Plant phenological modeling and its application in global climate change research: overview and future challenges - Zhao, Meifang; Peng, Changhui; Xiang, Wenhua; Deng, Xiangwen; Tian, Dalun; Zhou, Xiaolu; Yu, Guirui; He, Honglin; Zhao, Zhonghui (Environmental Reviews, 2013)
Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.

Applying an artificial neural network to simulate and predict Chinese fir (Cunninghamia lanceolata) plantation carbon flux in subtropical China - Wen, Xuding; Zhao, Zhonghui; Deng, Xiangwen; Xiang, Wenhua; Tian, Dalun; Yan, Wende; Zhou, Xiaolu; Peng, Changhui (Ecological Modelling, 2014)

rTRIPLEXCWFlux: An R package for carbon–water coupling model to simulate net ecosystem productivity and evapotranspiration in forests - Sun, Shulan; Ouyang, Shuai; Hu, Yanting; Zhao, Zhonghui; Liu, Meixian; Chen, Liang; Zeng, Yelin; Peng, Changhui; Zhou, Xiaolu; Xiang, Wenhua (Environmental Modelling & Software, 2023)

Plant functional trait diversity and structural diversity co-underpin ecosystem multifunctionality in subtropical forests - Ouyang, Shuai; Gou, Mengmeng; Lei, Pifeng; Liu, Yue; Chen, Liang; Deng, Xiangwen; Zhao, Zhonghui; Zeng, Yelin; Hu, Yanting; Peng, Changhui; Xiang, Wenhua (Forest Ecosystems, 2023)

Stand carbon storage and net primary production in China’s subtropical secondary forests are predicted to increase by 2060 - Jin, Jia; Xiang, Wenhua; Zeng, Yelin; Ouyang, Shuai; Zhou, Xiaolu; Hu, Yanting; Zhao, Zhonghui; Chen, Liang; Lei, Pifeng; Deng, Xiangwen; Wang, Hui; Liu, Shirong; et al. (Carbon Balance and Management, 2022)
Abstract Background Forest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5). Results The runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060. Conclusions The obtained results lead to conclusion that proper management of China’s subtropical secondary forests could be considered as one of the steps towards achieving China’s target to become carbon neutral by 2060.