Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands

Tan Kun

Introduction Data sets Results and discussion Conclusion

1. Introduction (1)

average altitude above 4000 m; 44% of the total grassland area of China; 6% of the worldwide grassland area

unique climate regime and low intensity of human disturbance: low temperature, precipitation in growing season, high solar radiation

sensitive region to climate change

1. Introduction (2)

The primary objective

Using ORCHIDEE model to quantify the state of the carbon cycle of the high elevation Qinghai-Tibetan grassland biome and evaluate its response to future warming

(b)(a)

Fig. Spatial distribution of mean annual temperature (a) and annual precipitation (b)

Vegetation distribution Climate data

2. Data sets (1)

Monthly air temperature, air temperature amplitude, precipitation, number of precipitation days, and air relative humidity data (0.2°, average of 1980-1990) were derived through interpolating observing climate data from 125 Chinese meteorological stations around Qinghai-Tibetan Plateau (39 of stations are located over Qinghai-Tibetan grasslands);

soil temperature (average of 1980-1990) at 20 cm depth across 30 meteorological stations at Qinghai-Tibetan grasslands

Fig. Grasslands type of Qinghai-Tibetan Plateau, and the location of Haibei, climatic stations, and the county set soil profiles

2. Data sets (2) Eddy-covariance data Haibei flux site, 2002-2004

Satellite-derived LAI datasetAt Haibei site (2002-2004):

FAPAR (EC-JRC), 6 km / 10 days

LAI = -1/k × ln (1 - FAPAR)

At 39 meteorological stations (Jul-Aug in 2001):

LAI (GIMMS), 0.25°

FAPAR (EC-JRC), 0.5°

Soil carbon inventory data

China’s Second National Soil Survey database (1979-1985): 2473 typical soil profiles

Extracted SOCD at 119 typical grassland soil profiles distributed over 51 counties of Qinghai-Tibetan; averaged SOCD of all profiles within the same county, due to the lack of detailed geographical coordinate for each profile.

3. Results

3.1 Calibration of ORCHIDEE

3.1.1 Flux controlling parameters improved from eddy-covariance measurements

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TE

R (

gC/d

ay/m

2)

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DOY

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TER(modeled)TER(measured)

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E (

gC/d

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1 31 61 91 121 151 181 211 241 271 301 331 361

NEE (modeled)NEE(measured)

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1 31 61 91 121 151 181 211 241 271 301 331 361

GP

P (

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1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

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T (

°C)

GPP(modeled)

GPP(measured)T (°C)

0.0

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LA

I

FAPAR (average 2002-2004)

FAPAR (2002)

Modeled (2002)

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

FAPAR (average 2002-2004)

FAPAR (2003)

Modeled (2003)

1 31 61 91 121 151 181 211 241 271 301 331 361

FAPAR (average 2002-2004)

FAPAR (2004)

Modeled (2004)

ORCHIDEE model V0 version

Observed attributes of Qinghai-Tibetan grasslands point out to lower LAI, higher specific leaf area (SLA), shorter leaf age, and lower shoot/root ratio than the V0-version.

Attributes:doubled SLA to 0.0288 m2 (g C)-1 [He et al., 2006], shortened the critical leaf age from 180 days to 70 days [Eckstein et al., 1999], and decreased the initial allocation of shoot/root ratio in growing season from 2/1 to 1/2 [Hui & Jackson, 2006].

Phenology:GDD_crit = 270 + 6.25 × Tl + 0.03125 × Tl2 (V0 version)GDD_crit = 220 + 6.25 × Tl + 0.03125 × Tl2Leaf onset starts after actual GDD exceeds GDD_crit.

T_crit = -1.375 + 0.1 × Tl + 0.00375 × Tl2 (V0 version)T_crit = 6.375 + 0.1 × Tl + 0.00375 × Tl2Leaf turnover speeds up after actual temperature falls under T_crit

constrain LAI to remain below 5% of LAImax during the first 7 days of the growing season, instead of 50% of LAImax during the first 14 days in the V0 version

These parameters adjustments altogether defined a new model version called Version-1 (V1), calibrated to match the observed biophysical parameters and phenology.

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DOY

LAI

From FAPAR (10-day, average 2002-2004)

Modeled

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DOY

GP

PGPP(modeled)

GPP(measured)

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DOY

NE

E

NEE(modeled)

NEE(measured)

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-1

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0 30 60 90 120 150 180 210 240 270 300 330 360

DOY

RE

RE(modeled)

RE(measured)

Figure 2

ORCHIDEE model V1 version

Change Q10 from 2 (Version V0) to 3 according to the observation of Peng et al. [2009]

f(T) = exp [ ln (Q10) × ( T – 30 ºC ) / 10 ºC ]This higher Q10 value at lower temperatures defines a new model version, called Ver

sion-2 (V2)

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2

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1 31 61 91 121 151 181 211 241 271 301 331 361

GP

P (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

GPP(modeled)

GPP(measured)

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2

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1 31 61 91 121 151 181 211 241 271 301 331 361

NE

E (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

NEE(modeled)NEE(measured)

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0

2

4

6

8

1 31 61 91 121 151 181 211 241 271 301 331 361

TE

R (

gC/d

ay/m

2 )

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

1 31 61 91 121 151 181 211 241 271 301 331 361

RE(modeled)RE(measured)

0.0

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1.0

1.5

2.0

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1 31 61 91 121 151 181 211 241 271 301 331 361

LA

I

FAPAR (average 2002-2004)

FAPAR (2002)

Modeled (2002)

1 31 61 91 121 151 181 211 241 271 301 331 361

DOY

FAPAR (average 2002-2004)

FAPAR (2003)

Modeled (2003)

1 31 61 91 121 151 181 211 241 271 301 331 361

FAPAR (average 2002-2004)

FAPAR (2004)

Modeled (2004)

ORCHIDEE model V2 version

3.1.2 Evaluation against in situ soil temperature data

y = 1.07 x

R2 = 0.95, RMSE = 1.56

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Modeled S-20cm Te (ºC)

Ob

se

rve

d S

-20

cm

Te

(ºC

)

Spring (Apr. - May)

Summer (Jun. - Aug.)

Autumn (Sep. - Oct.)

Winter (Nov. - Mar.)

Comparisons of modeled and observed average monthly 20cm-depth-soil temperature (S-20cm Te) at 30 stations in Qinghai-Tibetan grasslands for 1980-1990.

3.1.3 Evaluation against satellite LAI observations

y = 0.98 x

R2 = 0.29, n = 39RMSE = 0.66

RM-G = 0.56

RM-F = 0.59

RG-F = 0.900.0

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Modeled LAI

GIM

MS

LA

I

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FA

PA

RGIMMS LAI

FAPAR

Relationship of average July and August LAI derived from ORCHIDEE model V3 with corresponding GIMMS LAI and FAPAR across 39 meteorological stations in Qinghai-Tibetan grasslands. RM-G, RM-F, and RG-F represent correlation coefficient of modeled LAI and GIMMS LAI, modeled LAI and FAPAR, and GIMMS LAI and FAPAR, respectively.

3.1.4 Soil carbon stock validation

y = 0.96 x

R2 = 0.38, n=51RMSE = 4.20

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Modeled SOC (Kg C m-2

)

Ob

serv

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OC

(Kg

C m

- 2)

(b)

y = 0.70 x

R2 = 0.27, n=51RMSE = 6.22

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Modeled SOC (Kg C m-2

)

Ob

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OC

(Kg

C m

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(a)

decreased the turnover time of passive soil carbon from 350 year to 70 years.turnover rate: 0.026 yr-1

Based on the previous estimates of 0.16 Pg C yr-1 in annual NPP [Piao et al., 2005], 0.35 Pg C in biomass stocks [Piao et al., 2007], and 7.4 Pg C in SOC stocks [Yang et al., 2008] of Qinghai-Tibetan grassland, one can estimate that the average turnover rate for the whole Plateau grassland carbon stocks is about 0.021 yr-1

OBSERVED

21

3

03

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02 323

1

00

33

0,1,2,3

1.0

0.99

0.1

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Standard Deviation

Sta

nd

ard

Dev

iati

on

OBSERVEDGPPNEETERLAISOCGIMMS LAIS-TEarc系列1arc2系列12系列13系列14系列15系列16系列17系列18系列19系列20系列21系列22

Second-order statistics of modeled and observed GPP, NEE, TER, and LAI at Haibei site, average SOC density for 51 counties, LAI data at 39 meteorological stations (GIMMS LAI), and 20cm-depth-soil temperature at 30 meteorological stations (S-TE). The radial co-ordinate gives the magnitude of total standard deviation (SD), normalized by the observed value, and the angular co-ordinate gives the correlation of simulations and observations. It follows that the distance between the OBSERVED point and any model's point is proportional to the centered root-mean-square (RMS) error, normalized by the observed SD, too. Numbers indicate the model version

3.2 Evaluation of NPP and carbon stocks of Qinghai-Tibetan grasslands

(g C m-2 yr-1)(g C m-2)

(Kg C m-2)Table Different studies estimated grassland NPP, biomass, and SOC in Qinghai-Tibetan (Q-T) and Tibet (excluding Qinghai) (T)

Area NPP biomass SOC Source Region

(106 km2)

Total

(Pg C yr-1)

Density

(g C m-2 yr-1)

Total

(Pg C)

Density

(g C m-2)

Total

(Pg C)

Density

(Kg C m-2)

Q-T 1.24 0.35 282.3 Piao et al., 2007

Q-T 1.31 0.16 122.1 Piao et al., 2005

Q-T 1.48 0.13 89.8 9.72* 6.57* Zhang et al., 2007

Q-T 1.14 7.40 6.50 Yang et al., 2008

Q-T 1.39 0.32 232.8 0.36 259.1 11.94 8.59 This study

T 0.82 0.19 230.8 Wang et al., 2008

T 0.89 0.20 230.1 This study

1

3.3 Change in NPP and carbon stocks in response to rising temperature by 2 ℃

(g C m-2 yr-1)

(g C m-2)

(Kg C m-2)

NPP: 8.7% increase;Biomass C stocks: not change;Soil C stocks: about 1.2 Pg lost, which is approximately 10% of today’s stock.

Conclusion

The calibrated ORCHIDEE model estimates 0.32 Pg C yr-1 of total annual NPP, 0.36 Pg C of vegetation total biomass (aboveground and belowground), and 11.94 Pg C of SOC stocks for Qinghai-Tibetan grasslands covering an area of 1.4 × 106 km2. The mean annual NPP, vegetation biomass, and soil carbon stocks decrease from the Southeast to the Northwest, alongside with precipitation gradients.

In response to rising temperature by 2°C, approximate 10% of current SOC stocks in Qinghai-Tibetan grasslands could be lost, even though NPP increases by about 9%. This result implies that Qinghai-Tibetan grasslands may be a vulnerable component of the terrestrial carbon cycle to future climate warming.

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