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Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems

Artículos

Revista/Conferencia:

JOURNAL OF ENVIRONMENTAL MANAGEMENT

Language:

English

Autor:

Mao Fangjie; Li Pingheng; Xu Xiaojun; Shi Yongjun; Zhou Yufeng; Tu Guoqing

Experts:

Zhou Guomo; Du Huaqiang

Año:

2016

Volumen:

172

Número de páginas:

29-39

Palabras claves:

Ecosystem model; BIOME-BGC; Moso bamboo forest; Ecosystem management; Carbon cycle

Numerical models are the most appropriate instrument for the analysis of the carbon balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based model BIOME-BGC is widely used in simulation of carbon balance within vegetation, litter and soil of unmanaged ecosystems. For Moso bamboo forests, however, simulations with BIOME-BGC are inaccurate in terms of the growing season and the carbon allocation, due to the oversimplified representation of phenology. Our aim was to improve the applicability of BIOME-BGC for managed Moso bamboo forest ecosystem by implementing several new modules, including phenology, carbon allocation, and management. Instead of the simple phenology and carbon allocation representations in the original version, a periodic Moso bamboo phenology and carbon allocation module was implemented, which can handle the processes of Moso bamboo shooting and high growth during «on-year» and «off-year». Four management modules (digging bamboo shoots, selective cutting, obtruncation, fertilization) were integrated in order to quantify the functioning of managed ecosystems. The improved model was calibrated and validated using eddy covariance measurement data collected at a managed Moso bamboo forest site (Anji) during 2011-2013 years. As a result of these developments and calibrations, the performance of the model was substantially improved. Regarding the measured and modeled fluxes (gross primary production, total ecosystem respiration, net ecosystem exchange), relative errors were decreased by 42.23%, 103.02% and 18.67%, respectively. (C) 2015 Elsevier Ltd. All rights reserved.