Gross Primary Production of Terrestrial Vegetation

Xiao, Xiangming; Cui, Jin; Dong, Jinwei

doi:10.1007/978-3-642-25047-7_5

Cited by 7 publications

(2 citation statements)

References 97 publications

(137 reference statements)

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“…GPP is estimated by using a light use efficiency model, where GPP is the total gross C assimilated by the system [ Heinsch et al , ; Kalfas et al , ; Xiao et al , , ]. This model is ideal in systems where soil water deficit does not influence photosynthetic rates, such as in wetlands where absorbed light explains >80% of the variance in partitioned GPP (Figure S5).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands

et al. 2017

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Wetlands and flooded peatlands can sequester large amounts of carbon (C) and have high greenhouse gas mitigation potential. There is growing interest in financing wetland restoration using C markets; however, this requires careful accounting of both CO2 and CH4 exchange at the ecosystem scale. Here we present a new model, the PEPRMT model (Peatland Ecosystem Photosynthesis Respiration and Methane Transport), which consists of a hierarchy of biogeochemical models designed to estimate CO2 and CH4 exchange in restored managed wetlands. Empirical models using temperature and/or photosynthesis to predict respiration and CH4 production were contrasted with a more process‐based model that simulated substrate‐limited respiration and CH4 production using multiple carbon pools. Models were parameterized by using a model‐data fusion approach with multiple years of eddy covariance data collected in a recently restored wetland and a mature restored wetland. A third recently restored wetland site was used for model validation. During model validation, the process‐based model explained 70% of the variance in net ecosystem exchange of CO2 (NEE) and 50% of the variance in CH4 exchange. Not accounting for high respiration following restoration led to empirical models overestimating annual NEE by 33–51%. By employing a model‐data fusion approach we provide rigorous estimates of uncertainty in model predictions, accounting for uncertainty in data, model parameters, and model structure. The PEPRMT model is a valuable tool for understanding carbon cycling in restored wetlands and for application in carbon market‐funded wetland restoration, thereby advancing opportunity to counteract the vast degradation of wetlands and flooded peatlands.

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