基于PML-V2模型和站点观测数据的植被水利用率及其趋势差异分析

李淑津; 袁华; 孔冬冬; 董文宗; 黄丽娜; 戴永久

doi:10.3878/j.issn.1006-9585.2022.22009

基于PML-V2模型和站点观测数据的植被水利用率及其趋势差异分析

doi: 10.3878/j.issn.1006-9585.2022.22009

李淑津^1,,
袁华^{1, 2, 3, ,},
孔冬冬⁴,
董文宗¹,
黄丽娜¹,
戴永久^{1, 2, 3}

1.
中山大学大气科学学院，广东珠海 519000
2.
南方海洋科学与工程广东省实验室（珠海），广东珠海 519000
3.
广东省气候变化与自然灾害研究重点实验室，广州 510275
4.
中国地质大学（武汉）环境学院，武汉 430078

基金项目: 国家自然科学基金 42075160、41730962，国家重点研发计划项目 2017YFA0604300

详细信息

作者简介:
李淑津，女，1994年出生，硕士研究生，主要从事植被水利用率研究。E-mail: 260065053@qq.com

通讯作者:
袁华，E-mail: yuanh25@mail.sysu.edu.cn

中图分类号: P463
计量
- 文章访问数: 79
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- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-19
- 网络出版日期: 2022-05-13
- 刊出日期: 2023-01-25

Difference Analyses in Vegetation Water Use Efficiency and Their Trends Based on the PML-V2 Model and Site Observations

Shujin LI^1
,,
Hua YUAN^{1, 2, 3
, ,},
Dongdong KONG⁴,
Wenzong DONG¹,
Lina HUANG¹,
Yongjiu DAI^{1, 2, 3}

1.
School of Atmosphere Science, Sun Yat-sen University, Zhuhai, Guangdong Province 519000
2.
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong Province 519000
3.
Guangdong Province Key Laboratory for Climate Change and Natural Disaster, Guangzhou 510275
4.
School of Environmental Studies, China University of Geosciences, Wuhan 430078

Funds: National Natural Science Foundation of China (Grants 42075160 and 41730962)，National Key Research and Development Program of China (Grant 2017YFA0604300)

摘要

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。
- 冠层水利用率 /
- 内禀水利用率 /
- 蒸散发分离 /
- 冠层导度 /
- 蒸腾作用
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.
- Canopy water use efficiency /
- Intrinsic water use efficiency /
- Evapotranspiration partitioning /
- Canopy conductance /
- Transpiration

HTML全文

图 1 落叶阔叶林（deciduous broadleaf forests, DBF）和常绿针叶林（evergreen needleleaf forests, ENF）的站点分布

Figure 1. Distributions of deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF) sites

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图 2 12个站点蒸散发的观测值（E_o）与模拟值（E_s）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E_o的平均值，${\overline{{E}_{\mathrm{s}}}}$是E_s的平均值）

Figure 2. Observation (E_o) and simulation (E_s) 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 E_o and ${\overline{{E}_{\mathrm{s}}}}$ is the mean of E_s)

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图 3 同图2，但为GPP的时间序列

Figure 3. Same as in Fig. 2, but for the time series of gross primary production (GPP)

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图 4 GPP和E的NSE值的对比（NSE_E表示E的站点观测值与模拟值的NSE值，NSE_GPP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, NSE_E represents the NSE between station observation and simulation of E, and NSE_GPP represents the NSE between station observation and simulation of GPP: (a) DBF; (b) ENF

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图 5 同图4，但为决定系数R²的散点图

Figure 5. Same as in Fig. 4, but for the scatter plot of the coefficient of determination (R²)

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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

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图 7 同图6，但为总初级生产力（GPP）、冠层导度（G_c）和蒸腾作用（T）的趋势

Figure 7. Same as Fig. 6, but for the trend’s plot of GPP, canopy conductance (G_c) and transpiration (T)

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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

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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)

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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。

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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
站点ID	IGBP（地表覆盖分类系统）	RMSE_E/mm d⁻¹	RMSE_GPP/g(C) m⁻² d⁻¹		Bias_E/mm d⁻¹	Bias_GPP/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

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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

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表 4 DBF和ENF中根据bootstrap自助法得到的GPP、冠层导度（G_c）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 (G_c), 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%
	G_c	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%
	G_c	76.59%	0.62%	23.41%	−0.33%
	T	77.90%	0.66%	22.10%	−0.39%

下载: 导出CSV

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