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Uncertainties in carbon residence time and NPP-driven carbon uptake in terrestrial ecosystems of the conterminous USA: a Bayesian approach Cover

Uncertainties in carbon residence time and NPP-driven carbon uptake in terrestrial ecosystems of the conterminous USA: a Bayesian approach

By: ,   and    
Open Access
|Jan 2012

Figures & Tables

Fig. 1. 

Schematic diagram of the regional terrestrial ecosystem (TECO-R) model for inversion analysis of carbon residence time. NPP, which is modelled by maximum light-use efficiency (ɛ*) (CASA model), is allocated to plant tissues (leaf q L , stem q W and root ) based on allocation coefficients (α L , α W , α R ). Plant tissues enter into fine litter (q F ), coarse litter (q C ) and soil organic carbon pools (q S ) through litterfall. Decomposed litter releases part of the carbon to the atmosphere, and the rest transfers into the soil (θ C , θ F ). Through mechanical breakdown, part of the coarse litter becomes fine litter (η). To reflect the differences in soil profile, roots and soil organic carbon pools are divided into three layers (0–20, 20–50 and 50–100 cm). Each pool has an associated carbon residence time, τ k (k=1, 2, 3).

Table 1. Symbol and definition of parameters, their lower (LL) and upper limits (UL) and other constraints for inverse analysis. Unit is gC MJPAR −1 for ɛ * and years for carbon residence times. Allocation and partitioning coefficients are dimensionless.

Symbol Definition Unit LL UL Other constraint ɛ * Maximum light-use efficiency g C MJPAR−1 0.0 2.76 α L Allocation of NPP to leaves Dimensionless 0.0 1.0 α L >α W , not for grassland and cropland α W Allocation of NPP to wood Dimensionless 0.0 1.0 α W =0 for grassland and cropland α R Allocation of NPP to roots Dimensionless 0.0 1.0 α L +α W +α R =1 Allocation proportion of NPP for roots (0–20 cm) Dimensionless 0.0 1.0 >> Allocation proportion of NPP for roots (20–50 cm) Dimensionless 0.0 1.0 Allocation proportion of NPP for roots (50–100 cm) Dimensionless 0.0 1.0 ++=1 θ F Carbon partitioning coefficient of the fine litter pool Dimensionless 0.0 0.5 θ C Carbon partitioning coefficient of coarse litter pool Dimensionless 0.0 0.1 θ C =0 for grassland and cropland Carbon partitioning coefficient of SOC (0–20 cm) Dimensionless 0.0 0.1 Carbon partitioning coefficient of SOC (20–50 cm) Dimensionless 0.0 0.1 η Fraction of mechanical breakdown for coarse litter pool Dimensionless 0.0 0.1 τ L Site specific carbon residence time of leaves Year 0.0 5.0 0≤τ L ≤1 for deciduous broadleaf forest, grasslands and cropland τ W Site specific carbon residence time of wood Year 0.0 200.0 τ W >τ L , not for grassland and cropland Site specific carbon residence time of roots (0–20 cm) Year 0.0 10.0 <<, ≤5 for grassland and cropland Site specific carbon residence time of roots (20–50 cm) Year 0.0 20.0 τ R 2 ≤5 for grassland and cropland Site specific carbon residence time of roots (50–100 cm) Year 0.0 50.0 ≤10 for grassland and cropland Moisture and temperature corrected residence time of fine litter Year 0.0 2.0 Moisture and temperature corrected residence time of coarse litter Year 0.0 50.0 >, not for grassland and cropland Moisture and temperature corrected residence time of SOC (0–20 cm) Year 0.0 50.0 << Moisture and temperature corrected residence time of SOC (20–50 cm) Year 0.0 50.0 Moisture and temperature corrected residence time of SOC (50–100 cm) Year 0.0 100.0
Fig. 2. 

Comparisons between modelled and observed data lumped by each biome for 13 data sets. Biomes: ENF, evergreen needleleaf forest; DBF, deciduous broadleaf forest; MF, mixed forest; W, woodland; WG, wooded grassland; S, shrubland; G, grassland; and C, cropland. Unit with dimensions is not shown.

Fig. 3. 

Inversion results showing the histograms of 22 estimated parameters and cost function with 40 000 samples from M–H simulation for evergreen needleleaf forest (ENF). See Table 1 for parameter abbreviation. Unit with dimensions is not shown.

Fig. 4. 

Inversion results showing the histograms of 17 estimated parameters and cost function with 40 000 samples from M–H simulation for Grassland. See Table 1 for parameter abbreviation. Unit with dimensions is not shown.

Fig. 5. 

MLEs or means of estimated parameters for eight biomes. Error bars represent standard deviations (SDs) of parameters calculated from 40 000 samples of M–H simulation. Parameters θS1 and θS2 were not shown due to their consistence between biomes (0.05±0.03). See Fig. 2 for biome abbreviations and Table 1 for parameter abbreviations.

Fig. 6. 

Spatial patterns of ecosystem carbon residence times (a), its standard deviation (SD, b) and coefficient of variation (CV=SD/mean, c) in terrestrial ecosystems of the conterminous USA.

Fig. 7. 

Carbon allocation coefficients (a) and the averaged ecosystem C residence time (b) for eight biomes. See Fig. 2 for biomes abbreviation. Horizontal dash line represents the average ecosystem C residence time in the conterminous USA. Error bars represent standard deviation.

Fig. 8. 

The potential of ecosystems C uptakes (a), their coefficient of variance (b) and proportion of soil to ecosystem C uptake in the conterminous USA under the actual NPP increases from Hicke et al. (2002). The positive value means C sink while the negative value mean C source.

Fig. 9. 

Yearly ecosystem and soil C uptake in different biomes under the actual NPP increases from Hicke et al. (2002). Error bars represent standard deviation (SD).

Language: English
Page range: 17223 - 17223
Submitted on: Jan 17, 2012
Accepted on: Aug 20, 2012
Published on: Jan 1, 2012
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2012 Xuhui Zhou, Tao Zhou, Yiqi Luo, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.