The role of carbon flux and biometric observations in constraining a terrestrial ecosystem model: a case study in disturbed forests in East Asia

Abstract

The process of confining unnecessary freedom is a step toward advanced ecosystem modeling. This study demonstrates the importance of carbon flux and biometric observation in constraining a terrestrial ecosystem model with a simple optimization scheme. At the selected sites from AsiaFlux network, a simultaneous optimization scheme for both carbon flux and biomass was compared with carbon flux‐oriented and biomass‐oriented optimization schemes using the Biome‐BGC model. The optimization scheme oriented to either carbon flux or biomass provided simulation results that were consistent with observations, but with reduced performance in unconstrained variables. The simultaneous optimization scheme yielded results that were consistent with observations for both carbon flux and biomass. By comparing long‐term projections simulated by three schemes, it was found that the optimization oriented only to carbon flux has limited performance because misrepresented biomass significantly affected a projection of carbon exchange through heterotrophic respiration. From these experiments, we found that (1) a process model like Biome‐BGC is capable of reproducing both carbon flux and biomass within acceptable proximity, (2) constraining biomass is importance not just because it is one of carbon cycle components, but also it significantly affects simulations of carbon flux. Thus, it is important to invest more effort to improve simulation of biomass as well as carbon flux.

Modeling forest carbon cycle using long‐term carbon stock field measurement in the Delaware River Basin

“…, Kondo et al. ). Moreover, field measurement of biomass is feasible in all the forested area, whereas eddy covariance towers are far scarcer and cannot be installed in highly complex topographies.…”

“…In this study, forest biomass C stock measurements were used as the key benchmark in the model verification processes. Compared to C fluxes variables, which are frequently used in model calibrations based on eddy covariance data, forest biomass better represents the spatial variation of forest C cycling and synthesizes more information about the long-term processes of forest dynamics since it is an accumulative variable (Miehle et al 2006, Kondo et al 2013. Moreover, field measurement of biomass is feasible in all the forested area, whereas eddy covariance towers are far scarcer and cannot be installed in highly complex topographies.…”

“…Several studies have indicated that flux and biometric observations can play complementary roles in constraining model parameters (Kondo et al. , Du et al. ).…”

“…Forest Inventory Analysis (FIA) and remote sensing techniques have provided useful information on the spatial distribution of forest C stock changes (Woodbury et al 2007, Lichstein et al 2014. Several studies have indicated that flux and biometric observations can play complementary roles in constraining model parameters (Kondo et al 2013, Du et al 2015. The performance of a process-based simulation model could be improved if it can be parameterized using better data sources from long-term biometric C stock data from multiple sites representative of the simulated region (Carvalhais et al 2010, Molina-Herrera et al 2015.…”

Evaluation of modeled global vegetation carbon dynamics: Analysis based on global carbon flux and above-ground biomass data

“…Recent research has shown that it is necessary to use both carbon flux data and biometric data for DGVM calibration (Kondo et al, 2013;Seiler et al, 2014). Furthermore, uncertainties in DGVMderived carbon flux and biomass may also arise from the input data itself, such as meteorological forcing data (Barman et al, 2014a,b).…”