Establishing and validating individual-based carbon budget model FORCCHN of forest ecosystems in China

ACTA ECOLOGICA SINICA Volume 27, Issue 7, July 2007 Online English edition of the Chinese language journal Cite this article as: Acta Ecologica Sinica, 2007, 27(7), 2684−2694.

RESEARCH PAPER

Establishing and validating individual-based carbon budget model FORCCHN of forest ecosystems in China Yan Xiaodong1,*, Zhao Junfang1,2 1 Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: According to the principles of plant physiology, forest ecology and soil environment, the individual-based carbon budget model of forest ecosystems in China was established. The model process included two timesteps: the primary daily process comprises photosynthesis, plant respiration, allocation and litter production, and soil respiration and transfer; the primary annual process consists of allocation between stands, increase of tree height and breast diameter, and production of large amount of litter fall. Through validating net primary productiviry (NPP) and net ecosystem productivity (NEP) at the plot level and at the country level, it was demonsteated that the model can well simulate carbon budget of forest ecosystems in China, so it can also simulate dynamics of carbon budget of forest ecosystems in the past and in the future. Key Words: individual; forest ecosystems; carbon budget; model; validation

Under the influences of Global change and human activity, the biosphere’s carbon cycle has recently been the most important scientific issue focused by International GeosphereBiosphere Program (IGBP), Global Change and Terrestrial Ecosystem (GCTE) of World Climate Research Programme (WCRP) and International Human Dimensions Programme (IHDP) on Global Environmental Change, Land Use/Land Cover (LULC), International Global Atmospheric Chemistry Project (IGACP), Amazon Largescale Atmosphere and Biosphere Experiment (ALABE) and International Flux Observational Research Network (FLUXNET), and Biosphere- Atmosphere Stable Isotope Network (BASIN) and so on[1–3]. In the global carbon cycle, the problems which puzzle scientists are still uncertain underlying the research results of global terrestrial ecosystem’s carbon flux and carbon store estimation and regional distribution[4–6]. Carbon budget of ecosystems can't be directly and fully measured at the regional or global scales, so the model method has been an important means which can not be replaced in the research on terrestrial carbon cycle. At present, models of terrestrial carbon cycles mainly include biogeographic models (such as BIOME[7] and MAPSS[8]) and biogeochemical mod-

els (such as CENTRY[9], BIOME-BGC[10] and TEM[11]). The disadvantages of the two types of models are that they ignore the correlations and interactions of ecosystem’s structure and function, only simulating the change in carbon store and flux of ecosystems from one balance to another balance[12]. However, with the global change, the carbon cycling of terrestrial ecosystems will change with interactions of climate and land coverage, and with ecosystem’s structure, composition and function, and it also brings great effects to the processes of terrestrial carbon cycle. Consequently, establishing global dynamic vegetation models, i.e., establishing models reflecting the relationships of terrestrial ecosystem’s structure and function with the global change, has been a major aim for GCTE plan[13]. Recently, analogous models have been used in the research on forest carbon cycle, such as FORSKA model[14], NEWCOP model[15], etc. Because of the complexities of determining model input variables and parameters, there are still difficulties in using these models at large scale. For this purpose, at the country scale, a model based on the individuals has been established, which can reasonaly explain the dynamic mechanism of young forest’s carbon budget, and its aim is to provide scientific evidence for reasonally esti-

Received date: 2006-12-15; Accepted date: 2007-05-28 *Corresponding author. E-mail: [email protected] Copyright © 2007, Ecological Society of China. Published by Elsevier BV. All rights reserved.

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

mating carbon absorbency and dynamics of forest ecosystems under the research background of global change in the future.

1

Model and data source

1.1 Model description The carbon budget model FORCCHN (Forest Ecosystem Carbon Budget Model for China) of forest ecosystems in China was established based on individuals. The model was drived by daily meteorology conditions, and then simulated carbon budget of each tree one by one in some area. Through summing or coupling soil carbon cycle model, the model could be used to calculate carbon budget of forest ecosystems in unit area. The main processes considered in the model and the flow charts were as follows (Fig.1): The FORCCHN model was built with four major characteristics in mind. Firstly, the carbon, water and nutrient cycles must be fully coupled in the soil-plant-atmosphere system. Secondly, the external constraints on the model’s behaviour and the driving forces for the model must be the same as those which operated based on individuals. That is, the constraints must, as far as possible, be fundamental biological and physico-chemical processes and the driving variables can not be decided by current climate and statistical relations of ecosystems in advance. Thirdly, the carbon budget of ecosystems is decided by individuals’ growth of stands, so it can be estimated reliably. Fourthly, the model constructed is capable of predicting dynamic processes of the carbon budget of forest ecosystems induced by climate changes and equilibrium responses to climate changes in the future. The obvious difference to Hybrid[16,17] is that the model can be used to simulate not only individuals but also individuals’ growth year after year (breast diameter and tree height). The resolution of the

model is 10 km×10 km, and the model supposes that forest vegetation in every grid is uniform, and soil in forests is independent of mature forests and young forests. 1.2 Major control equations of the model The carbon budget equations of individuals and stands are calculated as follows: dxi = GPPi − tresp × ( RM i + RGi ) − Li dt d ( ∑ xi ) = ∑ GPPi − tresp × (∑ RM i + ∑ RGi ) − ∑ Li dt where xi denotes the carbon increment, GPPi the gross primary productivity, RMi the maintenance respiration, RGi the growth respiration, Li the litter amount with unit kgC·d–1, and tresp the effect of air temperature on plant respiration which changes from 0 to 1. 1.2.1 Primary daily process The primary daily process comprises photosynthesis, plant respiration, allocation and litter production, and soil respiration and transfer. It is assumed that the ith individual belongs to the jth plant function type. Photosynthesis: Gross primary productivities of each individual tree and stands in unit area are given by[18,19]: GPPi=min(GPPMi × fc × fdry × fT, an × aNS)

GPPM i =

2 × Am j × DL Kl j

⎡ 1 + 1 + Kl j × Sl j × PARi / Am j Ln ⎢ ⎢⎣1 + 1 + Kl j × Sl j × PARi × exp(− Kl j × LAI i ) / Am j

⎤ ⎥ ⎥⎦

where GPPi is the daily gross primary productivity of each individual (kgC·d–1), GPPMi the daily gross primary productivity of stand (kgC·d–1), DL the possible sunshine duration (h),

Fig. 1 Main processes and flow charts of FORCCHN model

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

PARi the photosynthetic active radiation of canopies at noon –2

(W·m ), Amj the possible maximal photosynthesis of leaves (kgC·m–2·h–1), Klj the extinction coefficient, Slj the initial slope of light intensity and photosynthesis [kgC·(m–2·h–1)/(W·m–2)], LAIi the leaf area index, fc the effect of carbon dioxide on the gross primary productivity, fdry the effect of water on the gross primary productivity, fT the effect of temperature on the gross primary productivity, an×aNS the effect of soil active nitrogen on the gross primary productivity, aNS the soil active nitrogen amount (kgN·m–2), and an=150. Respiration: The plant’s autotrophic respiration includes maintenance respiration and growth respiration[20]. The formulas which figure out maintenance respiration and growth respiration[21] are shown by: 2 1 ⎡ DLe0.069315(Td −15) − 0.009(Td −15) + 24 ⎣ 2 (24 − DL)e0.069315(Tn −15) − 0.009(Tn −15) ⎤ rk Cik ⎦ RGi = rg ⋅ (GPP − RM i )

RM ik (Td , Tn , DL) =

where RMik is the daily maintenance respiration (kgC·d–1); Td the daily average air temperature ( ℃ ); Tn the mean night-time air temperature (℃); DL the possible sunshine duration (h); rk the relative respiration rate of leaves, branches, stems, roots and fine roots at 15℃ (d–1); Cik the carbon pool amount (kgC), and when k denotes leaves or fine roots, Cik is leaf content or fine root content. When k denotes stems or roots, Cik is sapwood content (kgC); RGi the daily growth respiration (kgC·d–1); rg the growth respiration coefficient, rg=0.25; GPP the daily gross photosynthesis (kgC·d–1). Litter production: The litter fluxes of leaves and fine roots are computed as follows[21]: Lik=lkCik where Lik is litter fluxes of leaves or fine roots (kgC·d–1); Cik the corresponding carbon pool (kgC·d–1); lk the relative littering rate (d–1). Allocation: According to the allocation mechanism of photosynthesis production from Arrora and Boer[22] and Salter et al[23], the model is supposed that in daily processes, net photosynthesis production is only partitioned to leaves, fine roots and litters, and other photosynthesis productions are stored in the assumed buffer carbon pool. Therefore, the increments △Cik of leaves and fine roots and the possible maximal leaf carbon content or fine root carbon content are[24]: ⎧ min(Cik max − Cik , d k ⋅ BFi ) ΔCik = ⎨ ⎩0 Cik max = ek Dbi2

BFi > 0 BFi = 0

where BFi is the daily buffer carbon pool of the ith tree

(kgC); dk is the partition proportion coefficient, dfine root+dleaf=1; ek is the proportion coefficient (kgC·m–2); Dbi is the diameter at branch height (m). Soil organic matter respiration and transfer progress: The model supposes that soil process is day-to-day, and adoptes the modified soil carbon budget CENTURY model which is fit in forest soils[25]. The CENTURY model was originally developed for simulating and forecasting carbon cycle and productivity of grassland[26,27], and now it has been widely used for forest ecosystems. Through simulating the biological geochemistry cycle of carbon, nitrogen and phosphorus and some driving factors such as temperature and precipitation, it can forecast productivity of ecosystems[28]. The major processes and formulas in the modified CENTURY model[25] are described as follows: Leaf litter and fine root litter are sub-divided between soil structural pool and metabolic litter pool and the proportions are: fm=0.85 − 0.018Nr/Lr fs=1 − fm where fm is the proportion of fresh litter classified as metabolic litter, fs the proportion of fresh litter classified as structural litter, Nr and Lr are the respective concentrations of nitrogen and lignin in fresh litter. The decomposition rate, respiration release and carbon transported to other carbon pools are calculated as:

Dk = sr gT g we −bLs CSk Rk=pkDk SDkj=pj(Dk-Rk)

∑ pj =1 gT (T ) = e

3.36(T − 40) T + 31.79

⎛ ws ⎞ − 1⎟ g w (W ) = 1 − ⎜ ⋅ ff FC ⎝ ⎠

2

where Dk is daily decomposition amount of the kth carbon pool (kgC·m–2·d–1); sr the referenced relative decomposition rate (d–1); gT and gw the coefficients that describe the effect of temperature and water on the decomposition process; b is constant, 5.0; Ls the lignin content in metabolic litter, and otherwise 0; Csk the difference of soil carbon pool and soil lignin pool (kgC·m–2); Rk the respiration release amount (kgC·m–2·d–1); pk the proportion of respiration; SDkj the carbon amount transportated (kgC·m–2·d–1); pj the proportion transportated to the jth carbon pool; ws the water content of soil or litter (cm); f is constant, 0.6; FC the field holding capacity (cm); T the soil temperature (℃). 1.2.2 Primary annual process The primary annual process consists of allocation between stands, increase of tree height and breast diameter and production of large amount of litter fall. Every year the carbon cycles

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

through nonindividual death, such as litter production (including flower and fruit). There are two thresholds: if buffer carbon pool of each individual at the end of every year is bigger than the first threshold, the litter production of flower is maximal. And if buffer carbon pool of each individual at the end of every year is bigger than the second threshold, the litter productions of flower and fruit are maximal. The formulas are given by: BFi ≤ lm1 ⎧ BFi ⎪ Li , year = ⎨lm1 + y ( BFi − lm1 ) lm1 < BFi ≤ lm2 ⎪lm BF i > lm2 ⎩ 2 DCi = BFi − Li , year where Li,year is the litter production that year (kgC); BFi the buffer carbon of each individual at the end of that year (kgC); lm1and lm2 the first and the second thresholds, respectively (kgC); DCi the carbon store increment (kgC), changed by 95% of its carbon amount into other organs including the increase of tree height, basal diameter, height and diameter of branches. DCi also decides sapwood amount and possible maximal leaf area index or wood respiration in the next year, and so on. Wood increment, basal diameter increment and height increment can be shown that: DCi=fwood − f’wood Δh=cp ×Δd fwood=fstem+ftwing+froot fstem=2.019595243 × astem × d2 × h ftwing=1.121997537 × astem × d2 ×h ×(1 − m3)2 ×(1 − m) froot=1.5 ×2.019595243 ×astem ×d2 ×h ×[(1 − n3)2 ×(1+n) − 1] where DCi is the Wood increment (kgC); fwood the wood biomass in the last year(kgC); f’wood the wood biomass in this year(kgC); fstem the stem biomass (kgC); ftwig the twig biomass (kgC); froot the root biomass (kgC); d the basal diameter (m); Δd the increment of basal diameter (m); h the tree height (m); Δh the increment of tree height (m); b the twig height (m); hr the root depth (m); astem the bulk density of wood (kgC·m–3); cp is constant, and decided by illumination grads of tree canopy; m=b/h; n=hr/h. 1.3 Parameters, major initial conditions and boundary conditions 1.3.1 Parameters The model parameters consist of soil parameters and tree type parameters. The trees include nine basic types, taking into account wide distributions and their generic characteristics, such as shade tolerance (peak) species and sun tolerance species (pioneer). The soil parameters include soil organic matter parameters, litter pool decomposition parameters and soil physical parameters, and the soil physical parameters are principally dependent on geography position. The basic pa-

rameters of soil carbon cycle and the physiological and ecological parameters are shown respectively in Table 1 and Table 2. Table 1 Parameters of soil decomposition Symbol

Unit

Carbon pool

Value

S1

d–1

Above-ground metabolic litter pool

0.021

S2

d

–1

Above-ground structural litter pool

0.1

d

–1

Below-ground metabolic litter pool

0.027

S4

d

–1

Below-ground structural litter pool

0.13

S5

d–1

Fine woody litter pool

0.01

S6

d–1

Coarse woody litter pool

0.002

S7

d

–1

Below-ground coarse litter pool

0.002

S8

d

–1

Active soil organic matter pool

0.042

S9

d–1

Slow soil organic matter pool

0.001

S10

d–1

Resistant soil organic matter pool

3.5×10–5

S3

Note: The parameters were from the literature[25].

1.3.2 Major initial conditions and boundary conditions The major initial conditions and boundary conditions of this study are based on the 10 km×10 km grid database. The initial conditions include, from 1980 to 2002, daily maximal, minimum and average air temperature (℃) and precipitation (cm), relative humidity (%), wind speed (m·s–1) and total radiation (W·m–2). The boundary conditions include soil field capacity (cm), buck density (kgC·m–3), carbon pool (kgC·m–2), nitrogen pool (kgN·m–2), soil water content (cm), sand content (%), silt content (%), clay content (%), maximal leaf area index (m2·m–2) and minimum leaf area index (m2·m–2) in 1980 and forest cover rate (%). This simulation began on January 1st, 1980, and simulated carbon budget of forest ecosystems step by step in every grid. When validating the simulation, the simulation value of net primary productiviry (NPP) was an average from 1993 to 2002, and the simulated value of net ecosystem productivity (NEP) in comparison with flux observation was the monthto-month value in 2002 in the grids including the flux observation station. 1.4 Data used for validation Data were compiled from the literature on observation values of forest productivity, including 690 sample values from Luo (1996)① and 193 observation values from Liu[16]. Using these data, the model was tested in its ability to simulate dynamics of carbon budget of forest ecosystems in the past and in the future. In calculation, according to the position of every station, the average simulation value including the observation station from 1993 to 2002 was regarded as the net primary

①

Luo T X. Patterns of net primary productivity for Chinese major forest types

and its mathematical models. Beijing, China: Commission for Integrated Survey of Natural Resources, Chinese Academy of Sciences. 1996.

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

productivity.

2

Model validation

At present, one of the difficulties in regional models is to validate the models entirely[30]. There are four methods to validate carbon cycle at the large scale: (1) in comparison with short term data; (2) time and space instead; (3) rebuilding historical materials; (4) in comparison with other models. This study uses the comparison method with other observations and simulation values to demonstrate the model’s validity to some extent. 2.1 Validation at sample plot level In comparison with the observation values in the literature

mentioned above, the model can well simulate the net primary productivity (Fig. 2). The correlation coefficient is more than 0.77 and is significantly correlated. When the intercept is set to be zero, the slope of regression between observation and simulation is 1.009. So it is obvious that the model could well simulate NPP of forests, but at some region the values are lower. The reasons are: (1) the high observation values are mainly in Southern, China, where there are lots of artificial forests. But in simulation, the model treats these artificial forests as crude forests or crude hypo-forests; (2) the observation values are higher. In some areas, the values are even more than 1.8 kgC⋅m–2⋅a–1 (Fig. 2), and they are higher than the value (1.5 kgC⋅m–2⋅a–1) from Lieth[31].

Table 2 Parameters of physiology and ecology in the FORCCHN model Parameters of physiology and ecology

Rain forest tree

Evergreen broad-leaved tree

Deciduous broad-leaved tree

Evergreen conifer tree Deciduous

Shade

Sun

Shade

Sun

Shade

Sun

Shade

Sun

tolerance

species

tolerance

species

tolerance

species

tolerance

species

conifer tree

Lo

5.5

11.0

5.5

11.0

5.5

11.0

5.5

11.0

11.0

Am

5.5×10–4

5.5×10–4

5.5×10–4

5.5×10–4

5.0×10–4

5.0×10–4

5.0×10–4

5.0×10–4

5.0×10–4

Sl

1.3×10–5

1.3×10–5

1.3×10–5

1.3×10–5

1.3×10–5

1.3×10–5

1.3×10–5

1.3×10–5

1.3×10–5

Kl

4.5×10–1

4.5×10–1

4.5×10–1

4.5×10–1

4.0×10–1

4.0×10–1

4.0×10–1

4.0×10–1

3.5×10–1

rL

–3

–3

–3

–3

–3

–3

–3

–3

1.2×10–2

–3

2.0×10–3

–3

2.5×10–3

rW

2.0×10

–3

1.0×10

–3

2.0×10

–3

1.0×10

–3

rR

1.5×10

1.5×10

lm2

0.50

0.50

2.0×10

–3

1.0×10

–3

1.5×10

2.0×10

–3

1.0×10

–3

1.5×10

0.40

0.40

6.0×10

–3

2.0×10

–3

2.5×10

0.40

3.0×10

–3

2.0×10

–3

2.5×10 0.40

3.5×10

–3

2.0×10

–3

2.5×10

0.50

3.5×10 2.0×10 2.5×10 0.50

0.50

CNL

40.0

40.0

45.0

45.0

40.0

40.0

60.0

60.0

50.0

CNW

200.0

200.0

200.0

200.0

200.0

200.0

200.0

200.0

200.0

CNR

40.0

40.0

45.0

45.0

40.0

40.0

60.0

60.0

50.0

Hmax

40.0

60.0

50.0

40.0

40.0

40.0

60.0

60.0

50.0

Dmax

2.0

3.0

2.0

1.5

2.0

1.5

2.0

2.0

2.0

Amax

200.0

100.0

400.0

200.0

400.0

200.0

1000.0

300.0

500.0

eL

600.0

600.0

600.0

600.0

200.0

700.0

700.0

700.0

300.0

eR

20.0

20.0

20.0

20.0

30.0

30.0

15.0

15.0

28.0

cLAIL

15.0

15.0

15.0

15.0

45.0

20.0

18.0

18.0

40.0

astem

350.0

350.0

350.0

350.0

350.0

350.0

350.0

350.0

350.0

Tmin

5.0

5.0

3.0

1.0

–1.0

–5.5

–5.5

–2.5

–5.5

Topt

27.0

29.0

27.0

25.0

23.0

20.0

18.0

23.0

16.0

Tmax

50.0

50.0

50.0

50.0

45.0

45.0

40.0

40.0

35.0

dry

1.0

0.8

0.9

0.8

0.8

0.6

0.9

0.7

lL

2.0×10–3

2.0×10–3

2.0×10–3

2.0×10–3

1.1×10–4

1.1×10–4

2.0×10–3

2.0×10–3

1.1×10–4

Lr/Nr

40.0

40.0

40.0

40.0

30.0

50.0

80.0

80.0

50.0

lR

–5

5.0×10

–5

5.0×10

–5

5.0×10

–5

5.0×10 [29]

Note: The parameters are calculated according to the literature

–5

4.0×10

–5

4.0×10

–5

8.0×10

0.5

–5

8.0×10

8.0×10–5

. Lo the photosynthesis compensate point; Am the Maximal photosynthesis; Sl the initial slope

of light intension and photosynthesis[kgC·(m–2·h–1)/(W·m–2)]; Kl the extinction coeffinient; rL the relative breath rate of foliage (d–1) ; rW the relative breath rate of wood (d–1) ; rR the relative breath rate of root(d–1); lm2 the threshold value of fruit; CNL the C:N ratio of foliage; CNR the C:N ratio of wood; CNR the C:N ratio of root; Hmax the maximal tree height (m); Dmax the maximal tree diameter (m); Amax the maximal tree age (a); eL the coefficient of leaf content (kgC·m–2); eR the coefficient of root weigh (kgC·m–2); cLAIL the coefficient of leaf area (m2·kgC–1); astem the bulk density of wood (kgC·m–3); Tmin the lowest temperature of photosynthesis (℃); Topt the optimum temperature of photosynthesis (℃); Tmax the highest temperature of photosynthesis (℃); dry the capability of enduring drought; lL the relative litter rate of leaves(d–1); Lr/Nr the ration of lignin and nitrogen content; lR the relative litter rate of root(d–1).

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

2.2 Validation at global total amount level It is now well accepted that NPP and NEP of forest ecosystems in China has been studied for some times. For example, ① Arrota et al[22], Ouyang et al[32], Liu (2001) , Cao[33], He et al[34], Pan et al[35] and Sun et al[36] have used different models to simulate NPP of forests or all vegetations. As far as NEP is concerned, Dai[37], Streets et al[38], Gong et al[39] and Fang et al[40] have adopted different methods to study NEP in China. In short, the carbon dioxide is absorbed by the forest ecosystem in China, and global NEP is between 0.7 and 1.58 Pg C·a–1. The average NEP is about 1.22 Pg C·a–1. Fig. 3 illustrates the comparison of the simulation by using FORCCHN model with other research results. From above, it is known that the estimation based on remote sensing is lower, the estimation based on data analysis or ecosystem process models is moderate, and the estimations based on land surface process models of AVIM and TEM are higher owing to imparticular vegetation types. From Fig. 3, it is also known that the result by using FORCCHN model is close to Ouyang’s estimation, and they both are higher than other average levels. Fig. 4 reflects this FORCCHN model’s performance in simulating NEP. Even including all vegetation, most estimations are lower. In view of NPP of forests in China being about 1.22 PgC·a–1, the value is considered credible. The forests in China are mainly young forests or artificial forests, so the NEP is bigger. However, the NEP calculated by Fang et al[40] is lower (0.04 PgC·a–1), so are the results by Cao et al[33] and Gong et al[39]. But the NEP of the FORCCHN model is higher (0.21 PgC·a–1), in virtue of young forests or artificial forests. 2.3 Validation of the spacial difference of NPP based on global total amount Fig. 5 illustrates the spacial distribution of NPP of forest ecosystems in China, simulated by using FORCCHN model during 2000. The total amount of NPP is mainly focused on east monsoon regions of Northeast, North, Middle and South China and a little in northwest arid regions and Qinghai-Tibet plateau. Along the longitude, the distribution trend of NPP is that the higher in southeast, and lower in northwest. The reason is that in northwest regions the vegetations covered are meager, the climate is dry but humid, and the water heat is abundant in east regions, so the vegetations grow well there. The distribution of NPP accords with the averaged NPP from CEVSA during 1981–2000[41] (Fig. 6).

3

ecology and soil environment, the individual-based carbon budget model of forest ecosystems (FORCCHN) in China is established. FORCCHN can explain the dynamics of carbon budget of young forests in China. The correlation coefficient between simulative results and observed data is 0.77, which is

Fig. 2 Comparison of the NPP simulation with the observation

Fig. 3 Comparison of the NPP simulation with the observation at the country level *the number of references

Conclusions and discussion According to the principles of plant physiology, forest ①iu M L. Studies on land uses/cover change and carbon and productivity of terrestrial ecosystem. Beijing, China: Institute of Remote Sensing Applications, Chinese Academy of Sciences. 2000.

Fig. 4 Comparison of the NEP simulation with the observation at the country level

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

Fig. 5 Averaged distribution of forest NPP from FORCCHN during 2000 (gC⋅m–2⋅a–1)

Fig. 6 Averaged NPP from CEVSA during 1981–2000

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

higher than that of other research results. The spatial distribution of NPP in China has the similar results. FORCCHN can well simulate carbon budget of forest ecosystems in China, so it can also simulate dynamics of carbon budget of forest ecosystems in the past and in the future. The validation of FORCCHN shows that our model can simulate relationships between forest carbon budgets and stand age in Northeast and Northwest China. However, there still exist uncertainties in our simulation processes, which need further research to be conducted. First of all, our model doesn’t consider every process in forest ecosystems, such as canopy interception, surface runoff and actual evapotranspiration. Secondly, parameters and initial fields are not identical with the fact. Finally, there is huge uncertainty in the data that are used to model validations. Besides, meteorologic factors, such as radiation, precipitation, temperature, are the basic driving forces in the process of forest carbon fixation in our model. Therefore, if FORCCHN is coupled with climate models in the future, it can well evaluate the ability to absorb the emissions and make the effects of climate changes on forest ecosystems in China more impersonal and quantificational.

Acknowledgements The project was supported by the Major State Basic Research Development Program of China（973 program) (No. 2002CB412500) and Innovative Item of Knowledge Chinese Academy of Science (No. KZCX1-SW-01-11)

References

model. Landscape Ecology, 1992, 7(1): 27–43. [ 9 ] Parton W J. Observation and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochemical Cycles, 1993, 7(4): 785–809. [10] White M A, Thornton P E, Runing S W. A continental phenology model for monitoring vegetation response to interannual climatic variability. Global Biogeochemical Cycles, 1997, 11(2): 217–234. [11] Melillo J M, McGuire A D. Global climate change and terrestrial net primary production. Nature, 1993, 363: 234–240. [12] Wang Y X, Zhao S D. Modelings of terrestrial carbon cycling. Chinese Journal of Applied Ecology, 1998, 9(6): 658–664. [13] VEMAP Members. Vegetation/ecosystem modeling and analysis project: Comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem response to climate change and CO2 doubling. Global Biogeochemical Cycles, 1995(4): 407–437. [14]

Prentice I C. Biome modelling and carbon cycles. In: The Global Carbon Cycles, NATO ASI Series. Heidelberg: SpringerVerlag, 1993, 115: 219–238.

[15] Yan X D, Zhao S D, Yu Z L. Modeling growth and succession of northeastern China forests and its applications in global change studies. Acta Phytoecologica Sinica, 2000, 24(1): 1–8. [16] Friend A D. The prediction and physiological significance of tree height. In: Solomom A M, Shugart H H eds., Vegetation Dynamic and Global Change. New York: Chapman and Hall, 1993. 101–115. [17] Friend A D, Stevens A K, Knox R G, Cannell M G R. A process-based terrestrial biosphere model of ecosystem dynamics (Hybrid v.3.0). Ecological Modelling, 1997, 95: 249–287. [18] Oikawa T. Simulation of forest carbon dynamic based on dry

[ 1 ] Steffen W, Tyson P. Global change and the earth system: a planet under pressure. IGBP Science, 2001, 4: 33. [ 2 ] IPCC. Special Report of Emission Scenarior of the Intergovernment Panel on Climate Change. In: Nakicenovic N, Sward R ed., UK: Cambridge University Press, 2001. 570. [ 3 ] Global Carbon Project. Science Framework and Implementati-

matter production model: 1. fundamental model structure of a tropical rainforest ecosystem. Botanical Magazine Toyko, 1985, 98: 225–238. [19] Yan X D, Zhao S D. Simulation model of carbon storage dynamics in temperate broadleaved-coniferous mixed forest ecosystem. Chinese Journal of Ecology, 1995, 14(2): 6–12.

on. Earth System Science Partnership (IGBP, IHDP, WCRP,

[20] McCree K J. Equation for the rate of dark respiration of white

DIVERSTTAS) Report No.1. Global Carbon Project Report

clover as function of dry weight, photosynthesis and tempera-

No. 1, Camberra, 2003. 68.

ture. Crop Science, 1974, 14: 509–514.

[ 4 ] Tans P P, Fung I, Takahasi T. Observational constraints on the

[21] Ruimy A, Dedieu G, Saugier B. TURC: A diagnostic model of

global atmospheric carbon budget. Science, 1990, 247: 1431–

continental gross primary productivity and net primary produc-

1438.

tivity. Global Biogeochemical Cycle, 1996, 10(2): 269–285.

[ 5 ] Ciais P, Tans P P. A large northern hemisphere terrestrial CO2

[22] Arrota V K, Boer G J. A parameterization of leaf phenology

sink indicated by the 13C/12C ratio of atmospheric CO2. Science,

for the terrestrial ecosystem component of climate models.

1995, 269: 1098–1102. [ 6 ] IPCC. Climate change 2001—the Third Assessment Report of the IPCC, 2002. [ 7 ] Prentice I C. A simulation model for the transient effects of climate change on forest landscapes. Ecological Modelling, 1993, 65: 51–70. [ 8 ] Neilson R P, King G A, Koerper G. Toward a rule-based biome

Global Change Biology, 2005, 11(1): 39–59. [23] Salter M G, Franklin K A, Whitelam G C. Gating of the rapid shade-avoidance response by the circadian clock in plants. Nature, 2003, 426: 680–683. [24] Shinozak K, Yoda K, Hozumi K, Kira T. A quantitative analysis of plant form—the pipe model theory, I. Basic analyses. Japanese Journal of Ecology, 1964, 14: 97–105.

YAN Xiaodong et al. / Acta Ecologica Sinica, 2007, 27(7): 2684–2694

[25] Kirschbaum M U F, Paul K I. Modelling C and N dynamics in

[34] He Y, Dong W J, Ji J J, et al. The net primary productivity

forest soils with a modified version of the CENTURY model.

simulation of terrestrial ecosystems in China by AVIM. Ad-

Soil Biology and Biochemistry, 2002, 34(3): 341–354.

vance in Earth Sciences, 2005, 20(3): 345–349.

[26] Parton W J, Mckeown B, Kirchner V, Ojima D S. Century

[35] Pan Y D, Melillo J M, Kicklighter D W, Xiao X, McGuire A D.

Users’ Mannul. Fort Collins, CO: Natural Resource Ecology

Modeling structural and functional responses of terrestrial

Laboratory, Colorado State University. 1992.

ecosystems in China to changes in climate and atmospheric

[27] Parton W T, Scurlock J M O, Ojima D S, Gilmanov T G, Scho-

CO2. Acta Phytoecologica Sinica, 2001, 25(2): 175–189.

les R J, Schimel D C, Kiechner T, Menaut H C, Seatedt T,

[36] Sun R, Zhu Q J. Studies on terrestrial net primary productivity

Garcia M E, Kamnalrut A, Kinyamario J L. Obervations and

and seasonal change. Journal of Remote Sensing, 2001, 5(4):

modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochemical Cycles, 1993, 7: 785–809. [28] Huang Z L. Application of a CENTURY model to management effects in the productivity of forest in Dinghushan. Acta Phytoecologica Sinica, 2000, 24(2): 175–179.

300– 305. [37] Dai M H, Zhai W D, Lu Z M, et al. Regional studies of carbon cycles in China: progress and perspectives. Advance in Earth Sciences, 2004, 19(1): 120–130. [38] Streets D G, Bond T C, Carmichael G R, Fernandes S D, Fu Q, He D, Klimont Z, Nelson S M, Tsai N Y, Wang M Q, Woo J H,

[29] Goto N, Sakoda A, Suzuki M. Modelling of soil carbon dy-

Yarber K F. An inventory of gaseous and primary aerosol

namics as a part of carbon cycle in terrestrial ecosystem. Eco-

emissions in Asia in the year 2000. Journal of Geographysical

logical Modelling, 1994, 74(3-4): 183–204.

Research, 2003, 108(21): 8809.

[30] Yu M, Gao Q, Xu H M, et al. Response of vegetation distribu-

[39] Gong P, Xu M, Jin C, Jing M C, Ye Q, Greg B, Ji Y L, Shao Q

tion and primary production of terrestrial ecosystem of China

W. A preliminary study on the carbon dynamics of China's

to climate change. Quateranry Sciences, 2001, 21(4): 281–293.

terrestrial ecosystems in the past 20 years. Earth Science Fron-

[31] Lieth H, Whittaker R H. Primary Productivity of the Biosphere. New York: Springer-Verlad Press. 1975. [32] Ouyang Z Y, Wang X K, Miao H. A primary study on Chinese terrestrial ecosystem services and their ecological-economic values. Acta Ecologica Sinica, 1999, 19(5): 607–613. [33] Cao M K, Tao B, LI K R, et al. Change of carbon flux of terrestrial ecosystem in China from 1981 to 2000. Acta Botanica Sinica, 2003, 45(5): 552–560.

tiers, 2002, 9(1): 55–61. [40] Fang J Y, Chen A P, Peng C H. Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 2001, 292(5525): 2320–2322. [41] Tao B, Li K R, Shao X M, et al. Temporal and spatial pattern of net primary production of terrestrial ecosystem in China. Acta Geographica Sinica, 2003, 58(3): 372–380.