Vegetation modelling has been viewed as a major approach for examining the dynamics of vegetation under climatic change. However, the characterization of uncertainty of model results is still a key issue. In order to improve future model-based research, it is important to synthesize the current approaches and the issues that arise in vegetation modelling and to propose potential strategies for improving model-based research. This study first reviews the progress of vegetation models from static-equilibrium to transient-dynamic and to current coupled multi-objective vegetation models. Then, the four main sources leading to the uncertainty of model results are described, including (1) factors related to vegetation models (their structure, assumption and parameterization), (2) the data used to run a model, (3) the approaches used to validate model results, and (4) the spatiotemporal scaling issues involved in vegetation modelling. Finally, four strategies are proposed for improving future model-based research. These include improvements in the model structure and parameterization, enhancement of the quality of analytical data, improvement of the analytical approaches, and continued development of coupled dynamic vegetation models. Using a literature synthesis, this study provides researchers with a general guidance on applying vegetation models for simulating the effects of climatic variations on terrestrial vegetation.
