Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach: Model-data fusion in ecology - Peng, Changhui; Guiot, Joel; Wu, Haibin; Jiang, Hong; Luo, Yiqi (Ecology Letters, 2011)

Application of the ecosystem model and Markov Chain Monte Carlo for parameter estimation and productivity prediction - Li, Weizhong; Peng, Changhui; Zhou, Xiaolu; Sun, Jianfeng; Zhu, Qiuan; Wu, Haibin; St-Onge, Benoit (Ecosphere, 2015)
It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process‐based ecological models and data in cohesive, systematic ways. In process‐based model applications, inherent spatial and temporal heterogeneities found within terrestrial ecosystems may lead to the uncertainties of model predictions. To reduce simulation uncertainties due to inaccurate model parameters, the Markov Chain Monte Carlo (MCMC) method was applied in this study to improve the estimations of four key parameters used in the process‐based ecosystem model of TRIPLEX‐FLUX. These four key parameters include a maximum photosynthetic carboxylation rate of 25°C (Vmax), an electron transport (Jmax) light‐saturated rate within the photosynthetic carbon reduction cycle of leaves, a coefficient of stomatal conductance (m), and a reference respiration rate of 10°C (R10). Seven forest flux tower sites located across North America were used to investigate and facilitate understanding of the daily variation in model parameters for three deciduous forests, three evergreen temperate forests, and one evergreen boreal forest. Eddy covariance CO 2 exchange measurements were assimilated to optimize the parameters in the year 2006. After parameter optimization and adjustment took place, net ecosystem production prediction significantly improved (by approximately 25%) compared to the CO 2 flux measurements taken at the seven forest ecosystem sites. Results suggest that greater seasonal variability occurs in broadleaf forests in respect to the selected parameters than in needleleaf forests. This study also demonstrated that the model‐data fusion approach by incorporating MCMC method is able to better estimate parameters and improve simulation accuracy for different ecosystems located across North America.

Simulation of dissolved organic carbon concentrations and fluxes in Chinese monsoon forest ecosystems using a modified TRIPLEX-DOC model - Shi, Shengwei; Yang, Mingxia; Hou, Yue; Peng, Changhui; Wu, Haibin; Zhu, Qiuan; Liang, Qiong; Xie, Junfei; Wang, Meng (Science of The Total Environment, 2019)

Elevation‐induced climate change as a dominant factor causing the late Miocene C4 plant expansion in the Himalayan foreland - Wu, Haibin; Guo, Zhengtang; Guiot, Joël; Hatté, Christine; Peng, Changhui; Yu, Yanyan; Ge, Junyi; Li, Qin; Sun, Aizhi; Zhao, Deai (Global Change Biology, 2014)
Abstract During the late Miocene, a dramatic global expansion of C 4 plant distribution occurred with broad spatial and temporal variations. Although the event is well documented, whether subsequent expansions were caused by a decreased atmospheric CO 2 concentration or climate change is a contentious issue. In this study, we used an improved inverse vegetation modeling approach that accounts for the physiological responses of C 3 and C 4 plants to quantitatively reconstruct the paleoclimate in the Siwalik of Nepal based on pollen and carbon isotope data. We also studied the sensitivity of the C 3 and C 4 plants to changes in the climate and the atmospheric CO 2 concentration. We suggest that the expansion of the C 4 plant distribution during the late Miocene may have been primarily triggered by regional aridification and temperature increases. The expansion was unlikely caused by reduced CO 2 levels alone. Our findings suggest that this abrupt ecological shift mainly resulted from climate changes related to the decreased elevation of the Himalayan foreland.