Difference Analyses in Vegetation Water Use Efficiency and Their Trends Based on the PML-V2 Model and Site Observations
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摘要: 运用基于Penman-Monteith公式改进得到的模型PML-V2,结合12个FLUXNET站点及其对应的叶面积指数数据,进行蒸散发分离,进而计算并分析内禀水利用率(intrinsic water use efficiency, iWUE)和冠层水利用率(canopy water use efficiency, tWUE)的趋势差异。结果表明,在站点尺度上,两种植被水利用率的变化均存在不一致性。对于落叶阔叶林(deciduous broadleaf forests, DBF),iWUE的增幅比tWUE的增幅大,而在常绿针叶林(evergreen needleleaf forests, ENF)中则相反。在DBF中,冠层导度和蒸腾作用趋势的差异可在一定程度上解释两种植被水利用率的趋势差异。通过回归分析发现森林(包括DBF和ENF)的气温和大气CO2浓度的趋势对tWUE趋势的影响更大。研究结果表明,两种植被水利用率及其趋势存在差异。基于iWUE的研究结果并不能完全反映植被的实际水利用率变化程度,因此也不能全面反映植被与大气的相互作用。本文在站点尺度明确了全球气候变化背景下两种植被水利用率的趋势差异,有助于理解陆地生态系统与大气之间的相互作用,为合理有效地预测未来气候变化及陆地植被的演变提供有用的参考依据。Abstract: With the PML-V2 model, the evapotranspiration was separated and the trends of intrinsic water use efficiency (iWUE) and canopy water use efficiency (tWUE) was calculated to investigate the differences between them. The results show that the change in these two types of water use efficiency is inconsistent at the site scale. The trend of iWUE is greater than that of iWUE in deciduous broadleaf forests (DBF), whereas the contrary occurs in evergreen coniferous forests (ENF). The discrepancy in canopy conductance and transpiration trends can help explain the difference in iWUE and tWUE trends in DBF. Regression analysis revealed that the trends of air temperature and carbon dioxide concentration in forests (DBF and ENF) have a stronge influence on the trend of tWUE. The results of this paper demonstratethe differences between the trends of iWUE and tWUE. Therefore, study results about iWUE cannot fully indicate the trends of the actual water use efficiency of vegetation, and they cannot thoroughly reflect the interactions between vegetation and the atmosphere. This study reveals the trend difference between iWUE and tWUE under the background of global climate change, which is useful for understanding the interactions between terrestrial ecosystems and the atmosphere and provides a useful reference for predicting future climate change and the evolution of terrestrial vegetation reasonably and effectively.
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图 2 12个站点蒸散发的观测值(Eo)与模拟值(Es)的7 d平均时间序列(每个分图的标题为站点ID及其植被覆盖类型;圆点表示观测值,线表示模拟值,其中
${\overline{{E}_{\mathrm{o}}}}$ 是Eo的平均值,${\overline{{E}_{\mathrm{s}}}}$ 是Es的平均值)Figure 2. Observation (Eo) and simulation (Es) 7-d average time series of evapotranspiration at 12 sites (the title of each plot is the site ID and its vegetation cover type, dots represent observed values and lines represent simulated values,
${\overline{{E}_{\mathrm{o}}}}$ is the mean of Eo and${\overline{{E}_{\mathrm{s}}}}$ is the mean of Es)图 4 GPP和E的NSE值的对比(NSEE表示E的站点观测值与模拟值的NSE值,NSEGPP表示GPP的站点观测值与模拟值的NSE值):(a)DBF;(b)ENF
Figure 4. Comparison of NSE values of GPP and E, NSEE
represents the NSE between station observation and simulation of E, and NSEGPP represents the NSE between station observation and simulation of GPP: (a) DBF; (b) ENF 图 6 DBF和ENF的内禀水利用率(iWUE)和冠层水利用率(tWUE)的趋势图。纵坐标表示各站点每年内禀水利用率的相对年变化率,粗红线为该植被类型各站点的趋势进行平均得到的平均趋势线,黑色线为每个站点的趋势线,蓝色散点表示各站点每年内禀水利用率的相对年变化率;左上角的小图为各站点内禀水利用率的趋势和频数分布图,红色虚线为用bootstrap自助法获取的平均趋势90%的置信区间:(a)DBF的iWUE;(b)ENF的iWUE;(c)DBF的tWUE;(d)ENF的tWUE
Figure 6. Trends of DBF and ENF’s intrinsic water use efficiency (iWUE) and canopy water use efficiency (tWUE). The vertical coordinates represent the relative annual change rate of iWUE at each site in each year. The thick red line represents the average of trends from stations with the same vegetation type. The black lines represent the trend lines of each station, and the blue scatter represents the relative annual change rate of iWUE at each station in each year. The upper left corner insert plot displays the trend and frequency distribution of iWUE at each station, and the red dotted line represents the 90% confidence interval of the average trend derived by the bootstrap method: (a) iWUE of DBF; (b) iWUE of ENF; (c) tWUE of DBF; (d) tWUE of ENF
图 8 森林中各变量的平均趋势(纵坐标,表示各变量平均每年变化百分比)及其90%置信区间,图中包含7个变量:内禀水利用率(iWUE),冠层水利用率(tWUE),气温(Tavg),饱和水汽压差(VPD),大气CO2浓度(CO2),降水(Prcp),入射短波辐射(Rs)。森林中各变量的平均趋势由12个站点的趋势平均得到。图中误差棒为平均趋势90%的置信区间,由bootstrap自助法确定
Figure 8. Trend of each variable (vertical coordinate, which represents the average annual percentage change of each variable) in the forest and its 90% confidence interval. There are seven variables in the figures, which are intrinsic water use efficiency (iWUE), canopy water use efficiency (tWUE), air temperature (Tavg), the vapor pressure deficit (VPD), atmospheric carbon dioxide concentration (CO2), precipitation (Prcp), incoming shortwave radiation (Rs). The trend of each variable in the forest is calculated by averaging the trends of 12 stations. The error bars in the figures represent the 90% confidence interval of the mean trends, which are calculated using the bootstrap method
图 9 内禀水利用率(iWUE)和冠层水利用率(tWUE)作为因变量和5个环境因子(横坐标)作为自变量进行的偏最小二乘回归得到的回归系数(纵坐标)分布(回归分析前已根据Z分数将自变量和因变量标准化,因此回归系数为无量纲量。n为回归分析样本量,*表示P值小于0.05,***表示P值小于0.001)
Figure 9. The intrinsic water use efficiency (iWUE) and canopy water use efficiency (tWUE) are dependent variables, whereas the five environmental factors are independent variables (horizontal ordinate). Using partial least squares regression to calculate the regression coefficient (vertical coordinate) of independent and dependent variables (the independent and dependent variables were standardized according to Z scores before regression analysis. Therefore, the regression coefficient is dimensionless. n in the figure represents the sample size of the regression analysis. * indicates a P-value less than 0.05 and *** indicates a P-value less than 0.001)
表 1 北半球12个站点的地理及气象信息
Table 1. Geographic and meteorological information for 12 sites in the Northern Hemisphere
站点ID IGBP(地表覆盖分类系统) 起始年份 结束年份 纬度 经度 海拔/m 年均温度/°C 年降水量/mm DE-Hai DBF 2000 2009 51.0792°N 10.4522°E 430 8.3 720 DE-Lnf DBF 2002 2012 51.3282°N 10.3678°E 451 6.96 894.6 DK-Sor DBF 2004 2013 55.4859°N 11.6446°E 40 8.2 660 US-MMS DBF 2000 2014 39.3232°N 86.4131°W 275 10.85 1032 US-Oho DBF 2005 2013 41.5545°N 83.8438°W 230 10.1 849 CA-TP4 ENF 2004 2014 42.71012°N 80.3574°W 184 8 1036 DE-Tha ENF 2000 2014 50.9626°N 13.5652°E 385 8.2 843 FI-Hyy ENF 2005 2014 61.8474°N 24.2948°E 181 3.8 709 IT-Lav ENF 2004 2014 45.9562°N 11.2813°E 1353 7.8 1291 NL-Loo ENF 2000 2014 52.1666°N 5.7436°E 25 9.8 786 RU-Fyo ENF 2000 2012 56.4615°N 32.9221°E 265 3.9 711 US-NR1 ENF 2006 2012 40.0329°N 105.5464°W 3050 1.5 800 注:站点ID由国家简称和站点名字组成。 表 2 12个站点GPP和E的观测值与模拟值的均方根误差(RMSE)和偏差(Bias)
Table 2. Root-Mean-Squared Error (RMSE) and Bias of GPP and E between observations and simulations at 12 sites
站点ID IGBP(地表覆盖分类系统) 均方根误差 偏差 RMSEE/mm d−1 RMSEGPP/g(C) m−2 d−1 BiasE/mm d−1 BiasGPP/g(C) m−2 d−1 DE-Hai DBF 0.30 2.02 0.03 0.35 DE-Lnf DBF 0.31 1.55 0.04 0.36 DK-Sor DBF 0.43 1.52 0.05 0.27 US-MMS DBF 0.50 1.82 0.17 0.40 US-Oho DBF 0.51 1.46 0.15 0.28 CA-TP4 ENF 0.49 1.61 0.03 0.22 DE-Tha ENF 0.31 1.14 0.02 0.10 FI-Hyy ENF 0.33 1.05 0.04 0.06 IT-Lav ENF 0.31 1.62 0.01 −0.05 NL-Loo ENF 0.34 1.00 0.00 0.01 RU-Fyo ENF 0.51 1.52 0.03 0.03 US-NR1 ENF 0.43 1.12 0.09 0.09 表 3 12个站点的冠层水利用率和内禀水利用率的Pearson相关系数以及经过t检验的p值
Table 3. Pearson correlation coefficient and P-value after t-test calculated by iWUE and tWUE of 12 sites
站点ID IGBP(地表覆盖分类系统) 相关系数 p值 站点ID IGBP(地表覆盖分类系统) 相关系数 p值 DE-Hai DBF −0.79 0.006 DE-Tha ENF −0.74 0.002 DE-Lnf DBF −0.84 0.008 FI-Hyy ENF −0.94 0.002 DK-Sor DBF −0.69 0.087 IT-Lav ENF −0.65 0.041 US-MMS DBF −0.66 0.010 NL-Loo ENF −0.90 <0.001 US-Oho DBF −0.79 0.034 RU-Fyo ENF −0.59 0.127 CA-TP4 ENF −0.92 <0.001 US-NR1 ENF −0.96 <0.001 表 4 DBF和ENF中根据bootstrap自助法得到的GPP、冠层导度(Gc)和蒸腾作用(T)的平均趋势的正负统计值占比以及正负统计值均值
Table 4. Positive and negative statistical values of mean trends obtained by the bootstrap method and proportion and mean trends of GPP, canopy conductance (Gc), and transpiration (T) in DBF and ENF
IGBP(地表覆盖分类系统) 变量 正统计值占比 正统计值均值/a 负统计值占比 负统计值均值/a DBF GPP 59.53% 0.29% 40.47% −0.30% Gc 8.83% 0.34% 91.17% −1.12% T 20.97% 0.17% 79.03% −0.34% ENF GPP 80.78% 0.78% 19.22% −0.40% Gc 76.59% 0.62% 23.41% −0.33% T 77.90% 0.66% 22.10% −0.39% -
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