Characteristics and influencing factors of Bowen ratio variation in typical terrestrial ecosystems in China

doi:10.17521/cjpe.2019.0301

Abstract

Abstract:

Aims Bowen ratio (β) is an important parameter in land-surface processes. It affects the energy exchange between the surface and the atmosphere. This paper used integrated analyses to investigate the spatial variability and influencing factors of β.
Methods We collected the published literature on the measurement of surface energy balance by the Eddy Covariance (EC) method carried out in different ecosystem types in China, constructed the database of β and meteorological environment factors and analyzed the difference of β among ecosystems, the spatial variation characteristics of β and its influencing factors.
Important findings (1) The variation of β follows a lognormal distribution. The average β in all ecosystems was 0.95 ± 0.64, the coefficient of variation of β was 67%, the skewness was 1.58, and the kurtosis was 3.07. The shrub ecosystem has the highest mean value (1.26) and the wetland ecosystem has the lowest (0.49). (2) β is significantly different among ecosystems: β of shrub ecosystems is significantly higher than those in grassland, forest and wetland ecosystems, and β of croplands is between grassland ecosystems and forest with wetland ecosystems. (3) β increases with increasing latitude and does not change with longitude and altitude. For every 1° increase in latitude,β increases by 0.038. (4) β decreases with increase in mean annual precipitation (MAP), mean annual temperature (MAT), net radiation (R_n), precipitation of the studied year (PPT), mean temperature of the studied year (T_a), and leaf area index (LAI). (5) There are significant differences in the response of β to biotic and abiotic factors in different ecosystems: β of grassland, forest and shrub ecosystems are sensitive to changes in biotic and abiotic factors, while β of croplands and wetland ecosystems have no correlations with biotic and abiotic factors. (6) MAPand R_n are the direct factors influencing β. MAT affects βindirectly by affecting MAP, R_n and LAI. LAI affects β indirectly by affectingR_n. Our results indicate significant effect of the interaction between vegetation types and climatic factors on β. The most important factor affecting energy distribution is precipitation, and the regulation of leaf area on energy distribution is not significant.

Key words: Bowen ratio, latent heat flux, sensible heat flux, ecosystems, energy partitioning, climate, Leaf Area Index

HUANG Song-Yu, JIA Xin, ZHENG Jia-Jia, YANG Rui-Zhi, MU Yu, YUAN He-Di. Characteristics and influencing factors of Bowen ratio variation in typical terrestrial ecosystems in China[J]. Chin J Plant Ecol, 2021, 45(2): 119-130.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2019.0301

https://www.plant-ecology.com/EN/Y2021/V45/I2/119

Figures/Tables 12

Table 1 Main information of the Bowen ratio dataset in Chinese typical terrestrial ecosystems

变量名称 Variable name	缩写 Abbreviation	单位 Unit	变量名称 Variable name	缩写 Abbreviation	单位 Unit
纬度 Latitude	Lat	°	当年降水量 Annual precipitation of the studied year	PPT	mm
经度 Longitude	Lon	°	净辐射 Net radiation	R_n	MJ·m^-2·a^-1
海拔 Altitude	Alt	m	土壤热通量 Soil heat flux	G	MJ·m^-2·a^-1
年平均气温 Mean annual air temperature	MAT	℃	显热通量 Sensible heat flux	H	MJ·m^-2·a^-1
年降水量 Mean annual precipitation	MAP	mm	潜热通量 Latent heat flux	LE	MJ·m^-2·a^-1
生态系统类型 Ecosystem type	-	-	波文比 Bowen ratio	β	-
叶面积指数 Leaf Area Index	LAI	m²·m^-2	能量闭合度 Energy balance closure	EBC	-
当年平均气温 Mean annual air temperature of the studied year	T_a	℃	能量平衡比率 Energy balance ratio	EBR	-

Fig. 1 Distribution of the data resources of Bowen ratio in typical terrestrial ecosystems in China. CRO, cropland ecosystems; FOR, forest ecosystems; GRA, grassland ecosystems; SHR, shrubland ecosystems; WET, wetland ecosystems.

Fig. 2 Frequency distribution of Bowen ratio (β) in typical terrestrial ecosystems in China.

Fig. 3 Boxing diagram of the Bowen ratio (β) of different ecosystem types. Lowercase letters indicate the results of multiple comparisons. The same letter indicates that the difference of β between ecosystems is not significant (p > 0.5), while different letters indicate significant differences in β between ecosystems (p < 0.5). n is the sample size. CRO, cropland ecosystems; FOR, forest ecosystems; GRA, grassland ecosystems; SHR, shrubland ecosystems; WET, wetland ecosystems.

Fig. 4 Relationships between the Bowen ratio (β), sensible heat flux (H), latent heat flux (LE) and latitude, longitude and altitude of terrestrial ecosystems in China. CRO, cropland ecosystems; FOR, forest ecosystems; GRA, grassland ecosystems; SHR, shrubland ecosystems; WET, wetland ecosystems.

Table 2 Linear regressions between Bowen ratio (β) and geographical, climatic and biological factors in different ecosystems

影响因素 Influencing factor	生态系统类型 Ecosystem type	回归方程 Regression equation	样本数 Sample size	R²
纬度 Lat	FOR	β= 0.016Lat - 0.02	12	0.57^**
经度 Lon	FOR	β= 0.014Lon -1.13	12	0.32^*
年降水量 MAP	GRA	β= -0.00076MAP + 1.46	22	0.22^*
	FOR	β= -0.00021MAP + 0.73	12	0.36^*
	SHR	β= -0.0046MAP + 3.73	4	0.91^*
当年降水量 PPT	GRA	β= -0.00082PPT + 1.43	15	0.41^**
当年降水量 PPT	FOR	β= -0.00025PPT + 0.77	10	0.42^*
年平均气温 MAT	FOR	β= -0.017MAT + 0.74	11	0.45^*
当年平均气温 T_a	FOR	β= -0.020T_a + 0.77	8	0.48^*
净辐射 R_n	GRA	β= -000063R_n + 2.54	20	0.34^***
净辐射 R_n	SHR	β= -0.0021R_n + 7.09	4	0.97^*
叶面积指数 LAI	GRA	β= -0.20LAI + 1.45	12	0.34^*

Table 3 Linear regressions between sensible heat flux (H) and geographical, climatic and biological factors in different ecosystems

影响因素 Influencing factor	生态系统类型 Ecosystem type	回归方程 Regression equation	样本数 Sample size	R²
纬度 Lat	SHR	H =-373.96Lat + 15579.69	4	0.90^*
当年降水量 PPT	FOR	H =-0.20PPT + 1015.80	10	0.41^*
净辐射 R_n	SHR	H= 0.36R_n + 521.99	4	0.99^**

Table 4 Linear regressions between latent heat flux (LE) and geographical, climatic and biological factors in different ecosystems

影响因素 Influencing factor	生态系统类型 Ecosystem type	回归方程 Regression equation	样本数 Sample size	R²
纬度 Lat	GRA	LE= -45.40Lat +2767.46	22	0.36^**
	CRO	LE= -57.21Lat + 3424.40	12	0.48^**
	FOR	LE= -40.80Lat + 3032.86	12	0.62^**
经度 Lon	CRO	LE= -21.36Lon -3604.25	12	0.30^*
经度 Lon	FOR	LE =-36.92Lon +5944.80	12	0.39^*
海拔 Alt	SHR	LE= 0.43Alt -116.08	4	0.89^*
年降水量 MAP	GRA	LE =0.64MAP +683.66	22	0.34^**
	CRO	LE =1.18MAP +500.76	10	0.52^*
	FOR	LE= 0.50MAP + 1203.54	12	0.36^*
	SHR	LE= 2.19MAP - 78.90	4	0.97^**
当年降水量 PPT	GRA	LE= 0.65PPT + 725.23	15	0.44^**
年平均气温 MAT	FOR	LE =45.30MAT +1122.30	11	0.61^**
年平均气温 MAT	SHR	LE= -80.99MAT + 1200.72	4	0.90^*
当年平均气温 T_a	FOR	LE= 54.34T_a + 1024.19	8	0.62^*
净辐射 R_n	GRA	LE= 0.47R_n - 53.39	20	0.51^***
	FOR	LE= 0.58R_n + 39.06	12	0.61^**
	WET	LE= 0.69R_n - 441.67	5	0.77^*

Fig. 5 Relationships between Bowen ratio (β) and mean annual precipitation (MAP), annual precipitation of the studied year (PPT), mean annual air temperature (MAT), mean annual air temperature of the studied year (Ta), net radiation (Rn) and Leaf Area Index (LAI) of China's terrestrial ecosystems. CRO, cropland ecosystems; FOR, forest ecosystems; GRA, grassland ecosystems; SHR, shrubland ecosystems; WET, wetland ecosystems.

Fig. 6 Relationships between sensible heat flux (β) and mean annual precipitation (MAP), annual precipitation of the studied year (PPT), mean annual air temperature (MAT), mean annual air temperature of the studied year (Ta), net radiation (Rn) and Leaf Area Index (LAI) of China's terrestrial ecosystems. CRO, cropland ecosystems; FOR, forest ecosystems; GRA, grassland ecosystems; SHR, shrubland ecosystems; WET, wetland ecosystems.

Fig. 7 The relationships between latent heat flux (LE) and mean annual precipitation (MAP), annual precipitation of the studied year (PPT), mean annual air temperature (MAT), mean annual air temperature of the studied year (Ta), net radiation (Rn) and Leaf Area Index (LAI) of China's terrestrial ecosystems. CRO, cropland ecosystems; FOR, forest ecosystems; GRA, grassland ecosystems; SHR, shrubland ecosystems; WET, wetland ecosystems.

Fig. 8 Path diagrams for the effects of mean annual precipitation (MAP), mean annual air temperature (MAT), net radiation (Rn) and Leaf Area Index (LAI). The statistical results of the structural equation model (SEM) are χ2 = 1.75, df = 2, p = 0.093, RMSEA = 0.038. Solid and dashed arrows represent positive and negative correlations, respectively. The figure for each arrow is the standardized path coefficient. The bold numbers are the explained variance (R2) of LAI, Rn and β, respectively.

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