图 8 稻麦轮作农田生态系统白天CO2通量(FC)与气温(Ta, 左列)和饱和水汽压差(VPD, 右列)的块平均图
图a、b为2019年冬小麦, 图c、d为2019年水稻, 图e、f为2020年冬小麦, 图g、h为2020年水稻。Fig. a and b represent winter wheat in 2019; fig. c and d represent rice in 2019; fig. e and f represent winter wheat in 2020; fig. g and h represent rice in 2020.
Figure 8. Block average diagram of daytime CO2 flux (FC) versus air temperature (Ta, left column) and saturated water vapor pressure difference (VPD, right column) of rice-wheat rotation system
表 1 2019—2020年稻麦轮作农田生态系统各月份能量平衡闭合线性回归参数和能量平衡比率
Table 1. Linear regression coefficients of energy balance closure and energy balance ratio of rice-wheat rotation system during 2019−2020
月份
Month2019 2020 样本数
Number of samples线性回归
斜率
Linear regression slope线性回归截距
Linear regression intercept能量平衡
比率
Energy balance ratio决定系数
Coefficient of determination样本数
Number of samples线性回归
斜率
Linear regression slope线性回归截距
Linear regression intercept能量平衡
比率
Energy balance ratio决定系数
Coefficient of determination1 131 0.67 13.02 0.99 0.94 343 0.61 12.02 0.85 0.92 2 909 0.62 11.16 0.83 0.90 981 0.64 15.70 0.84 0.92 3 1160 0.71 12.68 0.84 0.97 1067 0.68 13.56 0.80 0.96 4 1089 0.70 8.99 0.77 0.96 1020 0.68 15.59 0.77 0.97 5 1151 0.77 8.73 0.82 0.97 1190 0.72 10.23 0.78 0.97 6 1194 0.64 31.02 0.83 0.88 879 0.67 19.46 0.81 0.85 7 1183 0.65 20.98 0.79 0.93 1255 0.63 15.29 0.77 0.91 8 1180 0.67 19.5 0.79 0.97 1193 0.72 19.67 0.84 0.96 9 1109 0.67 12.49 0.76 0.96 1035 0.70 15.32 0.80 0.96 10 1119 0.65 11.21 0.76 0.95 1041 0.65 10.95 0.73 0.96 11 179 0.68 9.58 0.76 0.95 1019 0.64 11.59 0.78 0.93 12 — — — — — 1151 0.51 15.37 0.83 0.86 -
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