Vegetation-specific model parameters are not required for estimating gross primary production

Wenping Yuan, Wenwen Cai, Shuguang Liu, Wenjie Dong, Jiquan Chen, M. Altaf Arain, Peter D. Blanken, Alessandro Cescatti, Georg Wohlfahrt, Teodoro Georgiadis, Lorenzo Genesio, Damiano Gianelle, Achim Grelle, Gerard Kiely, Alexander Knohl, Dan Liu, Michal V. Marek, Lutz Merbold, Leonardo Montagnani, Oleg Panferov, Mikko Peltoniemi, Serge Rambal, Antonio Raschi, Andrej Varlagin and Jiangzhou Xia

Ecological Modelling, 2014, vol. 292, issue C, 1-10

Abstract: Models of gross primary production (GPP) based on remote sensing measurements are currently parameterized with vegetation-specific parameter sets and therefore require accurate information on the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a vegetation-invariant set of parameters that can maintain or increase model applicability by reducing errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem carbon fluxes from 168 globally distributed sites in a range of vegetation types, we examined the predictive capacity of seven light use efficiency (LUE) models. Two model experiments were conducted: (i) a constant set of parameters for various vegetation types and (ii) vegetation-specific parameters. The results showed no significant differences in model performance in simulating GPP while using both set of parameters. These results indicate that a universal of set of parameters, which is independent of vegetation cover type and characteristics can be adopted in prevalent LUE models. Availability of this well tested and universal set of parameters would help to improve the accuracy and applicability of LUE models in various biomes and geographic regions.

Keywords: Light use efficiency; Gross primary production; Model parameters (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:292:y:2014:i:c:p:1-10

DOI: 10.1016/j.ecolmodel.2014.08.017

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