MODELING SEASONAL VARIATION OF CO2 FLUX IN A SUBTROPICAL CONIFEROUS FOREST USING THE EALCO MODEL

doi:10.17521/cjpe.2007.0140

Abstract

Abstract:

Aims Seasonal drought frequently occurs in the mid-subtropical region of China and commonly combines with high temperature. Our objectives were to test the sensitivity of carbon exchange to this seasonal drought and discuss the influence of seasonal drought on carbon assimilation.

Methods We used flux measurements obtained from eddy covariance technology since October 2002 over a human-planted forest ecosystem at Qianyanzhou (QYZ) (26°44' N, 115°03' E, 110.8 m als.). The EALCO (ecological assimilation of land and climate observations) model is parameterized to simulate the ecosystem carbon exchange process in the human-planted evergreen forest. Simulation results were validated using half-hourly carbon fluxes and daily and annualGPP (gross primary production), NEP (net ecosystem production) and TER (total ecosystem respiration) estimated from eddy covariance measurements.

Important findings In general, the model can effectively simulate the two years' carbon fluxes among soil-plant-atmosphere on hourly, daily and annual scales. Both simulations and observations showed strong impact of drought on GPP in 2003. Compared with 2004, the annual GPP in 2003 was 12.9% lower according to observations (1 610 vs. 1 865 g C·m^-2) and 11.2% lower according to model results (1 637 vs. 1 844 g C·m^-2). The diurnal variations of NEP from both observations and simulations during the period of soil water deficit showed asymmetric format, i.e., the peak value of carbon exchange accrued at a certain time in the morning and then decreased with time. Modeling results indicated that water stress has more influence on photosynthesis than TER, which led to the decrease of NEP. Further analysis suggested that deep soil water content controls canopy photosynthesis in sunny days before noon during soil water stress. Afternoon, both high temperature and deep soil water content eliminate the GPP, and their elimination percents are equal. On cloudy days, radiation and deep soil water content primarily determine the photosynthesis, and temperature becomes a generally minor controlling factor.

Key words: coniferous plantation, carbon flux, EALCO model, ecosystem processes, seasonal drought

MI Na, YU Gui-Rui, WANG Pan-Xing, WEN Xue-Fa, SUN Xiao-Min, ZHANG Lei-Ming, SONG Xia, WANG Shu-Sen. MODELING SEASONAL VARIATION OF CO₂ FLUX IN A SUBTROPICAL CONIFEROUS FOREST USING THE EALCO MODEL[J]. Chin J Plant Ecol, 2007, 31(6): 1119-1131.

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Figures/Tables 12

References 33

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符号 Symbol	描述 Description	取值 Value	单位 Unit	来源 Source
φ_c,min	式(3)	-200	m H₂O	Larcher, 1995; Kimmins, 1997
φ_c,max	式(3)	0	m H₂O	Larcher, 1995; Kimmins, 1997
m	式(6)	7		Ball et al., 1987
b	式(6)	0.008	mol·m^-2·s^-1	Ball et al., 1987
r_m,F	式(10), 叶 Leaf	4.8×10^-8	Kg C·kg C^-1·S^-1	Amthor, 1989
r_m,S	式(10), 茎 Stem	1.6×10^-8	Kg C·kg C^-1·S^-1	Amthor, 1989
r_m,S	式(10), 根 Root	2.4×10^-8	Kg C·kg C^-1·S^-1	Amthor, 1989
A	式(11)	8.8(16.9^*)
S	式(11)	710(710^*)	J·K^-1·mol^-1
H_a	式(11)	36 000(57 500^*)	J·mol^-1
H_dh	式(11)	220 000(226 000^*)	J·mol^-1
H_dl	式(11)	175 000(192 000^*)	J·mol^-1
r_g,X	式(12)	0.42		Amthor, 1989
V_cmax	最大羧化能力 Maximum catalytic activity of Rubisco	35	μmol·m^-2·s^-1	Farquhar et al., 1980
J_max	最大电子传递速 Potential rate of whole-chain electron transport	87	μmol·m^-2·s^-1	Farquhar et al., 1980
TreeDesi	树的密度Density of tree	0.199 1	trees·m^-2	宋霞等, 2004
SLA	比叶面积Specific leaf area	12	m²·kg^-1	李轩然等, 2007
ζ	决定凋落量的系数 Coefficient	0.000 2	(DOY210~250)	本研究 This study
		0.000 65	(DOY250~365)
Clay	粘土百分含量 Percent of clay	18	%	CERN数据 CERN tada
Sand	沙土百分含量 Percent of sand	20	%	CERN数据 CERN tada
SOM	有机物质含量 Content of soil organic matter	3	%	周志田等, 2002
SOILC	土壤碳含量 Content of soil carbon	8.76(6.80^**)	Kg C·m^-2	自运行后2003年1月1日的值 Data simulated from January 1, 2003
SOILN	土壤氮含量 Content of soil nitrogen	0.87	Kg N·m^-2	自运行后2003年1月1日的值 Data simulated from January 1, 2003

符号 Symbol	描述 Description	取值 Value	单位 Unit	来源 Source
φ_c,min	式(3)	-200	m H₂O	Larcher, 1995; Kimmins, 1997
φ_c,max	式(3)	0	m H₂O	Larcher, 1995; Kimmins, 1997
m	式(6)	7		Ball et al., 1987
b	式(6)	0.008	mol·m^-2·s^-1	Ball et al., 1987
r_m,F	式(10), 叶 Leaf	4.8×10^-8	Kg C·kg C^-1·S^-1	Amthor, 1989
r_m,S	式(10), 茎 Stem	1.6×10^-8	Kg C·kg C^-1·S^-1	Amthor, 1989
r_m,S	式(10), 根 Root	2.4×10^-8	Kg C·kg C^-1·S^-1	Amthor, 1989
A	式(11)	8.8(16.9^*)
S	式(11)	710(710^*)	J·K^-1·mol^-1
H_a	式(11)	36 000(57 500^*)	J·mol^-1
H_dh	式(11)	220 000(226 000^*)	J·mol^-1
H_dl	式(11)	175 000(192 000^*)	J·mol^-1
r_g,X	式(12)	0.42		Amthor, 1989
V_cmax	最大羧化能力 Maximum catalytic activity of Rubisco	35	μmol·m^-2·s^-1	Farquhar et al., 1980
J_max	最大电子传递速 Potential rate of whole-chain electron transport	87	μmol·m^-2·s^-1	Farquhar et al., 1980
TreeDesi	树的密度Density of tree	0.199 1	trees·m^-2	宋霞等, 2004
SLA	比叶面积Specific leaf area	12	m²·kg^-1	李轩然等, 2007
ζ	决定凋落量的系数 Coefficient	0.000 2	(DOY210~250)	本研究 This study
		0.000 65	(DOY250~365)
Clay	粘土百分含量 Percent of clay	18	%	CERN数据 CERN tada
Sand	沙土百分含量 Percent of sand	20	%	CERN数据 CERN tada
SOM	有机物质含量 Content of soil organic matter	3	%	周志田等, 2002
SOILC	土壤碳含量 Content of soil carbon	8.76(6.80^**)	Kg C·m^-2	自运行后2003年1月1日的值 Data simulated from January 1, 2003
SOILN	土壤氮含量 Content of soil nitrogen	0.87	Kg N·m^-2	自运行后2003年1月1日的值 Data simulated from January 1, 2003

变量 Item	年 Year	斜率 Slope	截距 Intercept	R²	标准差 Standard deviation	记录数 Observations
净CO₂交换量	2003	0.91	-0.34	0.68	3.66	12 849
Net CO₂ flux (NEP)	2004	0.91	-0.02	0.67	4.17	13 141
	2003~2004	0.91	-0.18	0.68	3.93	25 990

变量 Item	年 Year	斜率 Slope	截距 Intercept	R²	标准差 Standard deviation	记录数 Observations
净CO₂交换量	2003	0.91	-0.34	0.68	3.66	12 849
Net CO₂ flux (NEP)	2004	0.91	-0.02	0.67	4.17	13 141
	2003~2004	0.91	-0.18	0.68	3.93	25 990

变量 Variable	2003		2004
变量 Variable	模拟值 Simulation	估算值 Estimated¹⁾	模拟值 Simulation	估算值 Estimated¹⁾
GPP	1 637.5	1 610.4	1 844.1	1 865.8
NPP	546.8	-	728.9	-
NEP	404.4	387.2	582.1	423.8
R_above	-765.8	-	-768.3	-
R_soil	-467.3	-	-493.7	-536.5
TER	-1 233.1	-1 223.3	-1 262.0	-1 442.0

MODELING SEASONAL VARIATION OF CO₂ FLUX IN A SUBTROPICAL CONIFEROUS FOREST USING THE EALCO MODEL

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