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Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model Cover

Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model

Open Access
|Jan 2015

Figures & Tables

Fig. 1

Schematic diagram of the terrestrial ecosystem (TECO) model with canopy photosynthesis and vegetation–soil C transfer models for data assimilation. LAI, leaf area index; Ta, air temperature; PAR, photosynthetically active radiation; RH, relative humidity. The canopy photosynthesis model is used to describe gross primary production (GPP) calculated by leaf area index (LAI) and the carbon influx of the top leaf layer (A n). The latter was calculated by Leuning model (Leuning, 1995) using gross leaf CO2 uptake (A) and stomatal conductance (G s). For C3 plants, A is calculated by the rates of carboxylation enzymes (J c) and light electron transport rates (J e) according to a model developed by Farquhar et al. (1980).

Table 1. Symbols and description of parameters, their intervals (lower and upper limits) and units in this data assimilation

ParametersIntervalsUnitDescriptionC 1 0.176–2.95mg C g−1 d−1From pool ‘foliage biomass’ (X1) to pools ‘metabolic litter’ (X3) and ‘structure litter’ (X4)C 2 0.0548–0.274mg C g−1 d−1From pool ‘woody biomass’ (X2) to pool ‘structure litter’ (X4)C 3 0.548–2.74mg C g−1 d−1From pool 'metabolic litter’ (X3) to ‘microbes’ (X5)C 4 0.274–1.37mg C g−1 d−1From pool ‘structure litter’ (X4) to pools ‘microbes’ (X5) and ‘slow SOM’ (X6)C 5 2.74–6.85mg C g−1 d−1From pool ‘microbes’ (X5) to pools ‘slow SOM’ (X6) and ‘passive SOM’ (X7)C 6 0.0274–0.137mg C g−1 d−1From pool ‘slow SOM’ (X6) to pools ‘microbes’ (X5) and ‘passive SOM’ (X7)C 7 0.00137–0.00913mg C g−1 d−1From pool ‘passive SOM’ (X7) to pool ‘microbes’ (X5)a q 0.3–0.5mol mol−1photoCanopy quantum efficiency of photon conversionK25c50–600umol mol−1Michaelis–Menten constant for carboxylationEKc 20 000–100 000J mol−1Activation energy of K25cEK0 10 000–60 000J mol−1Activation energy of K250K2500.2–0.5mol mol−1Michaelis–Menten constant for oxygenationEVm 5000–50 000J mol−1Activation energy of V25 m 10–80umol mol−1CO2 compensation point without dark respirationrJmVm1–5dimensionlessRation of J m to V25 m at 25°CR0eco1–5umol CO2m−2s−1Whole ecosystem respiration at 0°CQ 10 1–3dimensionlessTemperature dependency of ecosystem respirationV25 m 1–20umol CO2m−2s−1Maximum carboxylation rate at 25°CfCi 0.5–0.9dimensionlessRation of internal CO2 to air CO2K n 0.7–0.9dimensionlessCanopy extinction coefficient for lightEΓ*2530 000–100 000J mol−1Activation energy of CO2 compensation point at 25°Cgl1000–2000dimensionlessEmpirical coefficient in Leuning modelD00.5–6kPaEmpirical coefficient in Leuning model
Fig. 2

Posterior distributions of 23 parameters using net ecosystem exchange (NEE) data (Experiment 1), biometric data (Experiment 2) and both NEE and biometric data (Experiment 3) for parameter constrains. The normal distribution curves (red line) represent that the parameters are well constrained by the datasets. See Table 1 for parameter abbreviations and units.

Fig. 3

Maximum likelihood estimators (MLEs) (or means for unconstrained parameters) for 23 parameters in three experiments (11 parameters are shown in panel a and the rest 12 parameters are shown in panel b). Error bars represent standard deviations (SDs) of parameters calculated from 50 000 samples of Metropolis–Hastings (M–H) simulation. The letters a, b and c above the bars indicate statistical significance (α=0.05). See Table 1 for parameter abbreviations and units.

Fig. 4

The frequency distributions of correlation coefficient between every possible parameter pair in three experiments (a) NEE data, (b) biometric data and (c) NEE and biometric data combined.

Fig. 5

Comparisons between simulated and observed woody biomass (a, g and m), foliage biomass (b, h and n), litterfall (c, i and o), soil respiration (d, j and p), mineral carbon (e, k and q), and forest floor C (f, l and r) from three experiments (NEE data, biometric data, and NEE and biometric data).

Fig. 6

Comparison of simulated and observed NEE data (from 2003 to 2010) based on three experiments (NEE data, biometric data, and NEE and biometric data combined). The simulated and observed NEE data derived from NEE data, biometric data, combined NEE and biometric data are listed in panels a, b and c; the relationships between simulated and observed NEE data are shown in panels d, e and f, respectively. SSE is sum of squares error.

Fig. 7

Predicted woody biomass (a), foliage biomass (b), metabolic litter (c), structural litter (d), microbes (e), slow SOM (f), and passive SOM (g) from 2010 to 2023 using the parameter values of MLEs (well-constrained parameters) and means (poorly constrained parameters) from Experiment 1, 2 and 3.

Fig. 8

C pools (i.e. woody biomass, foliage biomass, litterfall, mineral carbon and forest floor carbon) and soil respiration simulated by using 100 random parameters sampled from all 5000 parameters are listed in panels a, b, c, d, e and f. The red solid circles represent observed data and other symbols (line with point, 100 in all) represent simulated data in each panel.

Language: English
Page range: 24102 - 24102
Submitted on: Feb 17, 2014
Accepted on: Jan 29, 2015
Published on: Jan 1, 2015
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2015 Zhenggang Du, Yuanyuan Nie, Yanghui He, Guirui Yu, Huimin Wang, Xuhui Zhou, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.